Development and validation of a frailty assessment-based competing risk model for predicting cancer-specific survival in elderly patients with gastric cancer: a dual-center cohort study
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Abstract Frailty serves as a critical determinant of clinical outcomes in elderly cancer patients. Nevertheless, there remains a paucity of rigorously validated prognostic tools that account for frailty heterogeneity in older adults with gastric cancer (GC). This multicenter investigation enrolled 1,278 consecutive GC patients aged ≥ 70 years treated between January 2015 and December 2022 across two geographically distinct Chinese cohorts: the Xi'an Cohort (n = 934) and Yulin Cohort (n = 344). Using stratified randomization, the Xi'an cohort was partitioned into training (n = 653) and internal validation (n = 281) sets at a 7:3 ratio, while the Yulin cohort provided external validation. Frailty status was systematically assessed using the validated 5-Item Modified Frailty Index. Competing risks regression analysis identified significant predictors of cancer-specific survival (CSS) in the training cohort. Based on the risk factors, predictive models to predict patients’ 1-, 3-, and 5-year CSS were constructed. Model performance was rigorously evaluated through multiple metrics: the area under the receiver operating curve (AUC), Harrell's concordance index (C-index), calibration plots, and decision curve analysis (DCA). Multivariable analysis revealed severe frailty as an independent CSS predictor (subdistribution hazard ratio = 2.84, 95% CI: 2.04–3.96; P < 0.001). The frailty-incorporated competing risk model demonstrated superior predictive performance, achieving AUC values of 0.838 (training), 0.846 (internal validation), and 0.841 (external validation) - surpassing conventional prognostic models. Concordantly, the model attained the highest C-indices across all validation sets (training: 0.771; internal: 0.784; external: 0.784). Calibration plots showed excellent agreement between predicted and observed outcomes, while DCA confirmed enhanced clinical utility across relevant risk thresholds. These findings establish that frailty-adapted prognostic modeling significantly improves survival prediction accuracy in elderly GC patients. Our results underscore the imperative of comprehensive frailty assessment in clinical decision-making and risk stratification for this vulnerable population.
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Development and validation of a frailty assessment-based competing risk model for predicting cancer-specific survival in elderly patients with gastric cancer: a dual-center cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Development and validation of a frailty assessment-based competing risk model for predicting cancer-specific survival in elderly patients with gastric cancer: a dual-center cohort study Zhigang Wu, Huarui Xue, Xiaoxiao Ma, Zhenzhen Fang, Lin Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6618864/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Frailty serves as a critical determinant of clinical outcomes in elderly cancer patients. Nevertheless, there remains a paucity of rigorously validated prognostic tools that account for frailty heterogeneity in older adults with gastric cancer (GC). This multicenter investigation enrolled 1,278 consecutive GC patients aged ≥ 70 years treated between January 2015 and December 2022 across two geographically distinct Chinese cohorts: the Xi'an Cohort (n = 934) and Yulin Cohort (n = 344). Using stratified randomization, the Xi'an cohort was partitioned into training (n = 653) and internal validation (n = 281) sets at a 7:3 ratio, while the Yulin cohort provided external validation. Frailty status was systematically assessed using the validated 5-Item Modified Frailty Index. Competing risks regression analysis identified significant predictors of cancer-specific survival (CSS) in the training cohort. Based on the risk factors, predictive models to predict patients’ 1-, 3-, and 5-year CSS were constructed. Model performance was rigorously evaluated through multiple metrics: the area under the receiver operating curve (AUC), Harrell's concordance index (C-index), calibration plots, and decision curve analysis (DCA). Multivariable analysis revealed severe frailty as an independent CSS predictor (subdistribution hazard ratio = 2.84, 95% CI: 2.04–3.96; P < 0.001). The frailty-incorporated competing risk model demonstrated superior predictive performance, achieving AUC values of 0.838 (training), 0.846 (internal validation), and 0.841 (external validation) - surpassing conventional prognostic models. Concordantly, the model attained the highest C-indices across all validation sets (training: 0.771; internal: 0.784; external: 0.784). Calibration plots showed excellent agreement between predicted and observed outcomes, while DCA confirmed enhanced clinical utility across relevant risk thresholds. These findings establish that frailty-adapted prognostic modeling significantly improves survival prediction accuracy in elderly GC patients. Our results underscore the imperative of comprehensive frailty assessment in clinical decision-making and risk stratification for this vulnerable population. Biological sciences/Cancer/Gastrointestinal cancer/Gastric cancer Biological sciences/Cancer/Cancer screening Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction With the intensification of global population aging, healthcare planning and delivery for elderly patients are facing unprecedented challenges. This demographic shift holds particular significance in gastric cancer (GC) management, a disease characterized by both high prevalence and substantial mortality in older populations. According to the 2022 global cancer statistics, 968,350 new GC cases were identified, accounting for 4.9% of all malignancies 1 . Notably, the elderly population (defined as age ≥ 70 years) has emerged as a progressively expanding patient subset in GC epidemiology, with population-based data demonstrating a sustained increase in the mean age at diagnosis during recent decades 2, 3 . The high prevalence of age-related comorbidities renders treatment and follow-up strategies for elderly GC patients particularly complex and multifaceted. Nevertheless, current clinical practice guidelines remain based primarily on clinical trials that selectively exclude elderly patients through age cutoffs or functional status criteria to minimize confounding 4, 5 , resulting in insufficient evidence-based guidance for geriatric GC management.. Frailty, as a multidimensional geriatric syndrome, represents a critical concept in clinical evaluation of elderly patients. While operational definitions of frailty remain heterogeneous, current consensus characterizes it as a state of increased vulnerability to stressors and diminished physiologic reserve 6 . Emerging evidence has established associations between frailty status and poor clinical outcomes across malignancies, including lung cancer 7 , liver cancer 8 , prostate cancer 9 , etc. The International Society for Geriatric Oncology 10 and the ASCO Guideline for Geriatric Oncology 11 advocate routine geriatric assessments for cancer patients aged ≥70 years to inform risk stratification and treatment planning. Given the prognostic significance of frailty in GC, developing a comprehensive prognostic model incorporating frailty parameters could enhance patient-specific outcome prediction. To our knowledge, specific predictive models for cancer-specific mortality in geriatric GC patients are currently lacking. Therefore, this study aimed to evaluate frailty-associated prognostic impacts and construct and validate frailty-integrated survival prediction models using multi-center data from two tertiary referral hospitals. Such personalized prediction tools may facilitate individualized clinical decision-making and optimize follow-up protocols in this vulnerable population. Methods Study design and patients This retrospective multicenter cohort study analyzed clinicopathological characteristics and prognostic data collected from prospectively maintained databases at two tertiary hospitals between January 2015 and December 2022. Patients meeting the following inclusion criteria were eligible: (1) histopathologically confirmed gastric adenocarcinoma, (2) diagnosis age ≥ 70 years, and (3) receipt of curative-intent surgical resection. Exclusion criteria comprised: (1) presence of distant metastasis, (2) history of other malignancies, (3) surgery for local recurrence, (4) emergency procedures, or (5) missing clinicopathological or follow-up records. From 1,278 consecutive patients screened, the final cohort comprised 934 subjects from the First Affiliated Hospital of Xi'an Jiaotong University (Xi'an cohort) and 344 from the Yulin Hospital of the First Affiliated Hospital of Xi 'an Jiaotong University (Yulin cohort). Using computerized randomization, the Xi'an cohort was stratified into a training set (n = 653) and an internal validation set (n = 281) at a 7:3 ratio for model development and performance assessment, respectively. The Yulin cohort served as an external validation cohort to evaluate model generalizability. Figure 1 illustrates the patient selection process. This study complied with the Declaration of Helsinki and received approval from the Institutional Review Board of the First Affiliated Hospital of Xi'an Jiaotong University (No. XJTU1AF2020LSL-004). Written informed consent was obtained from all participants. Assessment of frailty status Patients completed clinical geriatric assessment within 3 days of admission. Frailty status was assessed using the validated 5-item modified frailty index (5-mFI), which demonstrated robust reliability and construct validity in prior studies 12 , 13 . This instrument evaluates five clinical domains: (1) history of severe chronic obstructive pulmonary disease (COPD), (2) congestive heart failure occurring in the month before surgery, (3) ability to perform daily activities (independent or independent), (4) hypertension that necessitates medication, and (5) diabetes mellitus that necessitates oral agents or insulin. Each domain was dichotomously scored (0 = absent;1 = present), yielding cumulative scores ranging 0–5. The 5-mFI was categorized as 0, 1, 2, or ≥ 3, which was corresponded to fit, pre-frailty, moderate frailty, and severe frailty. Data collection and patient followup Demographic and clinicopathological variables were systematically extracted from our institution's prospectively maintained GC database. Comprehensive data fields encompassed: preoperative characteristics, surgical pathology reports, postoperative complications, postoperative recovery, and follow-up data. After surgery, all patients were managed used standardized follow-up protocols. Postoperative follow-up was performed every 3 months for the first two years and then every 6 months after. The last follow-up date in this study was 31 December 2024. The overall survival (OS) was calculated from operation time to death or follow-up dead-line. Cancer-specific survival (CSS) was defined as the time from surgery to death due to GC. Statistical analysis Statistical analyses were conducted using R Statistical Software (v4.3.2). Categorical variables were compared using the Fisher’s exact test. Continuous variables were compared using one-way ANOVA or Kruskal-Wallis H test based on whether satisfying normal distribution or not. Fine and Gray competing risk models was used to analyze associations between risk factors and gastric cancer-specific mortality. GC mortality was considered the event of interest, while mortality from other specific causes was treated as a competing event. Based on the identified risk factors, predictive models for CSS were established. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, the concordance index (C-index) and decision curve analysis (DCA) were conducted to visually evaluate the performance of predictive nomograms. X-tile software was used to identify the optimal cutoff value for the selected nomogram 14 . Log-rank test was used to analyze the survival difference between the low-risk and high-risk groups. All statistical tests were two-sided at an α level of 0.05. Results Patient baseline characteristics From January 2015 to December 2022, A total of 1278 older patients with GC were included in this study, including 653 in the training set, 281 in the internal validation set, and 344 in the external validation set. Baseline clinicopathological characteristics across cohorts are detailed in Table 1 . While the training and internal validation sets demonstrated comparable profiles (all P > 0.05), significant inter-cohort variations emerged between training and external validation sets regarding: age ( P = 0.019), type for digestive tract reconstruction ( P < 0.001), open abdominal surgeries ( P < 0.001), proportion of preoperative chemotherapy ( P = 0.042), and proportion of adjuvant chemotherapy ( P = 0.003). No statistically significant differences were observed in sex, body mass index, American Society of Anesthesiologists physical status (ASA-PS), history of abdominal surgery, tumor location, operative procedure, lymph node dissection, tumor size, pathological T stage, pathological N stage, pathological TNM stage, histological type, lymphovascular invasion, and perineural invasion (all P > 0.05). Table 1 Baseline characteristics of elderly gastric cancer patients in the training, internal validation and external validation cohorts. Variable Xi’an cohort Yulin cohort Training set (n = 653) Internal validation set (n = 281) p -value a External validation Set (n = 344) p -value b Age, mean(SD), years 74.1 ± 3.1 74.5 ± 2.9 0.053 75.0 ± 3.8 <0.001 Sex, n (%) 0.724 0.649 Male 394 (60.3) 173 (61.6) 204 (59.3) Female 259 (39.7) 108 (38.4) 140 (40.7) ASA-PS, n (%) 0.461 0.119 I、II 404 (61.9) 181 (64.4) 199 (57.8) III、IV 249 (38.1) 100 (35.6) 145 (42.2) BMI, mean(SD), kg/m 2 22.4 ± 3.2 22.1 ± 3.6 0.203 22.5 ± 3.4 0.279 Previous abdominal surgery, n (%) 87 (13.3) 39 (17.4) 0.102 33 (9.6) 0.061 Tumor location, n (%) 0.839 0.196 Upper third 217 (33.2) 99 (35.2) 131 (38.1) Middle third 175 (26.8) 73 (26.0) 95 (27.6) Lower third 261 (40.0) 109 (38.8) 118 (34.3) Operative procedure, n(%) 0.585 0.082 Distal gastrectomy 351 (53.8) 161 (57.3) 199 (57.8) Total gastrectomy 243 (37.2) 98 (34.9) 128 (37.2) Subtotal gastrectomy 59 (9.0) 22 (7.8) 17 (4.9) Lymph node dissection, n(%) 0.300 0.074 D1+ 464 (71.1) 209 (74.4) 265 (77.0) D2 189 (28.9) 72 (25.6) 79 (23.0) Type of reconstruction, n(%) 0.414 <0.001 Billroth I 116 (17.8) 40 (14.2) 36 (10.5) Billroth II 135 (20.7) 61 (21.7) 44 (12.8) Roux-en-Y 402 (61.6) 180 (64.1) 264 (76.7) Open surgery, n (%) 67 (10.3) 35 (12.5) 0.324 68 (19.8) <0.001 Tumor size, mean(SD), cm 4.3 ± 1.2 4.7 ± 1.3 0.380 4.9 ± 1.4 0.143 Pathological T stage, n (%) 0.414 0.770 T1 183 (28.0) 89 (31.7) 93 (27.0) T2 132 (20.2) 45 (16.0) 62 (18.3) T3 227 (34.8) 99 (35.2) 122 (35.5) T4 111 (17.0) 48 (17.1) 66 (19.2) Pathological N stage, n (%) 0.376 0.896 N0 359 (55.0) 169 (60.1) 196 (57.0) N1 122 (18.7) 48 (17.1) 57 (16.6) N2 79 (12.1) 34 (12.1) 42 (12.2) N3 93 (14.2) 30 (10.7) 49 (14.2) Pathological TNM stage, n (%) 0.625 0.906 I 241 (36.9) 113 (40.2) 127 (36.9) II 192 (29.4) 77 (27.4) 98 (28.5) III 220 (33.7) 91 (32.4) 119 (34.6) Histological type, n (%) 0.459 0.493 Well/moderate 310 (47.5) 126 (44.8) 168 (48.8) Poor/undifferentiated 343 (52.5) 155 (55.2) 176 (51.2) Lymphovascular invasion, n (%) 339 (51.9) 157 (55.9) 0.266 166 (48.6) 0.124 Perineural invasion, n (%) 166 (25.4) 69 (24.6) 0.780 99 (28.8) 0.192 Preoperative chemotherapy, n (%) 16 (2.4) 10 (3.6) 0.345 3 (0.9) 0.042 Adjuvant chemotherapy, n (%) 121 (18.5) 49 (17.4) 0.692 39 (11.3) 0.003 ASA-PS: American Society of Anesthesiology Physical Status Classification; a training cohort vs. internal validation group b derivation set vs. external validation group Figure 2 illustrates the prognostic differences among the training set, the internal validation set and external validation set. The 5-year OS rates in the training, internal validation, and external validation cohorts were 46.9%, 52.0%, and 49.0%, respectively ( P = 0.251). The 5-year CSS rates were not significantly different among the three cohorts (57.8% vs. 63.0% vs. 61.1%, P = 0.124). There was no significant difference in the 5-year incidence of competing events among the three groups (18.4% vs. 17.3% vs. 21.2%, P = 0.942). Association between different frailty status and clinicopathological features of elderly GC patients In the derivation cohort, 207 patients (31.7%) were allocated to fit (5-mFI = 0), 176 patients (27.0%) as pre-frailty (5-mFI = 1), 151 patients (23.1%) as moderate frailty (mFI = 2), and 119 patients (18.2%) as severe frailty (mFI ≥ 3). The association between the clinicopathological characteristics of patients with elderly GC patients and different frailty status are presented in Table 2 . Patients combined with severe debilitation were found to be associated with older age ( P = 0.011), higher ASA-PS scores ( P < 0.001), and a lower proportion of neoadjuvant chemotherapy ( P < 0.001) and adjuvant chemotherapy ( P < 0.001). Table 2 Clinical and pathological characteristics of elderly gastric cancer patients classified by the 5-item modified frailty index in training cohort Characteristics Frailty Index = 0 (n = 207) Frailty Index = 1 (n = 176) Frailty Index = 2 (n = 151) Frailty Index ≥ 3 (n = 119) p -value Age, mean(SD), years 73.7 ± 3.2 74.1 ± 3.1 74.3 ± 2.8 74.8 ± 3.1 0.019 BMI, mean(SD), kg/m 2 22.4 ± 3.2 22.3 ± 3.3 22.6 ± 3.2 22.1 ± 3.1 0.665 Sex 0.381 Male 122 (58.9) 99 (56.3) 98 (64.9) 75 (64.9) Female 85 (41.1) 77 (43.8) 53 (35.1) 44 (35.1) ASA-PS, n (%) <0.001 I or II 172 (83.1) 108 (61.4) 84 (55.6) 40 (33.6) III or IV 35 (16.9) 68 (38.6) 67 (44.4) 79 (66.4) History of COPD 0 (0.0) 23 (13.1) 34 (22.5) 72 (62.6) <0.001 History of CHF 0 (0.0) 3 (1.7) 10 (6.6) 15 (13.0) <0.001 Partially/totally dependent 0 (0.0) 53 (30.1) 78 (51.7) 100 (87.0) <0.001 History of hypertension 0 (0.0) 58 (33.0) 105 (69.2) 103 (89.6) <0.001 History of diabetes 0 (0.0) 39 (22.1) 75 (49.7) 81 (70.4) <0.001 History of abdominal surgery 24 (11.6) 21 (11.9) 22 (14.6) 20 (16.8) 0.515 Tumor location, n (%) 0.358 Upper third 73 (35.3) 54 (30.7) 59 (39.1) 31 (26.1) Middle third 57 (27.5) 48 (27.3) 37 (24.5) 33 (27.7) Lower third 77 (37.2) 74 (42.0) 55 (36.4) 55 (46.2) Operative procedure, n(%) 0.093 Distal gastrectomy 115 (55.6) 89 (50.6) 76 (50.3) 71 (59.7) Total gastrectomy 66 (31.9) 75 (42.6) 64 (42.4) 38 (31.9) Subtotal gastrectomy 26 (12.6) 12 (6.8) 11 (7.3) 10 (8.4) Lymph node dissection, n(%) 0.198 D1+ 152 (73.4) 114 (64.8) 110 (72.8) 88 (73.9) D2 55 (26.6) 62 (35.2) 41 (27.2) 31 (26.1) Type of reconstruction, n(%) 0.306 Billroth I 35 (16.9) 31 (17.6) 21 (13.9) 29 (24.4) Billroth II 43 (20.8) 32 (18.2) 38 (25.2) 22 (18.5) Roux-en-Y 129 (62.3) 113 (64.2) 92 (60.9) 68 (57.1) Open surgery, n (%) 14 (6.8) 16 (9.1) 19 (12.6) 18 (15.1) 0.073 Tumor size, mean(SD), cm 4.3 ± 1.2 4.4 ± 1.1 4.5 ± 1.2 4.3 ± 1.2 0.456 Pathological T stage, n (%) 0.339 T1 54 (26.1) 46 (26.1) 44 (29.1) 39 (32.8) T2 35 (16.9) 36 (20.5) 36 (23.8) 25 (21.0) T3 77 (37.2) 58 (33.0) 54 (35.8) 38 (31.9) T4 41 (19.8) 36 (20.5) 17 (11.3) 17 (14.3) Pathological N stage, n (%) 0.487 N0 102 (48.3) 94 (53.4) 88 (58.3) 75 (63.0) N1 43 (20.8) 30 (17.0) 29 (19.2) 20 (16.8) N2 28 (13.5) 23 (13.1) 16 (10.6) 12 (10.1) N3 34 (16.4) 29 (16.5) 18 (11.9) 12 (10.1) Pathological TNM stage, n (%) 0.096 I 69 (33.3) 60 (34.1) 60 (39.7) 52 (43.7) II 53 (25.6) 56 (31.8) 48 (31.8) 35 (29.4) III 85 (41.1) 60 (34.1) 43 (28.5) 32 (26.9) Differentiation grade 0.143 Well/moderate 97 (46.9) 75 (42.6) 71 (47.0) 67 (56.3) Poor/undifferentiated 110 (53.1) 101 (57.4) 80 (53.0) 52 (43.7) Preoperative chemotherapy, n (%) 11 (5.3) 4 (2.3) 1 (0.7) 0 (0.0) <0.001 Adjuvant chemotherapy, n (%) 57 (27.5) 33 (18.8) 22 (14.6) 9 (7.6) <0.001 Lymphovascular invasion, n (%) 96 (46.4) 88 (50.0) 69 (45.7) 61 (51.3) 0.723 Perineural invasion, n (%) 58 (28.0) 48 (27.3) 31 (20.5) 29 (24.4) 0.387 Postoperative duration of hospital stays (days), median (IQR) 10 (7–14) 11.5 (8–14) 12 (8–15) 15 (11–19) <0.001 Postoperative overall complication, n (%) 42 (20.3) 56 (31.8) 56 (37.1) 51 (42.9) <0.001 Postoperative severe complication * , n (%) 10 (4.8) 18 (10.2) 20 (13.2) 18 (15.1) <0.001 ASA-PS: American Society of Anesthesiology Physical Status Classification; COPD, chronic obstructive pulmonary disease; CHF, congestive heart failure; IQR: inter-quartile range; *:Clavien-Dindo grade ≥ III. Regarding postoperative complications, patients with higher 5-mFI scores exhibited significantly elevated rates of postoperative overall complications [20.3% (5-mFI = 0) vs. 31.8% (5-mFI = 1) vs. 37.1% (5-mFI = 2) vs. 42.9% (5-mFI ≥ 3), P < 0.001] and severe complications (4.8% vs. 10.2% vs. 13.2% vs. 15.1%, P < 0.001). Furthermore, Patients with severe frailty had significantly longer postoperative hospital stay (10d vs. 11d vs. 12d vs. 15d, P < 0.001). Association between different frailty status and prognosis of elderly GC patients In the training set, the median follow-up was 57.0 months (range, 2-119 months), during which 291 patients occurred cancer-specific deaths and 146 occurred non-cancer related death. Significantly lower 5-year OS rate was observed in patients with severe frailty than in the other three groups [63.3% (5-mFI = 0) vs. 43.1% (5-mFI = 1) vs. 41.6% (5-mFI = 2) vs. 31.1% (5-mFI ≥ 3), P < 0.001]. Similar significant results were obtained for 5-year CSS rates of elderly patients with GC (Fig. 3B). The cumulative 5-year incidence of CSS in patients with severe frailty was 40.6%, indicating significantly poorer outcomes compared to those with moderate frailty(54.4%), pre-frailty (53.4%), and fit (73.4%). Moreover, statistically significant differences were observed in the 5-year cumulative incidence of competing risk events and frailty status (13.7% vs. 19.1% vs. 23.3% vs. 23.4%, P < 0.001). Factors associated with CSS of elderly GC patients The univariate and multivariate competing-risks regression for CSS of elderly GC patients are shown in Table 3 . The univariate competing risk analysis showed that age, 5-mFI, tumor size, differentiation grade, T stage, N stage, TNM stage, lymphovascular invasion, perineural invasion, and adjuvant therapy were identified as risk factors for CSS of elderly GC patients (all P < 0.05).Further multivariate analysis revealed that age ≥ 80 years [subdistribution hazard ratio (SHR) = 1.62, 95% confidence interval (CI) :1.08–2.43, P = 0.02], 5-mFI ≥ 3 (SHR = 2.84, 95% CI: 2.04–3.96, P < 0.001), undifferentiated state (SHR = 3.29, 95% CI: 1.53–7.09, P < 0.001), T4 stage (SHR = 1.73, 95% CI: 1.05–2.86, P = 0.03), N3 stage (SHR = 4.06, 95% CI: 2.68–6.51, P < 0.001), and perineural invasion (SHR = 1.45, 95% CI: 1.05–2.00, P = 0.03) were independent risk factors of CSS in elderly GC patients. Table 3 Univariate and multivariate competing risk analyses of risk factors for cancer specific survival in elderly gastric cancer patients after curative surgery Variables Univariate analysis Multivariate analysis SHR (95% CI) p -value SHR (95% CI) p -value Age 70–74 Reference Reference 75–79 1.38 (1.08–1.76) 0.01 1.29 (1.00-1.67) 0.04 80+ 1.48 (1.04–2.15) 0.04 1.62 (1.08–2.43) 0.02 Sex 0.27 male Reference female 0.88 (0.69–1.11) BMI < 18.5 Reference 18.5ཞ24.9 1.30 (0.86–1.95) 0.21 25.0ཞ29.9 1.05 (0.66–1.66) 0.84 ≥ 30 0.72 (0.22–2.35) 0.58 ASA-PS 0.49 I or II Reference III or IV 1.09 (0.86–1.37) 5-item modified frailty index 0 Reference Reference 1 1.75 (1.19–2.20) <0.001 1.65 (1.18–2.32) 0.004 2 1.81 (1.52–2.80) <0.001 1.68 (1.18–2.40) 0.004 ≥ 3 3.00 (1.52–2.80) <0.001 2.84 (2.04–3.96) <0.001 History of abdominal surgery 0.79 No Reference Yes 0.95 (0.68–1.35) Tumor location Upper third Reference Middle third 1.03 (0.76–1.40) 0.86 Lower third 1.09 (0.83–1.42) 0.54 Neoadjuvant therapy 0.15 No Reference Yes 0.49 (0.19–1.30) Surgical approach 0.75 Minimally invasive surgery Reference Open surgery 1.06 (0.74–1.54) Operative procedure Distal gastrectomy Reference Total gastrectomy 1.09 (0.86–1.39) 0.48 Subtotal gastrectomy 0.94 (0.62–1.41) 0.75 Extent of lymphadenectomy 0.22 D1+ Reference D2 0.85 (0.65–1.11) Type of reconstruction Billroth I Reference Billroth II 1.20 (0.81–1.77) 0.36 Roux-en-Y 1.33 (0.97–1.84) 0.08 Tumor size, mm 0.04 0.17 < 40 Reference Reference ≥ 40 1.30 (1.02–1.66) 1.20 (0.93–1.55) Differentiation grade Well differentiated Reference Reference Moderately differentiated 2.97 (1.69–5.22) <0.001 2.02 (1.14–3.60) <0.001 Poorly differentiated 4.61 (2.66–7.98) <0.001 2.23 (1.25–3.96) <0.001 Undifferentiated 6.61 (3.32–13.16) <0.001 3.29 (1.53–7.09) <0.001 Pathological T stage T1 Reference Reference T2 1.61 (1.06–2.46) 0.026 1.14 (0.74–1.77) 0.55 T3 3.52 (2.48–5.01) <0.001 1.72 (1.12–2.66) 0.01 T4 5.35 (3.69–7.75) <0.001 1.73 (1.05–2.86) 0.03 Pathological N stage N0 Reference Reference N1 2.42 (1.76–3.34) <0.001 2.01 (1.14–3.60) <0.001 N2 3.63 (2.62–5.03) <0.001 2.43 (1.25–3.96) <0.001 N3 6.75 (4.97–9.16) <0.001 4.06 (2.68–6.15) <0.001 Pathological TNM stage I Reference II 3.37 (2.36–4.81) <0.001 III 6.68 (4.78–9.33) <0.001 Lymphovascular invasion <0.001 0.74 Present Reference Reference Absent 1.74 (1.37–2.20) 1.05 (0.78–1.42) Perineural invasion <0.001 Present Reference Reference 0.03 Absent 2.78 (2.17–3.57) 1.45 (1.05-2.00) Adjuvant therapy <0.001 0.26 Yes Reference Reference No 1.86 (1.48–2.34) 0.85 (0.64–1.13) SHR: subdistribution hazard ration; ASA-PS: American Society of Anesthesiology Physical Status Classification. Construction and validation of the CSS prediction models The prediction model (Model 1) was constructed based on the six predictors from the multivariate competing risk analysis and was shown in Fig. 4. To further illustrate the importance of frailty assessment in predicting the prognosis of elderly GC patients, we constructed two additional models as controls. Model 2 contained features that were significant in the multivariate analysis except for frailty status assessment. Model 3 contained only the TNM staging system. The AUCs of 1-, 3-, and 5-year CSS of Model 1 were 0.768 (95% CI: 0.719–0.816), 0.823 (95% CI: 0.789–0.856), and 0.838 (95% CI: 0.804–0.871), which was statistically better than Model 2 and Model 3 (Supplementary Fig. 1). Likewise, consistent findings were also presented in the internal validation set and external validation set (Supplementary Figs. 2–3). The 5-year C-index of Model 1 was 0.771, 0.784, and 0.784 in the training, internal validation, and external validation cohort, respectively, which was superior to other models (Supplementary Figs. 4–6). It manifested that Model 1 had a better prediction ability. In addition, DCA also showed that the clinical utility of Model 1 was higher than that of Model 2 and Model 3 in predicting CSS at 1-, 3- and 5 years in the training set, internal validation set and external validation set (Supplementary Figs. 7–9). To further verify the discriminative ability of the Model 1, we performed risk classification based on the optimal cut-off value (142 points), which was established by X-tile 3.6.1 software in the training set (Supplementary Fig. 10). There was a significant prognostic difference in 5-year CSS rates between the high- and low-risk groups in the training cohort (23.3% vs. 73.8%, P < 0.001, Fig. 5). Similar results were validated in the internal and combined external validation sets (Supplementary Figs. 11–12). Discussion Globally, the average age of GC patients and the proportion of elderly GC patients are increasing. Accurate assessment of prognosis is critical to guide treatment decision making and clinical management of elderly patients with GC. The prognostic variability observed among elderly patients with GC is primarily evidenced by the marked differences in outcomes among patients with comparable AJCC stages 15 . This heterogeneity is largely attributable to the discrepancy between individuals' physiological and chronological ages. An assessment of frailty status effectively captures and distinguishes this heterogeneity 16 . In the current study, we found that severe frailty status was significantly associated with elevated risk of cancer-specific mortality and non-malignancy mortality in elderly GC. Furthermore, to the best of our knowledge, this is the first study to establish a CSS prediction model based on multidimensional frailty assessment for elderly GC patients. The prediction model demonstrated excellent discrimination and calibration in predicting CSS compared with other models, which have been validated in an independent external cohort. Currently, no consensus exists regarding standardized criteria for defining and assessing frailty in clinical practice. Multiple frailty assessment instruments have been applied for elderly GC patients, as documented in existing literature, yet their validation status remains variable 17 . Existing frailty evaluations in geriatric populations demonstrate significant heterogeneity in reported outcomes, primarily attributable to two factors: the use of divergent assessment scales and their implementation in diverse population groups. An optimal frailty measurement tool should strategically balance clinical practicality with measurement precision. The frailty assessment instrument employed in this study was derived from the frailty index (FI), serving as a prototypical example of the cumulative deficit model. Although the FI has demonstrated strong predictive validity for postoperative complications and long-term prognosis, its clinical utility in routine practice is substantially limited by the requirement to evaluate over 70 distinct clinical parameters. This operational complexity creates a discrepancy between research utility and practical application in clinical settings. Building upon this methodological evolution, Saxton et al. refined the FI to an 11-item metric through analysis of National Surgical Quality Improvement Program datasets, preserving most of the original predictive efficacy 18 . Subsequent analysis by Subramaniam et al. established statistical equipoise between the 5-mFI and 11-item modified frailty index (11-mFI) in surgical cohorts, with Pearson's correlation coefficients exceeding 0.90 between the 5-mFI and 11-mFI 19 . Based on these validation studies, the streamlined 5-mFI was selected as the preoperative frailty assessment framework for geriatric GC patients in this study. Beyond confirming the robust association between frailty and cancer-specific mortality, our study revealed that frailty independently correlated with elevated risk of non-cancer-related mortality. Our investigation observed that 23.2% (297/1278) of elderly GC patients experienced competing mortality from non-oncological causes. The causes of non-cancer death in this study included accidents, cardiovascular and cerebrovascular diseases, pneumonia, COPD and other non-cancer causes. Non-cancer mortality acts as competing events that compromise the validity of conventional survival analysis in quantifying cancer-specific mortality risk. Under competing risks scenarios, non-cancer mortality creates informative censoring that violates the independent censoring assumption required by Kaplan-Meier estimators, necessitating the use of cumulative incidence functions for unbiased estimation 20 . In order to more accurately evaluate the risk factors affecting the prognosis of elderly patients with GC, we chose the competing risk model as the analysis method in this study. In multivariable-adjusted competing risks analysis, severe frailty was confirmed as an independent risk factor for cancer-specific mortality (SHR = 2.84, 95% CI: 2.04–3.96) in elderly patients with GC. The underlying mechanisms that may account for the association between frailty and poor prognosis may be attributed to the following reasons. Previous studies have confirmed that preoperative frailty syndrome is significantly associated with severe postoperative complications in elderly patients, corroborating the findings of our current investigation 21 , 22 . The comorbidities and poor physical function status involved in 5-mFI increase the risk of postoperative morbidity in elderly patients with GC. This pathophysiological cascade may induce alterations in immune homeostasis, potentially creating a permissive microenvironment for residual micrometastatic disease 23 . In addition, emerging evidence suggests that frailty in cancer patients correlates with chronic low-grade systemic inflammation and compromised immune function, thereby potentially exacerbating tumor invasiveness and disease progression 24 . Recent research advances document that frailty is controlled by genetic pathways and biochemical processes conserved in evolution 25 . Potential hallmarks, including genomic instability, epigenetic alterations, loss of proteostasis, dysregulated nutrient sensing, and mitochondrial dysfunction, are likely interconnected and have varying contribution weights across different frailty phenotypes 26 . These epigenetic changes may also contribute to the adverse biological response of patients to cancer treatment and lead to poor prognosis 27 . However, the molecular and cellular mechanisms involved in frailty are still ambiguous. Further in-depth studies are required to further explore the detailed underlying mechanisms. Individualized survival prediction constitutes the cornerstone of precision oncology frameworks. Having established frailty-prognosis associations in elderly GC patients, we subsequently developed a CSS nomogram integrating frailty metrics. Comparative analyses demonstrated incremental predictive value of frailty assessment: the model 1 outperformed both the clinicopathological model (Model 2; ΔAUC = 0.039) and the TNM stage-based model (model 3; ΔAUC = 0.097). The generalizability and extrapolation of prediction models have been the key constraints for further application. The clinicopathologic features and results of frailty assessment involved in the prediction model are easily accessible in clinical practice. Moreover, the validity of the prediction model was further verified by risk stratification in the internal and external validation sets, indicating that the model was still robust in different cohorts. Thus, this model will help clinicians to quickly and accurately assess the CSS of elderly patients with GC, and at the same time identify high-risk groups and effectively develop corresponding personalization of adjuvant therapies, surveillance protocols, and follow-up intervals. In addition to the above findings, univariate competing risk analysis of this study showed that postoperative adjuvant chemotherapy tended to improve CSS of GC patients, but the difference was not statistically significant after adjusting for confounders by multivariate analysis. In clinical practice, the proportion of elderly patients with GC receiving adjuvant chemotherapy is significantly reduced due to concerns about the toxic and side effects of chemotherapy 28 . Thus, whether postoperative adjuvant chemotherapy can bring survival benefits to elderly patients with GC has always been debated. In the sub-group analysis of the prospective randomized controlled trial (RCT) by Sakuramoto et al., the results showed that patients older than 60 years of age who received postoperative adjuvant S-1 therapy did not have a significant survival benefit compared with patients who underwent surgery alone 29 . In the phase III CLASSIC trial, adjuvant CAPOX significantly improved 5-year disease-free survival (HR = 0.51, 95% CI: 0.34–0.78) but not 5-year OS (HR = 0.70, 95% CI: 0.44–1.12) in GC patients older than 65 years 30 . It should be noted that the above results were derived from comparisons in subgroup analysis stratified by age. At present, little other evidence is available directly assessing the effects of adjuvant therapy in the older GC patients. Only one multicenter RCT study in Japan is comparing the efficacy of modified S-1 adjuvant chemotherapy in frail elderly patients with pathological stage II/III GC 31 . We look forward to more relevant research focusing on elderly patients with GC in the future. This multicenter retrospective cohort study has several limitations. First, the retrospective design introduces an inherent risk of selection bias that may influence case ascertainment and data collection. Secondly, the generalizability of our findings may be constrained by the single-center origin of the external validation cohort, which might not fully represent the heterogeneous clinicopathological profiles observed in elderly GC populations across different geographic regions. Thus, further prospective, multi-regional studies are warranted to confirm our observations. Lastly, frailty state demonstrates temporal variability influenced by both disease progression and physiological aging processes. Our current investigation did not longitudinally assess the prognostic implications of these evolving frailty trajectories on elderly GC patients. Conclusion Severely debilitating condition was associated with worse short-term and long-term clinical outcomes of elderly GC patients after surgical resection. The implementation of a competing risk prediction model incorporating comprehensive frailty metrics significantly enhances the predictive precision for CSS in geriatric GC populations, providing superior risk stratification compared to conventional prognostic systems. Declarations Funding information: This project was supported by Yulin city science and Technology Program project (2024-SF-039). Data availability statement: The raw data and supporting materials used the current study is available from the corresponding author on reasonable request. Disclosures The authors declare no conflicts of interest. References Bray F. et al . Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians . 74 (3), 229-263 (2024). Joharatnam-Hogan N. et al . Challenges in the treatment of gastric cancer in the older patient. Cancer Treat Rev . 85 , 101980 (2020). Matsushima R. et al . An increasing trend of gastric cancer deaths and inadequate preventive measures in elderly adults. Helicobacter . 28 (4), e12988 (2023). Ajani J.A. et al . Gastric Cancer, Version 2.2022, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw . 20 (2), 167-192 (2022). Wang F.H. et al . The Chinese Society of Clinical Oncology (CSCO): Clinical guidelines for the diagnosis and treatment of gastric cancer, 2023. Cancer communications (London, England) . 44 (1), 127-172 (2024). Dent E. et al . Management of frailty: opportunities, challenges, and future directions. Lancet (London, England) . 394 (10206), 1376-1386 (2019). Liu T. et al . Frailty and prognosis in lung cancer: systematic review and meta-analysis. BMJ supportive & palliative care . 14 (2), 121-131 (2024). Yamada S. et al . Significance of Frailty in Prognosis After Hepatectomy for Elderly Patients with Hepatocellular Carcinoma. Ann Surg Oncol . 28 (1), 439-446 (2021). Deol E.S. et al . Frailty and survival among veterans treated with abiraterone or enzalutamide for metastatic castration-resistant prostate cancer. Journal of geriatric oncology . 14 (5), 101520 (2023). Wildiers H. et al . International Society of Geriatric Oncology consensus on geriatric assessment in older patients with cancer. J Clin Oncol . 32 (24), 2595-2603 (2014). Mohile S.G. et al . Practical Assessment and Management of Vulnerabilities in Older Patients Receiving Chemotherapy: ASCO Guideline for Geriatric Oncology Summary. J Oncol Pract . 14 (7), 442-446 (2018). Panayi A.C. et al . The modified 5-item frailty index is a predictor of perioperative risk in head and neck microvascular reconstruction: An analysis of 3795 cases. Am J Otolaryngol . 42 (6), 103121 (2021). Guiab K. et al . Can the 5-item Modified Frailty Index Predict Outcomes in Geriatric Trauma? A National Database Study. World journal of surgery . 46 (10), 2328-2334 (2022). Camp R.L. et al . X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res . 10 (21), 7252-7259 (2004). Saif M.W. et al . Gastric cancer in the elderly: an overview. European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology . 36 (8), 709-717 (2010). Rohrmann S. Epidemiology of Frailty in Older People. Adv Exp Med Biol . 1216 , 21-27 (2020). Liang H. et al . Frailty and long-term survival of patients with gastric cancer: a meta-analysis. Front Oncol . 13 , 1239781 (2023). Saxton A. et al . Preoperative frailty and quality of life as predictors of postoperative complications. Annals of surgery . 253 (6), 1223-1229 (2011). Subramaniam S. et al . New 5-Factor Modified Frailty Index Using American College of Surgeons NSQIP Data. Journal of the American College of Surgeons . 226 (2), 173-181.e178 (2018). Austin P.C. et al . Practical recommendations for reporting Fine-Gray model analyses for competing risk data. Stat Med . 36 (27), 4391-4400 (2017). Kim G. et al . Frailty in Elderly Gastric Cancer Patients Undergoing Gastrectomy. Dig Surg . 1-7 (2020). Kouzu K. et al . Preoperative fall risk assessment score as a prognostic factor in gastric cancer patients after gastrectomy. Jpn J Clin Oncol . 51 (4), 569-576 (2021). Zhang X. et al . The Biology of Aging and Cancer: Frailty, Inflammation, and Immunity. Cancer J . 23 (4), 201-205 (2017). Ferrucci L. et al . Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat Rev Cardiol . 15 (9), 505-522 (2018). Csete M.E. Basic Science of Frailty-Biological Mechanisms of Age-Related Sarcopenia. Anesth Analg . 132 (2), 293-304 (2021). Salvioli S. et al . Biomarkers of aging in frailty and age-associated disorders: State of the art and future perspective. Ageing research reviews . 91 , 102044 (2023). Maccormick R.E. Possible acceleration of aging by adjuvant chemotherapy: a cause of early onset frailty? Med Hypotheses . 67 (2), 212-215 (2006). Matthaiou C. et al . Management of gastric cancer in older adults. Journal of geriatric oncology . 8 (6), 403-406 (2017). Sakuramoto S. et al . Adjuvant chemotherapy for gastric cancer with S-1, an oral fluoropyrimidine. N Engl J Med . 357 (18), 1810-1820 (2007). Noh S.H. et al . Adjuvant capecitabine plus oxaliplatin for gastric cancer after D2 gastrectomy (CLASSIC): 5-year follow-up of an open-label, randomised phase 3 trial. Lancet Oncol . 15 (12), 1389-1396 (2014). Mizutani T. et al . A phase III trial to confirm modified S-1 adjuvant chemotherapy for pathological stage II/III vulnerable elderly gastric cancer patients who underwent gastric resection (JCOG1507, BIRDIE). Jpn J Clin Oncol . 48 (12), 1101-1104 (2018). Additional Declarations No competing interests reported. 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09:54:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2530501,"visible":true,"origin":"","legend":"\u003cp\u003eOverall survival curve (\u003cstrong\u003eA\u003c/strong\u003e), cancer-specific survival curve (\u003cstrong\u003eB\u003c/strong\u003e), and competitive risk curve (\u003cstrong\u003eC\u003c/strong\u003e) for different preoperative frailty status of elderly gastric cancer patients.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6618864/v1/cb6bea313bea96aa60ebc019.png"},{"id":90883466,"identity":"8446f9c3-8755-4db7-b6ba-c05e8c71f0b0","added_by":"auto","created_at":"2025-09-09 09:54:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":327330,"visible":true,"origin":"","legend":"\u003cp\u003eCompeting risk nomogram of cancer-specific survival for elderly gastric cancer 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study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith the intensification of \u0026zwnj;global population aging, healthcare\u0026zwnj; planning and delivery \u0026zwnj;for\u0026zwnj; elderly patients \u0026zwnj;are\u0026zwnj; facing \u0026zwnj;unprecedented\u0026zwnj; challenges. This demographic shift holds particular significance in gastric cancer (GC) management, a disease\u0026zwnj; characterized by \u0026zwnj;both\u0026zwnj; high prevalence and substantial mortality \u0026zwnj;in\u0026zwnj; older \u0026zwnj;populations\u0026zwnj;. According to the 2022 global cancer statistics, 968,350 new GC cases were identified, accounting for 4.9% of all malignancies\u003csup\u003e1\u003c/sup\u003e. Notably, the\u0026zwnj; elderly \u0026zwnj;population\u0026zwnj; (defined \u0026zwnj;as\u0026zwnj; age\u0026thinsp;\u0026ge;\u0026thinsp;70 years) \u0026zwnj;has emerged as\u0026zwnj; a progressively expanding \u0026zwnj;patient\u0026zwnj; subset \u0026zwnj;in\u0026zwnj; GC \u0026zwnj;epidemiology\u0026zwnj;, with \u0026zwnj;population-based\u0026zwnj; data demonstrating \u0026zwnj;a sustained increase\u0026zwnj; in \u0026zwnj;the\u0026zwnj; mean age \u0026zwnj;at\u0026zwnj; diagnosis \u0026zwnj;during\u0026zwnj; recent decades\u003csup\u003e2, 3\u003c/sup\u003e. The\u0026zwnj; high prevalence of \u0026zwnj;age-related\u0026zwnj; comorbidities \u0026zwnj;renders\u0026zwnj; treatment and follow-up \u0026zwnj;strategies\u0026zwnj; for elderly GC patients \u0026zwnj;particularly\u0026zwnj; complex and multifaceted. \u0026zwnj;Nevertheless,\u0026zwnj; current clinical practice guidelines \u0026zwnj;remain\u0026zwnj; based \u0026zwnj;primarily\u0026zwnj; on clinical trials \u0026zwnj;that\u0026zwnj; selectively \u0026zwnj;exclude\u0026zwnj; elderly patients \u0026zwnj;through\u0026zwnj; age \u0026zwnj;cutoffs\u0026zwnj; or \u0026zwnj;functional status\u0026zwnj; criteria \u0026zwnj;to\u0026zwnj; minimize \u0026zwnj;confounding\u0026zwnj;\u003csup\u003e4, 5\u003c/sup\u003e, resulting in\u0026zwnj; insufficient evidence-based \u0026zwnj;guidance\u0026zwnj; for \u0026zwnj;geriatric\u0026zwnj; GC management..\u003c/p\u003e\u003cp\u003eFrailty, \u0026zwnj;as\u0026zwnj; a multidimensional \u0026zwnj;geriatric\u0026zwnj; syndrome, \u0026zwnj;represents\u0026zwnj; a \u0026zwnj;critical\u0026zwnj; concept \u0026zwnj;in\u0026zwnj; clinical \u0026zwnj;evaluation\u0026zwnj; of elderly patients. \u0026zwnj;While\u0026zwnj; \u0026zwnj;operational\u0026zwnj; definitions \u0026zwnj;of frailty remain\u0026zwnj; heterogeneous, \u0026zwnj;current consensus\u0026zwnj; characterizes \u0026zwnj;it\u0026zwnj; as \u0026zwnj;a\u0026zwnj; state \u0026zwnj;of\u0026zwnj; increased \u0026zwnj;vulnerability to\u0026zwnj; stressors \u0026zwnj;and\u0026zwnj; diminished \u0026zwnj;physiologic\u0026zwnj; reserve\u003csup\u003e6\u003c/sup\u003e. Emerging\u0026zwnj; evidence \u0026zwnj;has\u0026zwnj; \u0026zwnj;established\u0026zwnj; associations between frailty \u0026zwnj;status\u0026zwnj; and \u0026zwnj;poor\u0026zwnj; clinical \u0026zwnj;outcomes\u0026zwnj; \u0026zwnj;across\u0026zwnj; malignancies, including lung cancer\u003csup\u003e7\u003c/sup\u003e, liver cancer\u003csup\u003e8\u003c/sup\u003e, prostate cancer\u003csup\u003e9\u003c/sup\u003e, etc. The International Society for Geriatric Oncology\u003csup\u003e10\u003c/sup\u003e and the ASCO Guideline for Geriatric Oncology\u003csup\u003e11\u003c/sup\u003e advocate\u0026zwnj; \u0026zwnj;routine\u0026zwnj; geriatric \u0026zwnj;assessments\u0026zwnj; \u0026zwnj;for\u0026zwnj; cancer patients \u0026zwnj;aged\u0026zwnj; \u0026ge;70 years \u0026zwnj;to\u0026zwnj; \u0026zwnj;inform\u0026zwnj; risk stratification \u0026zwnj;and\u0026zwnj; treatment \u0026zwnj;planning\u0026zwnj;. \u0026zwnj;Given\u0026zwnj; \u0026zwnj;the\u0026zwnj; \u0026zwnj;prognostic\u0026zwnj; significance \u0026zwnj;of\u0026zwnj; frailty \u0026zwnj;in\u0026zwnj; GC, \u0026zwnj;developing\u0026zwnj; a comprehensive prognostic model \u0026zwnj;incorporating\u0026zwnj; frailty \u0026zwnj;parameters\u0026zwnj; \u0026zwnj;could\u0026zwnj; \u0026zwnj;enhance\u0026zwnj; \u0026zwnj;patient-specific\u0026zwnj; outcome \u0026zwnj;prediction\u0026zwnj;. To our knowledge, specific predictive models for cancer-specific mortality in geriatric\u0026zwnj; GC patients are currently lacking.\u003c/p\u003e\u003cp\u003e\u0026zwnj;Therefore,\u0026zwnj; \u0026zwnj;this\u0026zwnj; study \u0026zwnj;aimed to\u0026zwnj; \u0026zwnj;evaluate \u0026zwnj;frailty-associated\u0026zwnj; prognostic \u0026zwnj;impacts\u0026zwnj; and \u0026zwnj;construct \u0026zwnj;and\u0026zwnj; validate \u0026zwnj;frailty-integrated\u0026zwnj; survival prediction models \u0026zwnj;using\u0026zwnj; multi-center \u0026zwnj;data\u0026zwnj; \u0026zwnj;from\u0026zwnj; two tertiary \u0026zwnj;referral\u0026zwnj; hospitals. \u0026zwnj;Such\u0026zwnj; \u0026zwnj;personalized\u0026zwnj; prediction \u0026zwnj;tools\u0026zwnj; \u0026zwnj;may\u0026zwnj; \u0026zwnj;facilitate\u0026zwnj; \u0026zwnj;individualized\u0026zwnj; clinical decision-making \u0026zwnj;and\u0026zwnj; \u0026zwnj;optimize\u0026zwnj; follow-up \u0026zwnj;protocols\u0026zwnj; \u0026zwnj;in\u0026zwnj; \u0026zwnj;this\u0026zwnj; vulnerable \u0026zwnj;population\u0026zwnj;.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and patients\u003c/h2\u003e\u003cp\u003eThis \u0026zwnj;retrospective\u0026zwnj; multicenter cohort study \u0026zwnj;analyzed\u0026zwnj; clinicopathological \u0026zwnj;characteristics\u0026zwnj; and \u0026zwnj;prognostic\u0026zwnj; data \u0026zwnj;collected\u0026zwnj; from \u0026zwnj;prospectively maintained\u0026zwnj; databases \u0026zwnj;at\u0026zwnj; two tertiary hospitals \u0026zwnj;between\u0026zwnj; January 2015 \u0026zwnj;and\u0026zwnj; December 2022. \u0026zwnj;Patients meeting\u0026zwnj; the \u0026zwnj;following\u0026zwnj; inclusion criteria \u0026zwnj;were\u0026zwnj; \u0026zwnj;eligible\u0026zwnj;: (1) \u0026zwnj;histopathologically confirmed\u0026zwnj; gastric adenocarcinoma, (2) \u0026zwnj;diagnosis\u0026zwnj; age\u0026thinsp;\u0026ge;\u0026thinsp;70 years, \u0026zwnj;and\u0026zwnj; (3) \u0026zwnj;receipt of\u0026zwnj; curative-intent surgical resection. \u0026zwnj;Exclusion criteria\u0026zwnj; \u0026zwnj;comprised\u0026zwnj;: (1) \u0026zwnj;presence of\u0026zwnj; distant metastasis, (2) \u0026zwnj;history of\u0026zwnj; other \u0026zwnj;malignancies\u0026zwnj;, (3) \u0026zwnj;surgery\u0026zwnj; for \u0026zwnj;local\u0026zwnj; recurrence, (4) emergency \u0026zwnj;procedures\u0026zwnj;, \u0026zwnj;or\u0026zwnj; (5) \u0026zwnj;missing\u0026zwnj; clinicopathological \u0026zwnj;or\u0026zwnj; follow-up \u0026zwnj;records\u0026zwnj;.\u003c/p\u003e\u003cp\u003e\u0026zwnj;From\u0026zwnj; 1,278 \u0026zwnj;consecutive\u0026zwnj; patients \u0026zwnj;screened\u0026zwnj;, \u0026zwnj;the final\u0026zwnj; cohort \u0026zwnj;comprised\u0026zwnj; 934 \u0026zwnj;subjects\u0026zwnj; \u0026zwnj;from\u0026zwnj; \u0026zwnj;the\u0026zwnj; First Affiliated Hospital of Xi'an Jiaotong University (\u0026zwnj;Xi'an\u0026zwnj; cohort) \u0026zwnj;and\u0026zwnj; 344 \u0026zwnj;from\u0026zwnj; the Yulin Hospital of the First Affiliated Hospital of Xi 'an Jiaotong University (\u0026zwnj;Yulin\u0026zwnj; cohort). \u0026zwnj;Using\u0026zwnj; computerized \u0026zwnj;randomization\u0026zwnj;, \u0026zwnj;the\u0026zwnj; Xi'an cohort \u0026zwnj;was\u0026zwnj; \u0026zwnj;stratified\u0026zwnj; \u0026zwnj;into\u0026zwnj; \u0026zwnj;a\u0026zwnj; training set (n\u0026thinsp;=\u0026thinsp;653) \u0026zwnj;and\u0026zwnj; \u0026zwnj;an\u0026zwnj; internal validation set (n\u0026thinsp;=\u0026thinsp;281) \u0026zwnj;at\u0026zwnj; \u0026zwnj;a\u0026zwnj; 7:3 ratio \u0026zwnj;for\u0026zwnj; model \u0026zwnj;development\u0026zwnj; \u0026zwnj;and\u0026zwnj; \u0026zwnj;performance\u0026zwnj; \u0026zwnj;assessment\u0026zwnj;, \u0026zwnj;respectively\u0026zwnj;. \u0026zwnj;The\u0026zwnj; Yulin cohort \u0026zwnj;served as\u0026zwnj; \u0026zwnj;an\u0026zwnj; external validation \u0026zwnj;cohort\u0026zwnj; \u0026zwnj;to\u0026zwnj; \u0026zwnj;evaluate\u0026zwnj; model \u0026zwnj;generalizability\u0026zwnj;. \u0026zwnj;Figure 1\u0026zwnj; \u0026zwnj;illustrates\u0026zwnj; \u0026zwnj;the\u0026zwnj; \u0026zwnj;patient\u0026zwnj; \u0026zwnj;selection\u0026zwnj; \u0026zwnj;process\u0026zwnj;.\u0026zwnj;\u003c/p\u003e\u003cp\u003e \u0026zwnj;This\u0026zwnj; study \u0026zwnj;complied with\u0026zwnj; \u0026zwnj;the\u0026zwnj; Declaration of Helsinki \u0026zwnj;and\u0026zwnj; \u0026zwnj;received\u0026zwnj; \u0026zwnj;approval\u0026zwnj; \u0026zwnj;from\u0026zwnj; \u0026zwnj;the\u0026zwnj; \u0026zwnj;Institutional\u0026zwnj; Review Board \u0026zwnj;of\u0026zwnj; \u0026zwnj;the\u0026zwnj; First Affiliated Hospital of Xi'an Jiaotong University (\u0026zwnj;No.\u0026zwnj; XJTU1AF2020LSL-004). \u0026zwnj;Written\u0026zwnj; informed consent \u0026zwnj;was\u0026zwnj; \u0026zwnj;obtained\u0026zwnj; \u0026zwnj;from\u0026zwnj; \u0026zwnj;all\u0026zwnj; \u0026zwnj;participants\u0026zwnj;.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssessment of frailty status\u003c/h3\u003e\n\u003cp\u003ePatients completed clinical geriatric assessment within 3 days of admission. Frailty status was assessed using the validated 5-item modified frailty index (5-mFI), which demonstrated robust reliability and construct validity in prior studies\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This instrument evaluates five clinical domains: (1) history of severe chronic obstructive pulmonary disease (COPD), (2) congestive heart failure occurring in the month before surgery, (3) ability to perform daily activities (independent or independent), (4) hypertension that necessitates medication, and (5) diabetes mellitus that necessitates oral agents or insulin. Each domain was dichotomously scored (0\u0026thinsp;=\u0026thinsp;absent;1\u0026thinsp;=\u0026thinsp;present), yielding cumulative scores ranging 0\u0026ndash;5. The 5-mFI was categorized as 0, 1, 2, or \u0026ge;\u0026thinsp;3, which was corresponded to fit, pre-frailty, moderate frailty, and severe frailty.\u003c/p\u003e\n\u003ch3\u003eData collection and patient followup\u003c/h3\u003e\n\u003cp\u003eDemographic and clinicopathological variables were systematically extracted from our institution's prospectively maintained GC database. Comprehensive data fields encompassed: preoperative characteristics, surgical pathology reports, postoperative complications, postoperative recovery, and follow-up data.\u003c/p\u003e\u003cp\u003eAfter surgery, all patients were managed used standardized follow-up protocols. Postoperative follow-up was performed every 3 months for the first two years and then every 6 months after. The last follow-up date in this study was 31 December 2024. The overall survival (OS) was calculated from operation time to death or follow-up dead-line. Cancer-specific survival (CSS) was defined as the time from surgery to death due to GC.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted using R Statistical Software (v4.3.2). Categorical variables were compared using the Fisher\u0026rsquo;s exact test. Continuous variables were compared using one-way ANOVA or Kruskal-Wallis \u003cem\u003eH\u003c/em\u003e test based on whether satisfying normal distribution or not. Fine and Gray competing risk models was used to analyze associations between risk factors and gastric cancer-specific mortality. GC mortality was considered the event of interest, while mortality from other specific causes was treated as a competing event. Based on the identified risk factors, predictive models for CSS were established. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, the concordance index (C-index) and decision curve analysis (DCA) were conducted to visually evaluate the performance of predictive nomograms. X-tile software was used to identify the optimal cutoff value for the selected nomogram\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Log-rank test was used to analyze the survival difference between the low-risk and high-risk groups. All statistical tests were two-sided at an α level of 0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePatient baseline characteristics\u003c/h2\u003e\u003cp\u003eFrom January 2015 to December 2022, A total of 1278 older patients with GC were included in this study, including 653 in the training set, 281 in the internal validation set, and 344 in the external validation set. Baseline clinicopathological characteristics across cohorts are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. While the training and internal validation sets demonstrated comparable profiles (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), significant inter-cohort variations emerged between training and external validation sets regarding: age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019), type for digestive tract reconstruction (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), open abdominal surgeries (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), proportion of preoperative chemotherapy (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042), and proportion of adjuvant chemotherapy (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.003). No statistically significant differences were observed in sex, body mass index, American Society of Anesthesiologists physical status (ASA-PS), history of abdominal surgery, tumor location, operative procedure, lymph node dissection, tumor size, pathological T stage, pathological N stage, pathological TNM stage, histological type, lymphovascular invasion, and perineural invasion (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of elderly gastric cancer patients in the training, internal validation and external validation cohorts.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eXi\u0026rsquo;an cohort\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYulin cohort\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTraining set\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;653)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInternal validation\u003c/p\u003e\u003cp\u003eset\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;281)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eExternal validation\u003c/p\u003e\u003cp\u003eSet\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;344)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge, mean(SD), years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e75.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSex, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.724\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.649\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e394 (60.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e173 (61.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e204 (59.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e259 (39.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e108 (38.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e140 (40.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eASA-PS, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI、II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e404 (61.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e181 (64.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e199 (57.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIII、IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e249 (38.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100 (35.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e145 (42.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBMI, mean(SD), kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePrevious abdominal surgery, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87 (13.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39 (17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e33 (9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTumor location, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.196\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUpper third\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e217 (33.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99 (35.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e131 (38.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle third\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e175 (26.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73 (26.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95 (27.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLower third\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e261 (40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109 (38.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e118 (34.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eOperative procedure, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistal gastrectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e351 (53.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e161 (57.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e199 (57.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal gastrectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e243 (37.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98 (34.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e128 (37.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSubtotal gastrectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59 (9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLymph node dissection, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eD1+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e464 (71.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e209 (74.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e265 (77.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eD2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e189 (28.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72 (25.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e79 (23.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eType of reconstruction, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBillroth I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116 (17.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40 (14.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e36 (10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBillroth II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135 (20.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61 (21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e44 (12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRoux-en-Y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e402 (61.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e180 (64.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e264 (76.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eOpen surgery, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67 (10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e68 (19.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTumor size, mean(SD), cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePathological T stage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e183 (28.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89 (31.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e93 (27.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e132 (20.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45 (16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e62 (18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e227 (34.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99 (35.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e122 (35.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e111 (17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 (17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66 (19.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePathological N stage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.896\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e359 (55.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e169 (60.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e196 (57.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e122 (18.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 (17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57 (16.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79 (12.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34 (12.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e42 (12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93 (14.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e49 (14.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePathological TNM stage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.906\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e241 (36.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113 (40.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e127 (36.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e192 (29.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77 (27.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e98 (28.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e220 (33.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91 (32.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e119 (34.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHistological type, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.459\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.493\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWell/moderate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e310 (47.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e126 (44.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e168 (48.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoor/undifferentiated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e343 (52.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e155 (55.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e176 (51.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLymphovascular invasion, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e339 (51.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e157 (55.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e166 (48.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePerineural invasion, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e166 (25.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69 (24.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.780\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e99 (28.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePreoperative chemotherapy, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAdjuvant chemotherapy, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e121 (18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49 (17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e39 (11.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eASA-PS: American Society of Anesthesiology Physical Status Classification;\u003c/p\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e training cohort vs. internal validation group\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e derivation set vs. external validation group\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure 2 illustrates the prognostic differences among the training set, the internal validation set and external validation set. The 5-year OS rates in the training, internal validation, and external validation cohorts were 46.9%, 52.0%, and 49.0%, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.251). The 5-year CSS rates were not significantly different among the three cohorts (57.8% vs. 63.0% vs. 61.1%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.124). There was no significant difference in the 5-year incidence of competing events among the three groups (18.4% vs. 17.3% vs. 21.2%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.942).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssociation between different frailty status and clinicopathological features of elderly GC patients\u003c/h3\u003e\n\u003cp\u003eIn the derivation cohort, 207 patients (31.7%) were allocated to fit (5-mFI\u0026thinsp;=\u0026thinsp;0), 176 patients (27.0%) as pre-frailty (5-mFI\u0026thinsp;=\u0026thinsp;1), 151 patients (23.1%) as moderate frailty (mFI\u0026thinsp;=\u0026thinsp;2), and 119 patients (18.2%) as severe frailty (mFI\u0026thinsp;\u0026ge;\u0026thinsp;3). The association between the clinicopathological characteristics of patients with elderly GC patients and different frailty status are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Patients combined with severe debilitation were found to be associated with older age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), higher ASA-PS scores (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a lower proportion of neoadjuvant chemotherapy (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and adjuvant chemotherapy (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinical and pathological characteristics of elderly gastric cancer patients classified by the 5-item modified frailty index in training cohort\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrailty Index\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;207)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFrailty Index\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;176)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFrailty Index\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;151)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFrailty Index\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;119)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge, mean(SD), years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e74.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBMI, mean(SD), kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.665\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.381\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e122 (58.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99 (56.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e98 (64.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e75 (64.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85 (41.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77 (43.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53 (35.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44 (35.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eASA-PS, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI or II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e172 (83.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e108 (61.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84 (55.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40 (33.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIII or IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68 (38.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67 (44.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e79 (66.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHistory of COPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23 (13.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34 (22.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e72 (62.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHistory of CHF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15 (13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePartially/totally dependent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53 (30.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e78 (51.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100 (87.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHistory of hypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58 (33.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e105 (69.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e103 (89.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHistory of diabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39 (22.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75 (49.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81 (70.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHistory of abdominal surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (11.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20 (16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.515\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTumor location, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUpper third\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73 (35.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54 (30.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e59 (39.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31 (26.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle third\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57 (27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 (27.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37 (24.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33 (27.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLower third\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77 (37.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74 (42.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55 (36.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e55 (46.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eOperative procedure, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistal gastrectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115 (55.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89 (50.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76 (50.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e71 (59.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal gastrectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66 (31.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75 (42.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64 (42.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38 (31.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSubtotal gastrectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10 (8.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLymph node dissection, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.198\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eD1+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e152 (73.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e114 (64.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e110 (72.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88 (73.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eD2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (26.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62 (35.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41 (27.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31 (26.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eType of reconstruction, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.306\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBillroth I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (17.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21 (13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29 (24.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBillroth II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 (18.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38 (25.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22 (18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRoux-en-Y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129 (62.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113 (64.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e92 (60.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e68 (57.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eOpen surgery, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19 (12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18 (15.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTumor size, mean(SD), cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.456\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePathological T stage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.339\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (26.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46 (26.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44 (29.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e39 (32.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36 (20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36 (23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25 (21.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77 (37.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58 (33.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54 (35.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38 (31.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (19.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36 (20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17 (11.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePathological N stage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102 (48.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94 (53.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88 (58.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e75 (63.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29 (19.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20 (16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23 (13.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 (10.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12 (10.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (16.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29 (16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12 (10.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePathological TNM stage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60 (34.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60 (39.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52 (43.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53 (25.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (31.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48 (31.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35 (29.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85 (41.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60 (34.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43 (28.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32 (26.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDifferentiation grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWell/moderate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97 (46.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75 (42.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71 (47.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e67 (56.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoor/undifferentiated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110 (53.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101 (57.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80 (53.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52 (43.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePreoperative chemotherapy, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAdjuvant chemotherapy, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57 (27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33 (18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9 (7.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLymphovascular invasion, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96 (46.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69 (45.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e61 (51.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.723\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePerineural invasion, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58 (28.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 (27.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31 (20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29 (24.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.387\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePostoperative duration of hospital stays (days), median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (7\u0026ndash;14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.5 (8\u0026ndash;14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12 (8\u0026ndash;15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15 (11\u0026ndash;19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePostoperative overall complication, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (20.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (31.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56 (37.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51 (42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePostoperative severe complication\u003csup\u003e*\u003c/sup\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (10.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18 (15.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eASA-PS: American Society of Anesthesiology Physical Status Classification; COPD, chronic obstructive pulmonary disease; CHF, congestive heart failure; IQR: inter-quartile range; *:Clavien-Dindo grade\u0026thinsp;\u0026ge;\u0026thinsp;III.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRegarding postoperative complications, patients with higher 5-mFI scores exhibited significantly elevated rates of postoperative overall complications [20.3% (5-mFI\u0026thinsp;=\u0026thinsp;0) vs. 31.8% (5-mFI\u0026thinsp;=\u0026thinsp;1) vs. 37.1% (5-mFI\u0026thinsp;=\u0026thinsp;2) vs. 42.9% (5-mFI\u0026thinsp;\u0026ge;\u0026thinsp;3), \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001] and severe complications (4.8% vs. 10.2% vs. 13.2% vs. 15.1%, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001). Furthermore, Patients with severe frailty had significantly longer postoperative hospital stay (10d vs. 11d vs. 12d vs. 15d, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001).\u003c/p\u003e\n\u003ch3\u003eAssociation between different frailty status and prognosis of elderly GC patients\u003c/h3\u003e\n\u003cp\u003eIn the training set, the median follow-up was 57.0 months (range, 2-119 months), during which 291 patients occurred cancer-specific deaths and 146 occurred non-cancer related death. Significantly lower 5-year OS rate was observed in patients with severe frailty than in the other three groups [63.3% (5-mFI\u0026thinsp;=\u0026thinsp;0) vs. 43.1% (5-mFI\u0026thinsp;=\u0026thinsp;1) vs. 41.6% (5-mFI\u0026thinsp;=\u0026thinsp;2) vs. 31.1% (5-mFI\u0026thinsp;\u0026ge;\u0026thinsp;3), \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001]. Similar significant results were obtained for 5-year CSS rates of elderly patients with GC (Fig.\u0026nbsp;3B). The cumulative 5-year incidence of CSS in patients with severe frailty was 40.6%, indicating significantly poorer outcomes compared to those with moderate frailty(54.4%), pre-frailty (53.4%), and fit (73.4%). Moreover, statistically significant differences were observed in the 5-year cumulative incidence of competing risk events and frailty status (13.7% vs. 19.1% vs. 23.3% vs. 23.4%, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eFactors associated with CSS of elderly GC patients\u003c/h2\u003e\u003cp\u003eThe univariate and multivariate competing-risks regression for CSS of elderly GC patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The univariate competing risk analysis showed that age, 5-mFI, tumor size, differentiation grade, T stage, N stage, TNM stage, lymphovascular invasion, perineural invasion, and adjuvant therapy were identified as risk factors for CSS of elderly GC patients (all \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05).Further multivariate analysis revealed that age\u0026thinsp;\u0026ge;\u0026thinsp;80 years [subdistribution hazard ratio (SHR)\u0026thinsp;=\u0026thinsp;1.62, 95% confidence interval (CI) :1.08\u0026ndash;2.43, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02], 5-mFI\u0026thinsp;\u0026ge;\u0026thinsp;3 (SHR\u0026thinsp;=\u0026thinsp;2.84, 95% CI: 2.04\u0026ndash;3.96, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001), undifferentiated state (SHR\u0026thinsp;=\u0026thinsp;3.29, 95% CI: 1.53\u0026ndash;7.09, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001), T4 stage (SHR\u0026thinsp;=\u0026thinsp;1.73, 95% CI: 1.05\u0026ndash;2.86, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), N3 stage (SHR\u0026thinsp;=\u0026thinsp;4.06, 95% CI: 2.68\u0026ndash;6.51, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001), and perineural invasion (SHR\u0026thinsp;=\u0026thinsp;1.45, 95% CI: 1.05\u0026ndash;2.00, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) were independent risk factors of CSS in elderly GC patients.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate competing risk analyses of risk factors for cancer specific survival in elderly gastric cancer patients after curative surgery\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c3\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e70\u0026ndash;74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e75\u0026ndash;79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.38 (1.08\u0026ndash;1.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.29 (1.00-1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e80+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.48 (1.04\u0026ndash;2.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.62 (1.08\u0026ndash;2.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.88 (0.69\u0026ndash;1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e18.5ཞ24.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.30 (0.86\u0026ndash;1.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e25.0ཞ29.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.05 (0.66\u0026ndash;1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.72 (0.22\u0026ndash;2.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eASA-PS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eI or II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eIII or IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.09 (0.86\u0026ndash;1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e5-item modified frailty index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.75 (1.19\u0026ndash;2.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.65 (1.18\u0026ndash;2.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.81 (1.52\u0026ndash;2.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.68 (1.18\u0026ndash;2.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.00 (1.52\u0026ndash;2.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.84 (2.04\u0026ndash;3.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eHistory of abdominal surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.95 (0.68\u0026ndash;1.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eTumor location\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUpper third\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMiddle third\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.03 (0.76\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLower third\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.09 (0.83\u0026ndash;1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eNeoadjuvant therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49 (0.19\u0026ndash;1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eSurgical approach\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMinimally invasive surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eOpen surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.06 (0.74\u0026ndash;1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eOperative procedure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDistal gastrectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eTotal gastrectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.09 (0.86\u0026ndash;1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eSubtotal gastrectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.94 (0.62\u0026ndash;1.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eExtent of lymphadenectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eD1+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eD2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.85 (0.65\u0026ndash;1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eType of reconstruction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBillroth I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBillroth II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.20 (0.81\u0026ndash;1.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRoux-en-Y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.33 (0.97\u0026ndash;1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eTumor size, mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.30 (1.02\u0026ndash;1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.20 (0.93\u0026ndash;1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eDifferentiation grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eWell differentiated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eModerately differentiated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.97 (1.69\u0026ndash;5.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.02 (1.14\u0026ndash;3.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003ePoorly differentiated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.61 (2.66\u0026ndash;7.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.23 (1.25\u0026ndash;3.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUndifferentiated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.61 (3.32\u0026ndash;13.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.29 (1.53\u0026ndash;7.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003ePathological T stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.61 (1.06\u0026ndash;2.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.14 (0.74\u0026ndash;1.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.52 (2.48\u0026ndash;5.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.72 (1.12\u0026ndash;2.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.35 (3.69\u0026ndash;7.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.73 (1.05\u0026ndash;2.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003ePathological N stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eN0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eN1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.42 (1.76\u0026ndash;3.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.01 (1.14\u0026ndash;3.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eN2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.63 (2.62\u0026ndash;5.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.43 (1.25\u0026ndash;3.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eN3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.75 (4.97\u0026ndash;9.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.06 (2.68\u0026ndash;6.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003ePathological TNM stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.37 (2.36\u0026ndash;4.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.68 (4.78\u0026ndash;9.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eLymphovascular invasion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePresent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAbsent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.74 (1.37\u0026ndash;2.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.05 (0.78\u0026ndash;1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003ePerineural invasion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePresent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAbsent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.78 (2.17\u0026ndash;3.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.45 (1.05-2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eAdjuvant therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.86 (1.48\u0026ndash;2.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.85 (0.64\u0026ndash;1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eSHR: subdistribution hazard ration; ASA-PS: American Society of Anesthesiology Physical Status Classification.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eConstruction and validation of the CSS prediction models\u003c/h2\u003e\u003cp\u003eThe prediction model (Model 1) was constructed based on the six predictors from the multivariate competing risk analysis and was shown in Fig.\u0026nbsp;4. To further illustrate the importance of frailty assessment in predicting the prognosis of elderly GC patients, we constructed two additional models as controls. Model 2 contained features that were significant in the multivariate analysis except for frailty status assessment. Model 3 contained only the TNM staging system.\u003c/p\u003e\u003cp\u003eThe AUCs of 1-, 3-, and 5-year CSS of Model 1 were 0.768 (95% CI: 0.719\u0026ndash;0.816), 0.823 (95% CI: 0.789\u0026ndash;0.856), and 0.838 (95% CI: 0.804\u0026ndash;0.871), which was statistically better than Model 2 and Model 3 (Supplementary Fig.\u0026nbsp;1). Likewise, consistent findings were also presented in the internal validation set and external validation set (Supplementary Figs.\u0026nbsp;2\u0026ndash;3). The 5-year C-index of Model 1 was 0.771, 0.784, and 0.784 in the training, internal validation, and external validation cohort, respectively, which was superior to other models (Supplementary Figs.\u0026nbsp;4\u0026ndash;6). It manifested that Model 1 had a better prediction ability. In addition, DCA also showed that the clinical utility of Model 1 was higher than that of Model 2 and Model 3 in predicting CSS at 1-, 3- and 5 years in the training set, internal validation set and external validation set (Supplementary Figs.\u0026nbsp;7\u0026ndash;9).\u003c/p\u003e\u003cp\u003eTo further verify the discriminative ability of the Model 1, we performed risk classification based on the optimal cut-off value (142 points), which was established by X-tile 3.6.1 software in the training set (Supplementary Fig.\u0026nbsp;10). There was a significant prognostic difference in 5-year CSS rates between the high- and low-risk groups in the training cohort (23.3% vs. 73.8%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;5). Similar results were validated in the internal and combined external validation sets (Supplementary Figs.\u0026nbsp;11\u0026ndash;12).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eGlobally, the average age of GC patients and the proportion of elderly GC patients are increasing. Accurate assessment of prognosis is critical to guide treatment decision making and clinical management of elderly patients with GC. The prognostic variability observed among elderly patients with GC is primarily evidenced by the marked differences in outcomes among patients with comparable AJCC stages\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. This heterogeneity is largely attributable to the discrepancy between individuals' physiological and chronological ages. An assessment of frailty status effectively captures and distinguishes this heterogeneity\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In the current study, we found that severe frailty status was significantly associated with elevated risk of cancer-specific mortality and non-malignancy mortality in elderly GC. Furthermore, to the best of our knowledge, this is the first study to establish a CSS prediction model based on multidimensional frailty assessment for elderly GC patients. The prediction model demonstrated excellent discrimination and calibration in predicting CSS compared with other models, which have been validated in an independent external cohort.\u003c/p\u003e\u003cp\u003eCurrently, no consensus exists regarding standardized criteria for defining and assessing frailty in clinical practice. Multiple frailty assessment instruments have been applied for elderly GC patients, as documented in existing literature, yet their validation status remains variable\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Existing frailty evaluations in geriatric populations demonstrate significant heterogeneity in reported outcomes, primarily attributable to two factors: the use of divergent assessment scales and their implementation in diverse population groups. An optimal frailty measurement tool should strategically balance clinical practicality with measurement precision. The frailty assessment instrument employed in this study was derived from the frailty index (FI), serving as a prototypical example of the cumulative deficit model. Although the FI has demonstrated strong predictive validity for postoperative complications and long-term prognosis, its clinical utility in routine practice is substantially limited by the requirement to evaluate over 70 distinct clinical parameters. This operational complexity creates a discrepancy between research utility and practical application in clinical settings. Building upon this methodological evolution, Saxton et al. refined the FI to an 11-item metric through analysis of National Surgical Quality Improvement Program datasets, preserving most of the original predictive efficacy\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Subsequent analysis by Subramaniam et al. established statistical equipoise between the 5-mFI and 11-item modified frailty index (11-mFI) in surgical cohorts, with Pearson's correlation coefficients exceeding 0.90 between the 5-mFI and 11-mFI \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Based on these validation studies, the streamlined 5-mFI was selected as the preoperative frailty assessment framework for geriatric GC patients in this study.\u003c/p\u003e\u003cp\u003eBeyond confirming the robust association between frailty and cancer-specific mortality, our study revealed that frailty independently correlated with elevated risk of non-cancer-related mortality. Our investigation observed that 23.2% (297/1278) of elderly GC patients experienced competing mortality from non-oncological causes. The causes of non-cancer death in this study included accidents, cardiovascular and cerebrovascular diseases, pneumonia, COPD and other non-cancer causes. Non-cancer mortality acts as competing events that compromise the validity of conventional survival analysis in quantifying cancer-specific mortality risk. Under competing risks scenarios, non-cancer mortality creates informative censoring that violates the independent censoring assumption required by Kaplan-Meier estimators, necessitating the use of cumulative incidence functions for unbiased estimation\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In order to more accurately evaluate the risk factors affecting the prognosis of elderly patients with GC, we chose the competing risk model as the analysis method in this study.\u003c/p\u003e\u003cp\u003eIn multivariable-adjusted competing risks analysis, severe frailty was confirmed as an independent risk factor for cancer-specific mortality (SHR\u0026thinsp;=\u0026thinsp;2.84, 95% CI: 2.04\u0026ndash;3.96) in elderly patients with GC. The underlying mechanisms that may account for the association between frailty and poor prognosis may be attributed to the following reasons. Previous studies have confirmed that preoperative frailty syndrome is significantly associated with severe postoperative complications in elderly patients, corroborating the findings of our current investigation\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The comorbidities and poor physical function status involved in 5-mFI increase the risk of postoperative morbidity in elderly patients with GC. This pathophysiological cascade may induce alterations in immune homeostasis, potentially creating a permissive microenvironment for residual micrometastatic disease\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In addition, emerging evidence suggests that frailty in cancer patients correlates with chronic low-grade systemic inflammation and compromised immune function, thereby potentially exacerbating tumor invasiveness and disease progression\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Recent research advances document that frailty is controlled by genetic pathways and biochemical processes conserved in evolution\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Potential hallmarks, including genomic instability, epigenetic alterations, loss of proteostasis, dysregulated nutrient sensing, and mitochondrial dysfunction, are likely interconnected and have varying contribution weights across different frailty phenotypes\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. These epigenetic changes may also contribute to the adverse biological response of patients to cancer treatment and lead to poor prognosis\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. However, the molecular and cellular mechanisms involved in frailty are still ambiguous. Further in-depth studies are required to further explore the detailed underlying mechanisms.\u003c/p\u003e\u003cp\u003eIndividualized survival prediction constitutes the cornerstone of precision oncology frameworks. Having established frailty-prognosis associations in elderly GC patients, we subsequently developed a CSS nomogram integrating frailty metrics. Comparative analyses demonstrated incremental predictive value of frailty assessment: the model 1 outperformed both the clinicopathological model (Model 2; ΔAUC\u0026thinsp;=\u0026thinsp;0.039) and the TNM stage-based model (model 3; ΔAUC\u0026thinsp;=\u0026thinsp;0.097). The generalizability and extrapolation of prediction models have been the key constraints for further application. The clinicopathologic features and results of frailty assessment involved in the prediction model are easily accessible in clinical practice. Moreover, the validity of the prediction model was further verified by risk stratification in the internal and external validation sets, indicating that the model was still robust in different cohorts. Thus, this model will help clinicians to quickly and accurately assess the CSS of elderly patients with GC, and at the same time identify high-risk groups and effectively develop corresponding personalization of adjuvant therapies, surveillance protocols, and follow-up intervals.\u003c/p\u003e\u003cp\u003eIn addition to the above findings, univariate competing risk analysis of this study showed that postoperative adjuvant chemotherapy tended to improve CSS of GC patients, but the difference was not statistically significant after adjusting for confounders by multivariate analysis. In clinical practice, the proportion of elderly patients with GC receiving adjuvant chemotherapy is significantly reduced due to concerns about the toxic and side effects of chemotherapy\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Thus, whether postoperative adjuvant chemotherapy can bring survival benefits to elderly patients with GC has always been debated. In the sub-group analysis of the prospective randomized controlled trial (RCT) by Sakuramoto et al., the results showed that patients older than 60 years of age who received postoperative adjuvant S-1 therapy did not have a significant survival benefit compared with patients who underwent surgery alone\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In the phase III CLASSIC trial, adjuvant CAPOX significantly improved 5-year disease-free survival (HR\u0026thinsp;=\u0026thinsp;0.51, 95% CI: 0.34\u0026ndash;0.78) but not 5-year OS (HR\u0026thinsp;=\u0026thinsp;0.70, 95% CI: 0.44\u0026ndash;1.12) in GC patients older than 65 years\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. It should be noted that the above results were derived from comparisons in subgroup analysis stratified by age. At present, little other evidence is available directly assessing the effects of adjuvant therapy in the older GC patients. Only one multicenter RCT study in Japan is comparing the efficacy of modified S-1 adjuvant chemotherapy in frail elderly patients with pathological stage II/III GC\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. We look forward to more relevant research focusing on elderly patients with GC in the future.\u003c/p\u003e\u003cp\u003eThis multicenter retrospective cohort study has several limitations. First, the retrospective design introduces an inherent risk of selection bias that may influence case ascertainment and data collection. Secondly, the generalizability of our findings may be constrained by the single-center origin of the external validation cohort, which might not fully represent the heterogeneous clinicopathological profiles observed in elderly GC populations across different geographic regions. Thus, further prospective, multi-regional studies are warranted to confirm our observations. Lastly, frailty state demonstrates temporal variability influenced by both disease progression and physiological aging processes. Our current investigation did not longitudinally assess the prognostic implications of these evolving frailty trajectories on elderly GC patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSeverely debilitating condition was associated with worse short-term and long-term clinical outcomes of elderly GC patients after surgical resection. The implementation of a competing risk prediction model incorporating comprehensive frailty metrics significantly enhances the predictive precision for CSS in geriatric GC populations, providing superior risk stratification compared to conventional prognostic systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding information:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis project was supported by Yulin city science and Technology Program project (2024-SF-039).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003eThe raw data and supporting materials used the current study is available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eDisclosures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F.\u003cem\u003e et al\u003c/em\u003e. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCA: a cancer journal for clinicians\u003c/em\u003e. \u003cstrong\u003e74 \u003c/strong\u003e(3), 229-263 (2024).\u003c/li\u003e\n\u003cli\u003eJoharatnam-Hogan N.\u003cem\u003e et al\u003c/em\u003e. Challenges in the treatment of gastric cancer in the older patient. \u003cem\u003eCancer Treat Rev\u003c/em\u003e. \u003cstrong\u003e85\u003c/strong\u003e, 101980 (2020).\u003c/li\u003e\n\u003cli\u003eMatsushima R.\u003cem\u003e et al\u003c/em\u003e. An increasing trend of gastric cancer deaths and inadequate preventive measures in elderly adults. \u003cem\u003eHelicobacter\u003c/em\u003e. \u003cstrong\u003e28 \u003c/strong\u003e(4), e12988 (2023).\u003c/li\u003e\n\u003cli\u003eAjani J.A.\u003cem\u003e et al\u003c/em\u003e. Gastric Cancer, Version 2.2022, NCCN Clinical Practice Guidelines in Oncology. \u003cem\u003eJ Natl Compr Canc Netw\u003c/em\u003e. \u003cstrong\u003e20 \u003c/strong\u003e(2), 167-192 (2022).\u003c/li\u003e\n\u003cli\u003eWang F.H.\u003cem\u003e et al\u003c/em\u003e. The Chinese Society of Clinical Oncology (CSCO): Clinical guidelines for the diagnosis and treatment of gastric cancer, 2023. \u003cem\u003eCancer communications (London, England)\u003c/em\u003e. \u003cstrong\u003e44 \u003c/strong\u003e(1), 127-172 (2024).\u003c/li\u003e\n\u003cli\u003eDent E.\u003cem\u003e et al\u003c/em\u003e. Management of frailty: opportunities, challenges, and future directions. \u003cem\u003eLancet (London, England)\u003c/em\u003e. \u003cstrong\u003e394 \u003c/strong\u003e(10206), 1376-1386 (2019).\u003c/li\u003e\n\u003cli\u003eLiu T.\u003cem\u003e et al\u003c/em\u003e. Frailty and prognosis in lung cancer: systematic review and meta-analysis. \u003cem\u003eBMJ supportive \u0026amp; palliative care\u003c/em\u003e. \u003cstrong\u003e14 \u003c/strong\u003e(2), 121-131 (2024).\u003c/li\u003e\n\u003cli\u003eYamada S.\u003cem\u003e et al\u003c/em\u003e. Significance of Frailty in Prognosis After Hepatectomy for Elderly Patients with Hepatocellular Carcinoma. \u003cem\u003eAnn Surg Oncol\u003c/em\u003e. \u003cstrong\u003e28 \u003c/strong\u003e(1), 439-446 (2021).\u003c/li\u003e\n\u003cli\u003eDeol E.S.\u003cem\u003e et al\u003c/em\u003e. Frailty and survival among veterans treated with abiraterone or enzalutamide for metastatic castration-resistant prostate cancer. \u003cem\u003eJournal of geriatric oncology\u003c/em\u003e. \u003cstrong\u003e14 \u003c/strong\u003e(5), 101520 (2023).\u003c/li\u003e\n\u003cli\u003eWildiers H.\u003cem\u003e et al\u003c/em\u003e. International Society of Geriatric Oncology consensus on geriatric assessment in older patients with cancer. \u003cem\u003eJ Clin Oncol\u003c/em\u003e. \u003cstrong\u003e32 \u003c/strong\u003e(24), 2595-2603 (2014).\u003c/li\u003e\n\u003cli\u003eMohile S.G.\u003cem\u003e et al\u003c/em\u003e. Practical Assessment and Management of Vulnerabilities in Older Patients Receiving Chemotherapy: ASCO Guideline for Geriatric Oncology Summary. \u003cem\u003eJ Oncol Pract\u003c/em\u003e. \u003cstrong\u003e14 \u003c/strong\u003e(7), 442-446 (2018).\u003c/li\u003e\n\u003cli\u003ePanayi A.C.\u003cem\u003e et al\u003c/em\u003e. The modified 5-item frailty index is a predictor of perioperative risk in head and neck microvascular reconstruction: An analysis of 3795 cases. \u003cem\u003eAm J Otolaryngol\u003c/em\u003e. \u003cstrong\u003e42 \u003c/strong\u003e(6), 103121 (2021).\u003c/li\u003e\n\u003cli\u003eGuiab K.\u003cem\u003e et al\u003c/em\u003e. Can the 5-item Modified Frailty Index Predict Outcomes in Geriatric Trauma? A National Database Study. \u003cem\u003eWorld journal of surgery\u003c/em\u003e. \u003cstrong\u003e46 \u003c/strong\u003e(10), 2328-2334 (2022).\u003c/li\u003e\n\u003cli\u003eCamp R.L.\u003cem\u003e et al\u003c/em\u003e. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. \u003cem\u003eClin Cancer Res\u003c/em\u003e. \u003cstrong\u003e10 \u003c/strong\u003e(21), 7252-7259 (2004).\u003c/li\u003e\n\u003cli\u003eSaif M.W.\u003cem\u003e et al\u003c/em\u003e. Gastric cancer in the elderly: an overview. \u003cem\u003eEuropean journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology\u003c/em\u003e. \u003cstrong\u003e36 \u003c/strong\u003e(8), 709-717 (2010).\u003c/li\u003e\n\u003cli\u003eRohrmann S. Epidemiology of Frailty in Older People. \u003cem\u003eAdv Exp Med Biol\u003c/em\u003e. \u003cstrong\u003e1216\u003c/strong\u003e, 21-27 (2020).\u003c/li\u003e\n\u003cli\u003eLiang H.\u003cem\u003e et al\u003c/em\u003e. Frailty and long-term survival of patients with gastric cancer: a meta-analysis. \u003cem\u003eFront Oncol\u003c/em\u003e. \u003cstrong\u003e13\u003c/strong\u003e, 1239781 (2023).\u003c/li\u003e\n\u003cli\u003eSaxton A.\u003cem\u003e et al\u003c/em\u003e. Preoperative frailty and quality of life as predictors of postoperative complications. \u003cem\u003eAnnals of surgery\u003c/em\u003e. \u003cstrong\u003e253 \u003c/strong\u003e(6), 1223-1229 (2011).\u003c/li\u003e\n\u003cli\u003eSubramaniam S.\u003cem\u003e et al\u003c/em\u003e. New 5-Factor Modified Frailty Index Using American College of Surgeons NSQIP Data. \u003cem\u003eJournal of the American College of Surgeons\u003c/em\u003e. \u003cstrong\u003e226 \u003c/strong\u003e(2), 173-181.e178 (2018).\u003c/li\u003e\n\u003cli\u003eAustin P.C.\u003cem\u003e et al\u003c/em\u003e. Practical recommendations for reporting Fine-Gray model analyses for competing risk data. \u003cem\u003eStat Med\u003c/em\u003e. \u003cstrong\u003e36 \u003c/strong\u003e(27), 4391-4400 (2017).\u003c/li\u003e\n\u003cli\u003eKim G.\u003cem\u003e et al\u003c/em\u003e. Frailty in Elderly Gastric Cancer Patients Undergoing Gastrectomy. \u003cem\u003eDig Surg\u003c/em\u003e. 1-7 (2020).\u003c/li\u003e\n\u003cli\u003eKouzu K.\u003cem\u003e et al\u003c/em\u003e. Preoperative fall risk assessment score as a prognostic factor in gastric cancer patients after gastrectomy. \u003cem\u003eJpn J Clin Oncol\u003c/em\u003e. \u003cstrong\u003e51 \u003c/strong\u003e(4), 569-576 (2021).\u003c/li\u003e\n\u003cli\u003eZhang X.\u003cem\u003e et al\u003c/em\u003e. The Biology of Aging and Cancer: Frailty, Inflammation, and Immunity. \u003cem\u003eCancer J\u003c/em\u003e. \u003cstrong\u003e23 \u003c/strong\u003e(4), 201-205 (2017).\u003c/li\u003e\n\u003cli\u003eFerrucci L.\u003cem\u003e et al\u003c/em\u003e. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. \u003cem\u003eNat Rev Cardiol\u003c/em\u003e. \u003cstrong\u003e15 \u003c/strong\u003e(9), 505-522 (2018).\u003c/li\u003e\n\u003cli\u003eCsete M.E. Basic Science of Frailty-Biological Mechanisms of Age-Related Sarcopenia. \u003cem\u003eAnesth Analg\u003c/em\u003e. \u003cstrong\u003e132 \u003c/strong\u003e(2), 293-304 (2021).\u003c/li\u003e\n\u003cli\u003eSalvioli S.\u003cem\u003e et al\u003c/em\u003e. Biomarkers of aging in frailty and age-associated disorders: State of the art and future perspective. \u003cem\u003eAgeing research reviews\u003c/em\u003e. \u003cstrong\u003e91\u003c/strong\u003e, 102044 (2023).\u003c/li\u003e\n\u003cli\u003eMaccormick R.E. Possible acceleration of aging by adjuvant chemotherapy: a cause of early onset frailty? \u003cem\u003eMed Hypotheses\u003c/em\u003e. \u003cstrong\u003e67 \u003c/strong\u003e(2), 212-215 (2006).\u003c/li\u003e\n\u003cli\u003eMatthaiou C.\u003cem\u003e et al\u003c/em\u003e. Management of gastric cancer in older adults. \u003cem\u003eJournal of geriatric oncology\u003c/em\u003e. \u003cstrong\u003e8 \u003c/strong\u003e(6), 403-406 (2017).\u003c/li\u003e\n\u003cli\u003eSakuramoto S.\u003cem\u003e et al\u003c/em\u003e. Adjuvant chemotherapy for gastric cancer with S-1, an oral fluoropyrimidine. \u003cem\u003eN Engl J Med\u003c/em\u003e. \u003cstrong\u003e357 \u003c/strong\u003e(18), 1810-1820 (2007).\u003c/li\u003e\n\u003cli\u003eNoh S.H.\u003cem\u003e et al\u003c/em\u003e. Adjuvant capecitabine plus oxaliplatin for gastric cancer after D2 gastrectomy (CLASSIC): 5-year follow-up of an open-label, randomised phase 3 trial. \u003cem\u003eLancet Oncol\u003c/em\u003e. \u003cstrong\u003e15 \u003c/strong\u003e(12), 1389-1396 (2014).\u003c/li\u003e\n\u003cli\u003eMizutani T.\u003cem\u003e et al\u003c/em\u003e. A phase III trial to confirm modified S-1 adjuvant chemotherapy for pathological stage II/III vulnerable elderly gastric cancer patients who underwent gastric resection (JCOG1507, BIRDIE). \u003cem\u003eJpn J Clin Oncol\u003c/em\u003e. \u003cstrong\u003e48 \u003c/strong\u003e(12), 1101-1104 (2018).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6618864/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6618864/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFrailty serves as a critical determinant of clinical outcomes in elderly cancer patients. Nevertheless, there remains a paucity of \u0026zwnj;rigorously validated prognostic tools\u0026zwnj; that account for frailty heterogeneity in older adults with gastric cancer (GC). This multicenter investigation enrolled 1,278 consecutive GC patients aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years treated between January 2015 and December 2022 across two geographically distinct Chinese cohorts: the Xi'an Cohort (n\u0026thinsp;=\u0026thinsp;934) and Yulin Cohort (n\u0026thinsp;=\u0026thinsp;344). \u0026zwnj;Using stratified randomization\u0026zwnj;, the Xi'an cohort was partitioned into training (n\u0026thinsp;=\u0026thinsp;653) and internal validation (n\u0026thinsp;=\u0026thinsp;281) sets at a 7:3 ratio, while the Yulin cohort provided \u0026zwnj;external validation\u0026zwnj;. Frailty status was systematically assessed using the validated 5-Item Modified Frailty Index. \u0026zwnj;Competing risks regression analysis\u0026zwnj; identified significant predictors of cancer-specific survival (CSS) in the training cohort. Based on the risk factors, predictive models to predict patients\u0026rsquo; 1-, 3-, and 5-year CSS were constructed. \u0026zwnj;Model performance\u0026zwnj; was rigorously evaluated through multiple metrics: the area under the receiver operating curve (AUC), Harrell's concordance index (C-index), calibration plots, and decision curve analysis (DCA). Multivariable analysis revealed \u0026zwnj;severe frailty\u0026zwnj; as an independent CSS predictor (subdistribution hazard ratio\u0026thinsp;=\u0026thinsp;2.84, 95% CI: 2.04\u0026ndash;3.96; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). \u0026zwnj;The frailty-incorporated competing risk model demonstrated superior predictive performance\u0026zwnj;, achieving AUC values of 0.838 (training), 0.846 (internal validation), and 0.841 (external validation) - \u0026zwnj;surpassing conventional prognostic models\u0026zwnj;. Concordantly, the model attained the highest C-indices across all validation sets (training: 0.771; internal: 0.784; external: 0.784). Calibration plots showed \u0026zwnj;excellent agreement\u0026zwnj; between predicted and observed outcomes, while DCA confirmed \u0026zwnj;enhanced clinical utility\u0026zwnj; across relevant risk thresholds. These findings establish that \u0026zwnj;frailty-adapted prognostic modeling\u0026zwnj; significantly improves survival prediction accuracy in elderly GC patients. Our results underscore the imperative of \u0026zwnj;comprehensive frailty assessment\u0026zwnj; in clinical decision-making and risk stratification for this vulnerable population.\u003c/p\u003e","manuscriptTitle":"Development and validation of a frailty assessment-based competing risk model for predicting cancer-specific survival in elderly patients with gastric cancer: a dual-center cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 09:54:19","doi":"10.21203/rs.3.rs-6618864/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-09-01T05:39:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-11T07:28:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-21T09:57:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-21T05:54:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-08T09:02:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f9adec71-939f-42b5-ad3c-ed86117e2f25","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":54155285,"name":"Biological sciences/Cancer/Gastrointestinal cancer/Gastric cancer"},{"id":54155286,"name":"Biological sciences/Cancer/Cancer screening"}],"tags":[],"updatedAt":"2025-09-09T09:54:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 09:54:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6618864","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6618864","identity":"rs-6618864","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}
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