Comparison of PSOX, FOLFOX and SOX Plus Sintilimab in advanced gastric cancer: a LASSO- Cox prognostic modeling study

Comparison of PSOX, FOLFOX and SOX Plus Sintilimab in advanced gastric cancer: a LASSO- Cox prognostic modeling 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 Research Article Comparison of PSOX, FOLFOX and SOX Plus Sintilimab in advanced gastric cancer: a LASSO- Cox prognostic modeling study Shao-Wei Jiang, Chen-Guang Zhang, Ke-Di Wang, Kun-Peng Shang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9278473/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 Objective: This study aimed to compare the efficacy and safety of three neoadjuvant treatment regimens—nab-paclitaxel plus oxaliplatin and S-1 (PSOX), oxaliplatin plus leucovorin and fluorouracil (FOLFOX), and S-1 combined with sintilimab and oxaliplatin (SOX+XDL)—in patients with advanced gastric cancer (GC). Additionally, independent prognostic factors associated with progression-free survival (PFS) were identified, and a predictive model was developed to enable individualized risk stratification and prognostic assessment. Methods: This retrospective study included 298 patients with advanced GC who met the inclusion and exclusion criteria. Patients were randomly divided into a training set and a validation set at a 7:3 ratio using a fixed random seed. In the training set, least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation was used to select variables based on the λ.1se criterion. Variables with non-zero coefficients were entered into a multivariable Cox proportional hazards model to identify independent factors associated with PFS, with HRs and 95% CIs calculated. The model was developed in the training set and validated in the validation set. Short-term efficacy, survival outcomes, and adverse events were compared among the three groups. Model performance was evaluated using receiver operating characteristic (ROC) curves and calibration plots. Results: LASSO regression identified five variables with non-zero coefficients, including tumor differentiation, N stage, TNM stage, RECIST 1.1 response, and tumor regression grade (TRG). Among these, TNM stage IIIC showed the largest coefficient, indicating the strongest impact on prognosis. These variables were subsequently included in a multivariable Cox proportional hazards model. The results demonstrated that poor differentiation (HR = 1.86, 95% CI: 1.19–2.91, P = 0.006), lymph node metastasis (HR = 1.69, 95% CI: 1.11–2.57, P = 0.013), and advanced clinical stage (cTNM stage IIIC; HR = 3.94, 95% CI: 2.47–6.28, P < 0.001) were independent risk factors for PFS in patients with GC. In contrast, a favorable response based on RECIST 1.1 (HR = 0.65, 95% CI: 0.46–0.92, P = 0.016) and a lower TRG grade (HR = 0.56, 95% CI: 0.39–0.82, P = 0.003) were identified as protective factors. Conclusion: This study demonstrated that the SOX+XDL regimen achieved a higher pathological response rate than PSOX and FOLFOX in patients with advanced GC, and showed superior outcomes in both PFS and OS, with an overall acceptable safety profile. The predictive model constructed based on LASSO and multivariable Cox regression exhibited good discrimination and calibration, and may serve as a useful tool for individualized risk assessment and clinical decision-making. gastric cancer neoadjuvant therapy LASSO Cox regression prognostic model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Gastric cancer (GC) is one of the most common malignancies of the digestive system worldwide, with high incidence and mortality rates [ 1 ]. Despite advances in surgical techniques and perioperative multimodal therapies in recent years, the prognosis of patients with advanced GC remains unsatisfactory, with a high risk of recurrence and metastasis [ 2 , 3 ]. Therefore, optimizing neoadjuvant treatment strategies and achieving precise prognostic assessment have become key focuses of current clinical research [ 4 ]. At present, chemotherapy regimens based on oxaliplatin combined with fluoropyrimidines (such as SOX and FOLFOX) have been widely used in the neoadjuvant treatment of advanced GC [ 5 ]. With the development of immunotherapy, programmed cell death protein 1 (PD-1) inhibitors combined with chemotherapy have emerged as a promising therapeutic approach, with several studies demonstrating improved tumor response rates and survival outcomes [ 6 ]. In addition, nab-paclitaxel-based combination regimens have shown favorable antitumor activity in certain patient populations [ 7 ]. However, direct comparative studies evaluating the efficacy and safety of different neoadjuvant regimens remain limited, and clinical decision-making still lacks high-quality evidence. On the other hand, traditional prognostic evaluation mainly relies on clinical staging and pathological characteristics, which may not fully capture individual heterogeneity among patients [ 8 ]. In recent years, statistical modeling–based prediction models have been increasingly applied in oncological prognostic assessment. The least absolute shrinkage and selection operator (LASSO) regression can effectively identify key variables in high-dimensional data, and its integration with Cox proportional hazards models facilitates the development of robust survival prediction models [ 9 , 10 ]. Nevertheless, for patients with advanced GC undergoing perioperative treatment, predictive models that incorporate both treatment response indicators (RECIST 1.1 and TRG) and conventional clinical features remain to be further explored [ 11 ]. Based on this background, the present study aimed to compare the efficacy and safety of three neoadjuvant regimens—nab-paclitaxel plus oxaliplatin and S-1 (PSOX), oxaliplatin plus leucovorin and fluorouracil (FOLFOX), and S-1 combined with oxaliplatin and sintilimab (SOX + XDL)—in patients with advanced GC. Furthermore, key prognostic factors were identified using LASSO regression and multivariable Cox analysis, and a PFS prediction model was developed to provide evidence for individualized treatment decision-making. Population and methods Patient Information A total of 298 patients with advanced GC treated at the Department of Gastrointestinal Oncology, Qinghai University Affiliated Hospital, between March 2018 and March 2023 were retrospectively enrolled. Among them, 106 patients received SOX + XDL, 150 received PSOX, and 42 received FOLFOX. All patients were randomly divided into a training set (70%) and a validation set (30%) using the sample function in R. The training set was used for model development, while the validation set was used for external validation. All patients received 2–4 cycles of neoadjuvant chemotherapy and 2–4 cycles of adjuvant chemotherapy, for a total of 6–8 cycles, combined with D2 radical gastrectomy. Patients were followed up regularly for 3 years postoperatively or until the occurrence of predefined endpoints. The treatment plan can be found in Supplementary Material S1 . Inclusion and exclusion criteria All patients met the following inclusion criteria: histologically confirmed primary gastric adenocarcinoma diagnosed by imaging and endoscopic biopsy, staged as IIB–IIIC according to the 8th edition of the AJCC TNM staging system of the Union for International Cancer Control (UICC), without other malignancies or distant metastasis, and achieving R0 resection (no residual tumor macroscopically or microscopically); receipt of 2–4 cycles of neoadjuvant chemotherapy and 2–4 cycles of adjuvant chemotherapy (total 6–8 cycles), with all surgical procedures performed at our institution in accordance with NCCN and CSCO guidelines; measurable primary tumor lesions on CT or MRI with postoperative pathological confirmation; and an Eastern Cooperative Oncology Group (ECOG) performance status ≤ 1, with adequate hepatic, renal, hematologic, and cardiopulmonary function to tolerate chemotherapy. Patients were excluded if they had allergies to chemotherapy agents or contraindications to chemotherapy, severe comorbid conditions (such as infectious diseases, gastrointestinal bleeding, pyloric obstruction, or gastrointestinal perforation), a history of other malignancies treated with radiotherapy, chemotherapy, biological therapy, or surgery, or incomplete clinical or imaging data that precluded accurate tumor measurement. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Qinghai University Affiliated Hospital, and written informed consent was obtained from all participants. Evaluation of therapeutic efficacy and adverse reactions Short-term efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST1.1), with complete response (CR) and partial response (PR) defined as responders, and stable disease (SD) and progressive disease (PD) as non-responders. Pathological response was assessed using postoperative specimens based on tumor regression grade (TRG) according to NCCN criteria, with TRG 0–2 classified as responders and TRG 3 as non-responders. All radiological and pathological evaluations were performed by experienced radiologists and pathologists. Treatment efficacy was compared among the three regimens based on RECIST1.1 and TRG assessments. Long-term efficacy was assessed using progression-free survival (PFS) as the primary endpoint, defined as the time from initiation of chemotherapy to tumor recurrence, development of new GC, or death from any cause, with a follow-up period of 3 years. Overall survival (OS) was defined as the time from initiation of chemotherapy to death from any cause or last follow-up within 3 years. Chemotherapy-related adverse events were evaluated according to the Common Terminology Criteria for Adverse Events (CTCAE), version 5.0. Statistical analysis All statistical analyses were performed using R software. Categorical variables were presented as frequencies (percentages) and compared using the Pearson χ² test or Fisher’s exact test, as appropriate. A two-sided P value < 0.05 was considered statistically significant. To develop the PFS prediction model, all patients were randomly divided into a training set (70%) and a validation set (30%) with a fixed random seed to ensure reproducibility. In the training set, LASSO regression with 10-fold cross-validation was applied to select candidate variables, and the optimal penalty parameter (λ) was determined based on the λ.1se criterion to reduce overfitting and enhance model stability; a fixed random seed was also applied during this process. Variables with non-zero coefficients were subsequently entered into a multivariable Cox proportional hazards model to estimate hazard ratios (HRs) and 95% confidence intervals (CIs), thereby identifying independent predictors of PFS. Model performance was evaluated in both the training and validation sets. Discrimination was assessed using receiver operating characteristic (ROC) curves and the area under the curve (AUC), as well as the concordance index (C-index), while calibration was evaluated using calibration plots comparing predicted and observed outcomes. Results Basic characteristics of patients Comparative analysis of baseline characteristics showed no significant differences among the three groups in terms of age, sex, ASA classification, tumor differentiation, clinical T stage, lymph node status, Lauren classification, tumor location, RECIST1.1 response, and pre-treatment tumor diameter (P > 0.05), indicating good baseline comparability. Only tumor regression grade (TRG) differed significantly among the groups (P = 0.048), as shown in Table 1 . Table 1 Basic patient characteristics and short-term efficacy, n (%) Characteristic Overall N = 298 PSOX N = 150 Folfox N = 42 SOX + XDL N = 106 Statistic P-value Age χ² = 0.48 0.788 ≤ 60 163 (54.7%) 84 (56.0%) 21 (50.0%) 58 (54.7%) > 60 135 (45.3%) 66 (44.0%) 21 (50.0%) 48 (45.3%) Sex χ² = 3.25 0.197 Male 235 (78.9%) 121 (80.7%) 36 (85.7%) 78 (73.6%) Female 63 (21.1%) 29 (19.3%) 6 (14.3%) 28 (26.4%) ASA χ² = 1.07 0.587 1 + 2 259 (86.9%) 128 (85.3%) 36 (85.7%) 95 (89.6%) 3 39 (13.1%) 22 (14.7%) 6 (14.3%) 11 (10.4%) Differentiation χ² = 1.31 0.860 Well 128 (43.0%) 65 (43.3%) 16 (38.1%) 47 (44.3%) Moderate 87 (29.2%) 44 (29.3%) 15 (35.7%) 28 (26.4%) Poorly 83 (27.9%) 41 (27.3%) 11 (26.2%) 31 (29.2%) cT stage χ² = 4.58 0.333 2 154 (51.7%) 75 (50.0%) 21 (50.0%) 58 (54.7%) 3 73 (24.5%) 44 (29.3%) 9 (21.4%) 20 (18.9%) 4 71 (23.8%) 31 (20.7%) 12 (28.6%) 28 (26.4%) cN stage χ² = 0.04 0.979 Positive 210 (70.5%) 106 (70.7%) 30 (71.4%) 74 (69.8%) Negative 88 (29.5%) 44 (29.3%) 12 (28.6%) 32 (30.2%) cTNM IIB 152 (51.0%) 67 (44.7%) 25 (59.5%) 60 (56.6%) IIIA 52 (17.4%) 35 (23.3%) 7 (16.7%) 10 (9.4%) IIIB 64 (21.5%) 29 (19.3%) 7 (16.7%) 28 (26.4%) IIIC 30 (10.1%) 19 (12.7%) 3 (7.1%) 8 (7.5%) RECIST1.1 χ² = 3.73 0.155 Effective 183 (61.4%) 91 (60.7%) 21 (50.0%) 71 (67.0%) Ineffective 115 (38.6%) 59 (39.3%) 21 (50.0%) 35 (33.0%) Lauren χ² = 3.47 0.483 Diffuse 101 (33.9%) 54 (36.0%) 12 (28.6%) 35 (33.0%) Mixed 142 (47.7%) 65 (43.3%) 21 (50.0%) 56 (52.8%) Intestinal 55 (18.5%) 31 (20.7%) 9 (21.4%) 15 (14.2%) Location χ² = 0.83 0.934 Upper 69 (23.2%) 32 (21.3%) 10 (23.8%) 27 (25.5%) Middle 120 (40.3%) 61 (40.7%) 18 (42.9%) 41 (38.7%) Lower 109 (36.6%) 57 (38.0%) 14 (33.3%) 38 (35.8%) TRG χ² = 6.08 0.048 Effective 226 (75.8%) 108 (72.0%) 29 (69.0%) 89 (84.0%) Ineffective 72 (24.2%) 42 (28.0%) 13 (31.0%) 17 (16.0%) Tumor diameter χ² = 6.11 0.191 ≤ 2 115 (38.6%) 59 (39.3%) 11 (26.2%) 45 (42.5%) 2–5 60 (20.1%) 34 (22.7%) 7 (16.7%) 19 (17.9%) ≥ 5 123 (41.3%) 57 (38.0%) 24 (57.1%) 42 (39.6%) χ²: Chi-square test Tumor location is classified as upper 1/3, middle 1/3, lower 1/3, diffuse TNM staging according to the American Joint Committee on Cancer. ASA: American Society of Anesthesiologists Score; RECIST: Response Evaluation Criteria in Solid Tumors; TRG: Tumor regression grade; XDL: Sintilimab. Efficacy (1) Short-term Efficacy: As shown in Table 1 , short-term treatment responses among the three groups were evaluated using RECIST1.1 and TRG. Based on RECIST1.1 criteria, the response rates were 60.7% in the PSOX group, 50.0% in the FOLFOX group, and 67.0% in the SOX + XDL group, with no statistically significant difference among the groups (χ² = 3.73, P = 0.155), indicating comparable radiological responses. However, TRG-based assessment revealed a significant difference among the three groups (χ² = 6.08, P = 0.048). The SOX + XDL group demonstrated the highest response rate (84.0%), which was notably higher than that in the PSOX group (72.0%) and the FOLFOX group (69.0%). Overall, although no significant differences were observed in radiological response based on RECIST1.1, the SOX + XDL regimen showed a superior pathological response based on TRG, suggesting a potential advantage in promoting tumor regression. (2) Survival Analysis: Kaplan–Meier analysis was performed to evaluate PFS and OS among the three groups (Fig. 1 ). For PFS, a clear separation of survival curves was observed. The SOX + XDL group consistently demonstrated the highest PFS rate throughout the follow-up period, with its survival curve remaining above those of the PSOX and FOLFOX groups; the PSOX group showed intermediate outcomes, while the FOLFOX group had the lowest PFS. The differences between groups became more pronounced over time, particularly after approximately 18 months. Log-rank testing indicated a statistically significant difference in PFS among the three groups (P = 0.017), suggesting that treatment regimens differed in their ability to delay disease progression. A similar trend was observed for OS. The SOX + XDL group exhibited the most favorable survival outcomes, maintaining consistently higher survival rates than the PSOX and FOLFOX groups throughout follow-up, followed by the PSOX group, with the FOLFOX group showing the poorest survival. The differences further widened over time, especially after 24 months. Log-rank analysis confirmed a significant difference in OS among the three groups (P = 0.002), indicating a substantial impact of treatment strategy on long-term survival. Additionally, risk table and event distribution analyses showed that the SOX + XDL group maintained a higher number of patients at risk and a lower event rate at each time point, further supporting its prognostic advantage. Overall, the SOX + XDL regimen demonstrated superior outcomes in both PFS and OS, followed by PSOX, while FOLFOX was associated with relatively poorer prognosis. Adverse reaction As shown in Table 2 , all three treatment regimens were generally well tolerated, and treatment-related adverse events were manageable. There were no statistically significant differences in the incidence of most adverse events among the three groups (P > 0.05). Specifically, the rates of nausea and vomiting, liver toxicity, peripheral neuropathy, neutropenia, fewer white blood cells, fewer platelets, and anemia were comparable across groups, with no significant differences observed (P > 0.05). However, the incidence of alopecia differed significantly among the three groups (χ² = 17.34, P = 0.002). The PSOX group exhibited a markedly higher rate of alopecia compared with the FOLFOX and SOX + XDL groups, whereas the SOX + XDL group showed a relatively lower incidence. Overall, all three regimens demonstrated acceptable safety profiles, with similar rates of most common adverse events, while the SOX + XDL regimen showed better tolerability in terms of alopecia. Table 2 Side effects associated with three groups treatment, n (%) Variables Total (n = 298) FOLFOX (n = 42) PSOX (n = 150) SOX + XDL (n = 106) P Nausea and vomiting 0.186 0 62 (20.81) 3 (7.14) 36 (24.00) 23 (21.70) 1 178 (59.73) 29 (69.05) 86 (57.33) 63 (59.43) 2 51 (17.11) 8 (19.05) 26 (17.33) 17 (16.04) 3 7 (2.35) 2 (4.76) 2 (1.33) 3 (2.83) Liver toxicity 0.442 0 252 (84.56) 34 (80.95) 125 (83.33) 93 (87.74) 1 41 (13.76) 6 (14.29) 23 (15.33) 12 (11.32) 2 5 (1.68) 2 (4.76) 2 (1.33) 1 (0.94) Alopecia 0.002 0 176 (59.06) 21 (50.00) 78 (52.00) 77 (72.64) 1 105 (35.23) 20 (47.62) 58 (38.67) 27 (25.47) 2 17 (5.70) 1 (2.38) 14 (9.33) 2 (1.89) Peripheral sensory neuropathy 0.912 0 147 (49.33) 23 (54.76) 75 (50.00) 49 (46.23) 1 121 (40.60) 15 (35.71) 61 (40.67) 45 (42.45) 2 25 (8.39) 4 (9.52) 12 (8.00) 9 (8.49) 3 5 (1.68) 0 (0.00) 2 (1.33) 3 (2.83) Fewer neutrophils 0.123 0 213 (71.48) 25 (59.52) 114 (76.00) 74 (69.81) 1 55 (18.46) 12 (28.57) 24 (16.00) 19 (17.92) 2 19 (6.38) 3 (7.14) 5 (3.33) 11 (10.38) 3 10 (3.36) 2 (4.76) 6 (4.00) 2 (1.89) 4 1 (0.34) 0 (0.00) 1 (0.67) 0 (0.00) Fewer white blood cells 0.352 0 192 (64.43) 25 (59.52) 100 (66.67) 67 (63.21) 1 68 (22.82) 11 (26.19) 28 (18.67) 29 (27.36) 2 27 (9.06) 5 (11.90) 13 (8.67) 9 (8.49) 3 7 (2.35) 0 (0.00) 6 (4.00) 1 (0.94) 4 4 (1.34) 1 (2.38) 3 (2.00) 0 (0.00) Fewer platelets 0.106 0 213 (71.48) 29 (69.05) 111 (74.00) 73 (68.87) 1 57 (19.13) 9 (21.43) 25 (16.67) 23 (21.70) 2 23 (7.72) 1 (2.38) 12 (8.00) 10 (9.43) 3 5 (1.68) 3 (7.14) 2 (1.33) 0 (0.00) Anemia 0.429 0 150 (50.34) 21 (50.00) 79 (52.67) 50 (47.17) 1 108 (36.24) 13 (30.95) 54 (36.00) 41 (38.68) 2 26 (8.72) 3 (7.14) 12 (8.00) 11 (10.38) 3 14 (4.70) 5 (11.90) 5 (3.33) 4 (3.77) Variable Selection by LASSO Regression In the training set, LASSO regression was applied to select candidate variables. The λ was determined 10-fold cross-validation, and the final model was selected based on the λ.1se criterion. As λ increased, the regression coefficients of variables gradually shrank toward zero (Fig. 2 ), indicating reduced model complexity. At the optimal λ value, five variables with non-zero coefficients were retained (Fig. 3 ), with detailed coefficients provided in Supplementary Material S1 . These variables included poor differentiation, lymph node metastasis, clinical TNM stage IIIC, RECIST1.1, and TRG. Among them, clinical TNM stage IIIC had the largest coefficient (0.477), suggesting the strongest impact on prognosis. Positive lymph node status (0.099) and poor differentiation (0.061) were also positively associated with increased risk of disease progression. In contrast, RECIST1.1 (− 0.133) and TRG (− 0.101) showed negative coefficients, indicating that better treatment response was associated with a lower risk of progression. Overall, clinical stage and tumor biological characteristics remained key prognostic factors, while treatment response indicators (RECIST1.1 and TRG) also contributed substantially to the predictive model. Cox regression analysis Based on variables selected by LASSO regression, tumor differentiation, lymph node status, clinical TNM stage, RECIST1.1, and TRG were included in the multivariable Cox proportional hazards model (Table 3 ). The results showed that poor differentiation was significantly associated with an increased risk of disease progression (HR = 1.86, 95% CI: 1.19–2.91, P = 0.006), whereas no significant difference was observed between moderate and well differentiation (P = 0.340). Patients with positive lymph node metastasis had a higher risk of progression (HR = 1.69, 95% CI: 1.11–2.57, P = 0.013). Clinical stage was also a significant prognostic factor, with stage IIIB (HR = 2.14, 95% CI: 1.39–3.28, P < 0.001) and stage IIIC (HR = 3.94, 95% CI: 2.47–6.28, P < 0.001) identified as adverse predictors of PFS, while no significant difference was observed between stage IIIA and stage IIB (P = 0.199). Regarding treatment response indicators, patients achieving a favorable response based on RECIST1.1 had a significantly lower risk of disease progression (HR = 0.65, 95% CI: 0.46–0.92, P = 0.016), indicating a protective effect. Similarly, lower TRG grades were associated with improved prognosis (HR = 0.56, 95% CI: 0.39–0.82, P = 0.003). Overall, tumor differentiation, lymph node metastasis, and clinical stage were identified as independent adverse prognostic factors, whereas RECIST1.1 response and TRG served as protective factors, jointly influencing PFS in patients with advanced GC. Table 3 Results of multivariate Cox proportional hazards regression analyses Variables N Event N HR 95% CI P Differentiation Well 61 29 — — Moderate 57 37 1.27 0.78, 2.09 0.340 Poorly 91 75 1.86 1.19, 2.91 0.006 cN stage Negative 59 30 — — Positive 150 111 1.69 1.11, 2.57 0.013 cTNM IIB 102 50 — — IIIA 21 15 1.49 0.81, 2.72 0.199 IIIB 48 42 2.14 1.39, 3.28 < 0.001 IIIC 38 34 3.94 2.47, 6.28 < 0.001 RECIST1.1 Ineffective 81 68 — — Effective 128 73 0.65 0.46, 0.92 0.016 TRG Ineffective 53 46 — — Effective 156 95 0.56 0.39, 0.82 0.003 Note: RECIST: Response Evaluation Criteria in Solid Tumors; TRG: Tumor regression grade Development of a nomogram for progression-free survival Based on the independent prognostic factors identified in the multivariable Cox regression analysis (tumor differentiation, lymph node status, clinical TNM stage, RECIST1.1 response, and TRG), a nomogram was developed to predict PFS in patients with advanced GC (Fig. 4 ). In this model, each variable was assigned a corresponding score, and the total points were calculated by summing the scores across all variables. The total score was then mapped to an individual linear predictor and the corresponding probabilities of 18, 24, and 36-month PFS. The nomogram demonstrated that clinical TNM stage contributed the greatest weight to the model, followed by lymph node status and tumor differentiation, highlighting the critical role of tumor stage in prognosis. Meanwhile, treatment response indicators, including RECIST1.1 and TRG, also contributed substantially, indicating their significant predictive value for patient outcomes. Model validation The discriminatory performance of the model was evaluated using time-dependent ROC curve analysis. ROC curves were plotted in both the training and validation sets to assess the predictive accuracy for PFS at different time points (Fig. 5 ). In the training set, the model demonstrated good discrimination across all time points, with an AUC of 0.843 (95% CI: 0.777–0.909) at 18 months, indicating high short-term predictive accuracy; the AUC was 0.810 (95% CI: 0.750–0.869) at 24 months, remaining at a favorable level; and 0.858 (95% CI: 0.754–0.963) at 36 months, suggesting strong long-term predictive performance. In the validation set, the model showed similarly stable performance, with AUCs of 0.837 (95% CI: 0.740–0.933) and 0.835 (95% CI: 0.747–0.922) at 18 and 24 months, respectively, comparable to those in the training set, indicating good reproducibility and stability. At 36 months, the AUC was 0.748 (95% CI: 0.609–0.887), slightly lower than that in the training set but still within an acceptable range. Overall, the model consistently achieved AUC values above 0.75 in both cohorts, with particularly strong performance at 18 and 24 months, highlighting its accuracy in short- to mid-term PFS prediction. The concordance between the training and validation sets further supports its robustness, generalizability, and potential clinical utility. Calibration curves were plotted to assess the agreement between predicted probabilities and observed outcomes for PFS at 18, 24, and 36 months in both the training and validation sets (Fig. 6 ). In the training set, the model demonstrated good calibration across all time points. At 18 months, the calibration curve closely aligned with the ideal diagonal line, indicating excellent agreement (Brier score: 12.6%). Similarly, good calibration was observed at 24 months (Brier score: 18.0%) and 36 months (Brier score: 14.5%). In the validation set, the model also showed stable calibration performance. The 18-month calibration curve closely approximated the reference line (Brier score: 17.0%), and good agreement was maintained at 24 months (Brier score: 17.7%). Although a slight deviation was observed at 36 months, the overall trend remained consistent with the ideal line (Brier score: 19.3%). Overall, the calibration curves in both the training and validation sets were close to the ideal diagonal, indicating good agreement between predicted and observed outcomes. The relatively low Brier scores across all time points further support the accuracy and stability of the model. DCA was performed to evaluate the clinical utility of the model at 18, 24, and 36 months in both the training and validation sets (Fig. 7 ). In the training set, the model demonstrated a consistently higher net benefit across a wide range of threshold probabilities at all time points. At 18 months, the model curve remained above both the “treat-all” and “treat-none” reference strategies, indicating a clear advantage in clinical decision-making. Similar trends were observed at 24 and 36 months, where the model provided greater net benefit across most threshold probability ranges, suggesting stable clinical applicability over time. In the validation set, the model also showed good clinical usefulness. At 18 and 24 months, the model curve consistently outperformed the reference strategies across a broad range of thresholds, indicating robust performance in an independent dataset. Although slight fluctuations in net benefit were observed at 36 months, the model still generally outperformed the “treat-all” and “treat-none” approaches. Overall, the model demonstrated favorable decision-making benefits in both cohorts, supporting its potential value in guiding clinical risk assessment and individualized treatment strategies. Discussion This single-center, real-world retrospective cohort study included 298 patients with locally advanced, resectable gastric adenocarcinoma (stage IIB–IIIC) who underwent R0 resection and D2 gastrectomy, and compared three treatment strategies: PSOX, FOLFOX, and SOX plus sintilimab (XDL). In terms of short-term efficacy, no statistically significant differences were observed among the three groups based on RECIST1.1 response rates; however, TRG assessment demonstrated a significantly higher response rate in the SOX + XDL group (P = 0.048). Regarding long-term outcomes, Kaplan–Meier analysis indicated that the SOX + XDL group achieved superior PFS and OS compared with the other groups (log-rank: P = 0.017 for PFS and P = 0.002 for OS). Overall safety was acceptable across all regimens, although the incidence of alopecia differed significantly among groups (P = 0.002). For prognostic modeling, LASSO regression was applied for variable selection, followed by construction of a multivariable Cox model incorporating tumor differentiation, lymph node status (clinical N stage), clinical TNM stage, RECIST 1.1 response, and TRG. The resulting nomogram demonstrated favorable predictive performance, with relatively high AUC values for 18, 24, and 36-month PFS in both the training and validation sets; however, the decline in AUC at 36 months in the validation set suggests some uncertainty in long-term extrapolation. Internationally, triplet perioperative chemotherapy regimens represented by FLOT have been established as a standard of care, whereas in East Asia, doublet regimens such as SOX and FOLFOX remain widely used, with evidence supporting their feasibility and efficacy. Notably, some randomized studies have demonstrated the non-inferiority and potential interchangeability of SOX compared with FOLFOX, further supporting the clinical relevance of the present findings [ 12 , 13 ]. In terms of short-term efficacy, no significant differences in response rates were observed among the three groups based on RECIST1.1, whereas the TRG-defined response rate was significantly higher in the SOX + XDL group. This discrepancy is consistent with the well-recognized challenges in evaluating neoadjuvant immunotherapy. On the one hand, the combination of immunotherapy and chemotherapy may result in concurrent tumor cell clearance and inflammatory infiltration, leading to a mismatch between radiological tumor size changes and the actual reduction in tumor burden [ 14 , 15 ]. On the other hand, pathological response indicators (such as pCR, MPR, and TRG) more directly reflect tumor cell eradication and are therefore more sensitive [ 16 ]. Previous studies have also emphasized the need for confirmatory assessment of atypical response patterns associated with immunotherapy, such as pseudoprogression, further highlighting the limitations of RECIST criteria alone in this context. Consistent with prior evidence, the TRG advantage observed in this study aligns with the reported trend that SOX combined with PD-1 inhibitors improves pathological response. For instance, propensity score–matched studies of perioperative SOX plus PD-1 inhibitors have shown enhanced pathological response rates, particularly in subgroups with higher PD-L1 combined positive score (CPS), Epstein–Barr virus (EBV) positivity, or deficient mismatch repair (dMMR) [ 17 , 18 ]. In addition, phase II studies of neoadjuvant XDL combined with chemotherapy (e.g., FLOT or CapeOx backbone) in resectable gastric or gastroesophageal junction (G/GEJ) cancer have demonstrated promising pathological responses with manageable safety profiles, and patients achieving pCR were more likely to exhibit favorable subsequent survival outcomes [ 19 ]. In terms of long-term outcomes, this study demonstrated that the SOX + XDL regimen was associated with superior PFS and OS compared with PSOX and FOLFOX. This finding is directionally consistent with randomized evidence from advanced or unresectable GC, where XDL combined with chemotherapy has been shown to improve survival. In the ORIENT-16 trial, conducted in Chinese patients with unresectable locally advanced, recurrent, or metastatic G/GEJ adenocarcinoma, XDL plus chemotherapy significantly improved overall survival compared with placebo plus chemotherapy [ 18 ]. This observation aligns with multiple recent large-scale clinical studies indicating that PD-1 inhibitors combined with chemotherapy can significantly improve survival outcomes in advanced GC. The underlying mechanism may involve not only the direct cytotoxic effects of chemotherapy but also its ability to induce immunogenic cell death, leading to the release of tumor antigens and enhancement of antitumor immune responses, thereby producing a synergistic effect with immunotherapy [ 21 , 22 ]. Although ORIENT-16 was conducted in the first-line treatment setting for advanced disease rather than in the neoadjuvant setting for resectable tumors, it provides important evidence supporting the clinical synergy between XDL and fluoropyrimidine–platinum-based chemotherapy [ 21 ]. The separation of survival curves observed in the present study further supports the biological plausibility and clinical relevance of this combination strategy. In terms of safety, all three regimens demonstrated acceptable tolerability, with most adverse events being manageable grade 1–2 toxicities. Except for alopecia, the incidence of common adverse events did not differ significantly among the groups. The overall toxicity profiles—including nausea and vomiting, liver function abnormalities, peripheral sensory neuropathy, myelosuppression, and anemia—were comparable across regimens. Notably, neither the oxaliplatin–fluoropyrimidine–based doublet regimens nor the addition of XDL to the SOX backbone resulted in an unacceptable increase in toxicity. The primary difference among groups was a higher incidence of alopecia in the PSOX group, which is consistent with the known pharmacological characteristics of nab-paclitaxel. Importantly, the SOX + XDL regimen did not show a higher overall incidence of adverse events compared with conventional chemotherapy, which is consistent with previous reports. In prior studies, the most common hematologic toxicities included leukopenia, neutropenia, and anemia, while the most frequent non-hematologic toxicities were elevated transaminases, vomiting, and pneumonia [ 23 – 26 ]. These findings are largely consistent with the present study, in which myelosuppression and gastrointestinal reactions were the predominant adverse events and were generally manageable. XDL is a fully human recombinant IgG4 monoclonal antibody targeting PD-1, which blocks the interaction between PD-1 and its ligands PD-L1/PD-L2, thereby restoring T-cell activity. In multiple clinical studies and reviews, XDL is commonly administered at a dose of 200 mg every 3 weeks (Q3W), with relatively linear pharmacokinetics and a half-life of approximately two weeks (with some variation across studies and tumor types), which is consistent with its use in perioperative combination regimens based on a 21-day cycle [ 27 , 28 ]. Caution is warranted when interpreting claims regarding “stronger binding affinity” or “higher receptor occupancy” compared with other PD-1 inhibitors. Several studies have suggested that XDL may exhibit high PD-1 binding capacity based on in vitro affinity, receptor occupancy, or structural epitope analyses. PD-1 is a key inhibitory receptor expressed on T cells, and upon ligand binding, it recruits intracellular phosphatases through its cytoplasmic motifs, thereby attenuating co-stimulatory signaling and suppressing T-cell activation, cytokine production, and effector function [ 29 , 30 ]. Mechanistically, PD-1–mediated inhibition is largely dependent on disruption of the CD28 co-stimulatory pathway; experimental studies have shown that CD28 is preferentially dephosphorylated by PD-1 signaling, indicating that effective PD-1 blockade depends not only on the antibody itself but also on tumor antigen presentation and the co-stimulatory microenvironment. Furthermore, interactions between PD-1 signaling and molecules such as SHP2, as well as its dynamic assembly within the immunological synapse and signaling complexes, constitute key molecular mechanisms underlying immune suppression and T-cell exhaustion [ 31 , 32 ]. These biological mechanisms provide a rationale for the favorable therapeutic effects observed with PD-1 blockade, including XDL, in patients with advanced GC. Limitations Several limitations of this study should be acknowledged. First, as a single-center retrospective analysis, it is inherently subject to potential selection and information biases. Although baseline characteristics were generally comparable among the three groups, residual confounding cannot be fully excluded. Second, the sample sizes across treatment groups were imbalanced, particularly with a relatively small number of patients in the FOLFOX group, which may have affected the stability of statistical analyses and the power of intergroup comparisons. Third, key immunological biomarkers—such as PD-L1 expression, microsatellite instability (MSI) status, tumor mutational burden (TMB), and tumor immune microenvironment-related indicators—were not included. These factors are known to be closely associated with the efficacy of immunotherapy, and their absence may limit mechanistic interpretation of the observed differences in treatment outcomes, as well as the predictive accuracy of the model in immunotherapy-treated populations. In addition, the prediction model was primarily based on clinicopathological variables and treatment response indicators, without incorporating molecular subtyping or multi-omics data, which may constrain its predictive performance and external applicability. Although internal validation was performed using a training and validation set, external validation in an independent cohort is still lacking, and the generalizability of the model requires further evaluation. Finally, the follow-up duration was relatively limited, and some patients had not yet reached endpoint events, which may affect the assessment of long-term survival outcomes. Future studies with multicenter, large-scale, and prospective designs, incorporating immunological biomarkers and molecular characteristics, are warranted to further validate and optimize both therapeutic strategies and predictive models. Conclusion In summary, the present study demonstrates that, compared with PSOX and FOLFOX, the SOX + XDL regimen significantly improves pathological response rates in the neoadjuvant treatment of advanced GC and provides superior PFS and OS, without a significant increase in severe adverse events, indicating a favorable and manageable safety profile. In addition, the predictive model developed based on LASSO regression and multivariable Cox analysis showed good discrimination and calibration, suggesting that integrating clinicopathological features with treatment response indicators can enhance the accuracy of prognostic assessment. These findings support SOX + XDL as a promising perioperative treatment strategy with a balanced profile of efficacy and safety, and provide valuable evidence for individualized treatment decision-making in patients with advanced GC. Abbreviations GC: gastric cancer; PFS: progression-free survival; OS: overall survival; PSOX: nab-paclitaxel plus oxaliplatin and S-1; FOLFOX: oxaliplatin plus leucovorin and fluorouracil; SOX+XDL: S-1 plus oxaliplatin combined with sintilimab; LASSO: least absolute shrinkage and selection operator; TRG: tumor regression grade; RECIST: Response Evaluation Criteria in Solid Tumors; CTCAE: Common Terminology Criteria for Adverse Events; ROC: receiver operating characteristic; AUC: area under the curve; CI: confidence interval; HR: hazard ratio; AJCC: American Joint Committee on Cancer; UICC: Union for International Cancer Control; ECOG: Eastern Cooperative Oncology Group; ASA: American Society of Anesthesiologists. Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the Affiliated Hospital of Qinghai University (Approval No. SL-2022-035). Written informed consent was obtained from all participants prior to inclusion in the study. Consent for publication All authors have read and approved the final manuscript and consented to its publication. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding authors on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by the Qinghai Provincial Science and Technology Program (Grant No. 2023-ZJ-787), entitled “Biological Function and Molecular Mechanisms of ZFP36 in Gastric Cancer”. The funding body had no role in the design of the study; collection, analysis, or interpretation of data; or in writing the manuscript. Authors' contributions S.-W.J. and C.-G.Z. contributed equally to this work. S.-W.J., C.-G.Z. and H.-L.W. conceived and designed the study. K.-D.W. and K.-P.S. collected and organized the clinical data. S.-W.J. and C.-G.Z. performed the statistical analyses and drafted the main manuscript. P.-J.Y. and H.-L.W. supervised the study, interpreted the data, and critically revised the manuscript. All authors reviewed the manuscript and approved the final version. Acknowledgements The authors would like to thank all patients who participated in this study and the staff of the Department of Gastrointestinal Oncology, Qinghai University Affiliated Hospital, for their support in data collection and clinical management. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024 May-Jun;74(3):229-263. doi: 10.3322/caac.21834. Epub 2024 Apr 4. PMID: 38572751. Tung I, Sahu A. The treatment of resectable gastric cancer: a literature review of an evolving landscape. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9278473","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634310577,"identity":"fefe5c6c-db29-4a23-bef7-258e06d7e483","order_by":0,"name":"Shao-Wei Jiang","email":"","orcid":"","institution":"Qinghai University","correspondingAuthor":false,"prefix":"","firstName":"Shao-Wei","middleName":"","lastName":"Jiang","suffix":""},{"id":634310578,"identity":"1db150d0-e99c-4e09-8ae6-762d0a95e437","order_by":1,"name":"Chen-Guang Zhang","email":"","orcid":"","institution":"Qinghai 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peng-Jie","middleName":"","lastName":"Yu","suffix":""},{"id":634310583,"identity":"36217e4e-64df-4536-bd8c-3b7e94da8896","order_by":5,"name":"Huan-Li Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBACfvbm45//VEjI8bM3EKlFsudYGgPPGQtjyZ4DRGoxuJFjxsDbVpG4YUYCsS47kGP2QIJNInGD5OONNxhqbKIJ6mBsOFZuYMAjYbxdOq3YguFYWm4DIS3MjM0bJBIkJGR3zs4xk2BsOExYCxszg4HEAQMJxg03zxCphYeNxUyyIUFCccMNHiK1SPCwJRszHJAABjLQLwnE+MX+/uODjxn/1QGj8vDGGx9qbAhrQQYGEgmkKIdoIVXHKBgFo2AUjAwAAJJbPniWxF5qAAAAAElFTkSuQmCC","orcid":"","institution":"Qinghai University Affiliated Hospital","correspondingAuthor":true,"prefix":"","firstName":"Huan-Li","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-03-31 10:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9278473/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9278473/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108838339,"identity":"87cdab12-71fb-4b8d-a6d6-a0ff7e353c17","added_by":"auto","created_at":"2026-05-09 00:34:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60889,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of progression-free survival and overall survival among the three groups\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9278473/v1/09c812c5692149861df4c2d9.png"},{"id":108838335,"identity":"5d77172d-1ba8-432f-9c8c-d204852b46b9","added_by":"auto","created_at":"2026-05-09 00:34:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":94593,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLasso regression analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9278473/v1/525bda70f71201e6c4bf21d5.png"},{"id":108838334,"identity":"7721b409-28b0-4813-a8c6-a2bdc4358edc","added_by":"auto","created_at":"2026-05-09 00:34:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41674,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLASSO-selected predictors and their corresponding coefficients\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9278473/v1/a909a7150f303887e347aaea.png"},{"id":108838337,"identity":"0c491b1e-b1a3-48b4-9a2e-060086f1f896","added_by":"auto","created_at":"2026-05-09 00:34:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94005,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eprogression-free survival nomogram\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9278473/v1/dfee63b0ae243803101086fa.png"},{"id":108838338,"identity":"b89acc14-9838-4b63-8dd1-fc2a90c29090","added_by":"auto","created_at":"2026-05-09 00:34:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":65393,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProgression-free survival receiver operating characteristic curve\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9278473/v1/fa8cc7af6093d1f41328d0f2.png"},{"id":108977166,"identity":"3adeb164-52c8-4da1-ae1b-32fa26d9f429","added_by":"auto","created_at":"2026-05-11 11:30:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":93732,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curve for progression-free survival\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9278473/v1/c1cfc5226bc62ad9833fbf1c.png"},{"id":108977189,"identity":"3d0f3d1f-a30f-4919-a6fa-b17da78ef669","added_by":"auto","created_at":"2026-05-11 11:30:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":88402,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision curve analysis curves of progression-free survival\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9278473/v1/49da4a05dbd7cf891603ebbb.png"},{"id":108979638,"identity":"fa08ba85-0b98-4337-86dc-4726b1c27f76","added_by":"auto","created_at":"2026-05-11 12:00:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1037681,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9278473/v1/dac4aec5-b55a-4b5a-b1e5-a67ef5e80860.pdf"},{"id":108838333,"identity":"eeafa09b-ec5a-4061-b2a3-52e52d33fdf7","added_by":"auto","created_at":"2026-05-09 00:34:11","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":19033,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9278473/v1/9199767464c2e682c3a47db9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of PSOX, FOLFOX and SOX Plus Sintilimab in advanced gastric cancer: a LASSO- Cox prognostic modeling study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer (GC) is one of the most common malignancies of the digestive system worldwide, with high incidence and mortality rates [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite advances in surgical techniques and perioperative multimodal therapies in recent years, the prognosis of patients with advanced GC remains unsatisfactory, with a high risk of recurrence and metastasis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, optimizing neoadjuvant treatment strategies and achieving precise prognostic assessment have become key focuses of current clinical research [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. At present, chemotherapy regimens based on oxaliplatin combined with fluoropyrimidines (such as SOX and FOLFOX) have been widely used in the neoadjuvant treatment of advanced GC [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. With the development of immunotherapy, programmed cell death protein 1 (PD-1) inhibitors combined with chemotherapy have emerged as a promising therapeutic approach, with several studies demonstrating improved tumor response rates and survival outcomes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition, nab-paclitaxel-based combination regimens have shown favorable antitumor activity in certain patient populations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, direct comparative studies evaluating the efficacy and safety of different neoadjuvant regimens remain limited, and clinical decision-making still lacks high-quality evidence. On the other hand, traditional prognostic evaluation mainly relies on clinical staging and pathological characteristics, which may not fully capture individual heterogeneity among patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In recent years, statistical modeling\u0026ndash;based prediction models have been increasingly applied in oncological prognostic assessment. The least absolute shrinkage and selection operator (LASSO) regression can effectively identify key variables in high-dimensional data, and its integration with Cox proportional hazards models facilitates the development of robust survival prediction models [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Nevertheless, for patients with advanced GC undergoing perioperative treatment, predictive models that incorporate both treatment response indicators (RECIST 1.1 and TRG) and conventional clinical features remain to be further explored [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Based on this background, the present study aimed to compare the efficacy and safety of three neoadjuvant regimens\u0026mdash;nab-paclitaxel plus oxaliplatin and S-1 (PSOX), oxaliplatin plus leucovorin and fluorouracil (FOLFOX), and S-1 combined with oxaliplatin and sintilimab (SOX\u0026thinsp;+\u0026thinsp;XDL)\u0026mdash;in patients with advanced GC. Furthermore, key prognostic factors were identified using LASSO regression and multivariable Cox analysis, and a PFS prediction model was developed to provide evidence for individualized treatment decision-making.\u003c/p\u003e"},{"header":"Population and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient Information\u003c/h2\u003e \u003cp\u003eA total of 298 patients with advanced GC treated at the Department of Gastrointestinal Oncology, Qinghai University Affiliated Hospital, between March 2018 and March 2023 were retrospectively enrolled. Among them, 106 patients received SOX\u0026thinsp;+\u0026thinsp;XDL, 150 received PSOX, and 42 received FOLFOX. All patients were randomly divided into a training set (70%) and a validation set (30%) using the sample function in R. The training set was used for model development, while the validation set was used for external validation. All patients received 2\u0026ndash;4 cycles of neoadjuvant chemotherapy and 2\u0026ndash;4 cycles of adjuvant chemotherapy, for a total of 6\u0026ndash;8 cycles, combined with D2 radical gastrectomy. Patients were followed up regularly for 3 years postoperatively or until the occurrence of predefined endpoints. The treatment plan can be found in \u003cb\u003eSupplementary Material S1\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion and exclusion criteria\u003c/h3\u003e\n\u003cp\u003e All patients met the following inclusion criteria: histologically confirmed primary gastric adenocarcinoma diagnosed by imaging and endoscopic biopsy, staged as IIB\u0026ndash;IIIC according to the 8th edition of the AJCC TNM staging system of the Union for International Cancer Control (UICC), without other malignancies or distant metastasis, and achieving R0 resection (no residual tumor macroscopically or microscopically); receipt of 2\u0026ndash;4 cycles of neoadjuvant chemotherapy and 2\u0026ndash;4 cycles of adjuvant chemotherapy (total 6\u0026ndash;8 cycles), with all surgical procedures performed at our institution in accordance with NCCN and CSCO guidelines; measurable primary tumor lesions on CT or MRI with postoperative pathological confirmation; and an Eastern Cooperative Oncology Group (ECOG) performance status\u0026thinsp;\u0026le;\u0026thinsp;1, with adequate hepatic, renal, hematologic, and cardiopulmonary function to tolerate chemotherapy. Patients were excluded if they had allergies to chemotherapy agents or contraindications to chemotherapy, severe comorbid conditions (such as infectious diseases, gastrointestinal bleeding, pyloric obstruction, or gastrointestinal perforation), a history of other malignancies treated with radiotherapy, chemotherapy, biological therapy, or surgery, or incomplete clinical or imaging data that precluded accurate tumor measurement. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Qinghai University Affiliated Hospital, and written informed consent was obtained from all participants.\u003c/p\u003e\n\u003ch3\u003eEvaluation of therapeutic efficacy and adverse reactions\u003c/h3\u003e\n\u003cp\u003eShort-term efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST1.1), with complete response (CR) and partial response (PR) defined as responders, and stable disease (SD) and progressive disease (PD) as non-responders. Pathological response was assessed using postoperative specimens based on tumor regression grade (TRG) according to NCCN criteria, with TRG 0\u0026ndash;2 classified as responders and TRG 3 as non-responders. All radiological and pathological evaluations were performed by experienced radiologists and pathologists. Treatment efficacy was compared among the three regimens based on RECIST1.1 and TRG assessments. Long-term efficacy was assessed using progression-free survival (PFS) as the primary endpoint, defined as the time from initiation of chemotherapy to tumor recurrence, development of new GC, or death from any cause, with a follow-up period of 3 years. Overall survival (OS) was defined as the time from initiation of chemotherapy to death from any cause or last follow-up within 3 years. Chemotherapy-related adverse events were evaluated according to the Common Terminology Criteria for Adverse Events (CTCAE), version 5.0.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software. Categorical variables were presented as frequencies (percentages) and compared using the Pearson χ\u0026sup2; test or Fisher\u0026rsquo;s exact test, as appropriate. A two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. To develop the PFS prediction model, all patients were randomly divided into a training set (70%) and a validation set (30%) with a fixed random seed to ensure reproducibility. In the training set, LASSO regression with 10-fold cross-validation was applied to select candidate variables, and the optimal penalty parameter (λ) was determined based on the λ.1se criterion to reduce overfitting and enhance model stability; a fixed random seed was also applied during this process. Variables with non-zero coefficients were subsequently entered into a multivariable Cox proportional hazards model to estimate hazard ratios (HRs) and 95% confidence intervals (CIs), thereby identifying independent predictors of PFS. Model performance was evaluated in both the training and validation sets. Discrimination was assessed using receiver operating characteristic (ROC) curves and the area under the curve (AUC), as well as the concordance index (C-index), while calibration was evaluated using calibration plots comparing predicted and observed outcomes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eBasic characteristics of patients\u003c/h2\u003e\n \u003cp\u003eComparative analysis of baseline characteristics showed no significant differences among the three groups in terms of age, sex, ASA classification, tumor differentiation, clinical T stage, lymph node status, Lauren classification, tumor location, RECIST1.1 response, and pre-treatment tumor diameter (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating good baseline comparability. Only tumor regression grade (TRG) differed significantly among the groups (P\u0026thinsp;=\u0026thinsp;0.048), as shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBasic patient characteristics and short-term efficacy, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;298\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePSOX\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;150\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eFolfox\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;42\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eSOX\u0026thinsp;+\u0026thinsp;XDL\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;106\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e163 (54.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e84 (56.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e21 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e58 (54.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e135 (45.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e66 (44.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e21 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e48 (45.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e235 (78.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e121 (80.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e36 (85.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e78 (73.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e63 (21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e29 (19.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e6 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e28 (26.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eASA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.587\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1\u0026thinsp;+\u0026thinsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e259 (86.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e128 (85.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e36 (85.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e95 (89.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e39 (13.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e22 (14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e6 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e11 (10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDifferentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.860\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e128 (43.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e65 (43.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e16 (38.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e47 (44.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e87 (29.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e44 (29.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e15 (35.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e28 (26.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePoorly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e83 (27.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e41 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e11 (26.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e31 (29.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ecT stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e154 (51.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e75 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e21 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e58 (54.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e73 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e44 (29.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e9 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e20 (18.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e71 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e31 (20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e12 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e28 (26.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ecN stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e210 (70.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e106 (70.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e30 (71.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e74 (69.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e88 (29.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e44 (29.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e12 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e32 (30.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ecTNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e152 (51.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e67 (44.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e25 (59.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e60 (56.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIIIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e52 (17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e35 (23.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e7 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e10 (9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIIIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e64 (21.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e29 (19.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e7 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e28 (26.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIIIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e30 (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e19 (12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e3 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e8 (7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRECIST1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEffective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e183 (61.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e91 (60.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e21 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e71 (67.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIneffective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e115 (38.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e59 (39.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e21 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e35 (33.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLauren\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDiffuse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e101 (33.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e54 (36.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e12 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e35 (33.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e142 (47.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e65 (43.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e21 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e56 (52.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIntestinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e55 (18.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e31 (20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e9 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e15 (14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e69 (23.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e32 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e10 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e27 (25.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e120 (40.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e61 (40.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e18 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e41 (38.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e109 (36.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e57 (38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e14 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e38 (35.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTRG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 6.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEffective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e226 (75.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e108 (72.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e29 (69.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e89 (84.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIneffective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e72 (24.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e42 (28.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e13 (31.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e17 (16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTumor diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 6.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e115 (38.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e59 (39.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e11 (26.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e45 (42.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2\u0026ndash;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e60 (20.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e34 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e7 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e19 (17.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e123 (41.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e57 (38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e24 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e42 (39.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u0026chi;\u0026sup2;: Chi-square test Tumor location is classified as upper 1/3, middle 1/3, lower 1/3, diffuse TNM staging according to the American Joint Committee on Cancer. ASA: American Society of Anesthesiologists Score; RECIST: Response Evaluation Criteria in Solid Tumors; TRG: Tumor regression grade; XDL: Sintilimab.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eEfficacy\u003c/h3\u003e\n\u003cp\u003e(1) Short-term Efficacy: As shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, short-term treatment responses among the three groups were evaluated using RECIST1.1 and TRG. Based on RECIST1.1 criteria, the response rates were 60.7% in the PSOX group, 50.0% in the FOLFOX group, and 67.0% in the SOX\u0026thinsp;+\u0026thinsp;XDL group, with no statistically significant difference among the groups (\u0026chi;\u0026sup2; = 3.73, P\u0026thinsp;=\u0026thinsp;0.155), indicating comparable radiological responses. However, TRG-based assessment revealed a significant difference among the three groups (\u0026chi;\u0026sup2; = 6.08, P\u0026thinsp;=\u0026thinsp;0.048). The SOX\u0026thinsp;+\u0026thinsp;XDL group demonstrated the highest response rate (84.0%), which was notably higher than that in the PSOX group (72.0%) and the FOLFOX group (69.0%). Overall, although no significant differences were observed in radiological response based on RECIST1.1, the SOX\u0026thinsp;+\u0026thinsp;XDL regimen showed a superior pathological response based on TRG, suggesting a potential advantage in promoting tumor regression.\u003c/p\u003e\n\u003cp\u003e(2) Survival Analysis: Kaplan\u0026ndash;Meier analysis was performed to evaluate PFS and OS among the three groups (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For PFS, a clear separation of survival curves was observed. The SOX\u0026thinsp;+\u0026thinsp;XDL group consistently demonstrated the highest PFS rate throughout the follow-up period, with its survival curve remaining above those of the PSOX and FOLFOX groups; the PSOX group showed intermediate outcomes, while the FOLFOX group had the lowest PFS. The differences between groups became more pronounced over time, particularly after approximately 18 months. Log-rank testing indicated a statistically significant difference in PFS among the three groups (P\u0026thinsp;=\u0026thinsp;0.017), suggesting that treatment regimens differed in their ability to delay disease progression. A similar trend was observed for OS. The SOX\u0026thinsp;+\u0026thinsp;XDL group exhibited the most favorable survival outcomes, maintaining consistently higher survival rates than the PSOX and FOLFOX groups throughout follow-up, followed by the PSOX group, with the FOLFOX group showing the poorest survival. The differences further widened over time, especially after 24 months. Log-rank analysis confirmed a significant difference in OS among the three groups (P\u0026thinsp;=\u0026thinsp;0.002), indicating a substantial impact of treatment strategy on long-term survival. Additionally, risk table and event distribution analyses showed that the SOX\u0026thinsp;+\u0026thinsp;XDL group maintained a higher number of patients at risk and a lower event rate at each time point, further supporting its prognostic advantage. Overall, the SOX\u0026thinsp;+\u0026thinsp;XDL regimen demonstrated superior outcomes in both PFS and OS, followed by PSOX, while FOLFOX was associated with relatively poorer prognosis.\u003c/p\u003e\n\u003ch3\u003eAdverse reaction\u003c/h3\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, all three treatment regimens were generally well tolerated, and treatment-related adverse events were manageable. There were no statistically significant differences in the incidence of most adverse events among the three groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Specifically, the rates of nausea and vomiting, liver toxicity, peripheral neuropathy, neutropenia, fewer white blood cells, fewer platelets, and anemia were comparable across groups, with no significant differences observed (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, the incidence of alopecia differed significantly among the three groups (\u0026chi;\u0026sup2; = 17.34, P\u0026thinsp;=\u0026thinsp;0.002). The PSOX group exhibited a markedly higher rate of alopecia compared with the FOLFOX and SOX\u0026thinsp;+\u0026thinsp;XDL groups, whereas the SOX\u0026thinsp;+\u0026thinsp;XDL group showed a relatively lower incidence. Overall, all three regimens demonstrated acceptable safety profiles, with similar rates of most common adverse events, while the SOX\u0026thinsp;+\u0026thinsp;XDL regimen showed better tolerability in terms of alopecia.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSide effects associated with three groups treatment, n (%)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;298)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eFOLFOX (n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePSOX (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eSOX\u0026thinsp;+\u0026thinsp;XDL (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNausea and vomiting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e62 (20.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e3 (7.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e36 (24.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e23 (21.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e178 (59.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e29 (69.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e86 (57.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e63 (59.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e51 (17.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e8 (19.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e26 (17.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e17 (16.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e7 (2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2 (4.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e2 (1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e3 (2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLiver toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e252 (84.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e34 (80.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e125 (83.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e93 (87.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e41 (13.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e6 (14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e23 (15.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e12 (11.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e5 (1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2 (4.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e2 (1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1 (0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAlopecia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e176 (59.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e21 (50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e78 (52.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e77 (72.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e105 (35.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e20 (47.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e58 (38.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e27 (25.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e17 (5.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e1 (2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e14 (9.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e2 (1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePeripheral sensory neuropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e147 (49.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e23 (54.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e75 (50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e49 (46.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e121 (40.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e15 (35.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e61 (40.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e45 (42.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e25 (8.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e4 (9.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e12 (8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e9 (8.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e5 (1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e2 (1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e3 (2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFewer neutrophils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e213 (71.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e25 (59.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e114 (76.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e74 (69.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e55 (18.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e12 (28.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e24 (16.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e19 (17.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e19 (6.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e3 (7.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e5 (3.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e11 (10.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e10 (3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2 (4.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e6 (4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e2 (1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e1 (0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1 (0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFewer white blood cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e192 (64.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e25 (59.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e100 (66.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e67 (63.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e68 (22.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e11 (26.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e28 (18.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e29 (27.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e27 (9.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e5 (11.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e13 (8.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e9 (8.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e7 (2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e6 (4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1 (0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e4 (1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e1 (2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e3 (2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFewer platelets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e213 (71.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e29 (69.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e111 (74.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e73 (68.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e57 (19.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e9 (21.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e25 (16.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e23 (21.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e23 (7.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e1 (2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e12 (8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e10 (9.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e5 (1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e3 (7.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e2 (1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAnemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e150 (50.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e21 (50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e79 (52.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e50 (47.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e108 (36.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e13 (30.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e54 (36.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e41 (38.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e26 (8.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e3 (7.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e12 (8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e11 (10.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e14 (4.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e5 (11.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e5 (3.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e4 (3.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eVariable Selection by LASSO Regression\u003c/h2\u003e\n \u003cp\u003eIn the training set, LASSO regression was applied to select candidate variables. The \u0026lambda; was determined 10-fold cross-validation, and the final model was selected based on the \u0026lambda;.1se criterion. As \u0026lambda; increased, the regression coefficients of variables gradually shrank toward zero (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating reduced model complexity. At the optimal \u0026lambda; value, five variables with non-zero coefficients were retained (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), with detailed coefficients provided in \u003cstrong\u003eSupplementary Material S1\u003c/strong\u003e. These variables included poor differentiation, lymph node metastasis, clinical TNM stage IIIC, RECIST1.1, and TRG. Among them, clinical TNM stage IIIC had the largest coefficient (0.477), suggesting the strongest impact on prognosis. Positive lymph node status (0.099) and poor differentiation (0.061) were also positively associated with increased risk of disease progression. In contrast, RECIST1.1 (\u0026minus;\u0026thinsp;0.133) and TRG (\u0026minus;\u0026thinsp;0.101) showed negative coefficients, indicating that better treatment response was associated with a lower risk of progression. Overall, clinical stage and tumor biological characteristics remained key prognostic factors, while treatment response indicators (RECIST1.1 and TRG) also contributed substantially to the predictive model.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003eCox regression analysis\u003c/h2\u003e\n \u003cp\u003eBased on variables selected by LASSO regression, tumor differentiation, lymph node status, clinical TNM stage, RECIST1.1, and TRG were included in the multivariable Cox proportional hazards model (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results showed that poor differentiation was significantly associated with an increased risk of disease progression (HR\u0026thinsp;=\u0026thinsp;1.86, 95% CI: 1.19\u0026ndash;2.91, P\u0026thinsp;=\u0026thinsp;0.006), whereas no significant difference was observed between moderate and well differentiation (P\u0026thinsp;=\u0026thinsp;0.340). Patients with positive lymph node metastasis had a higher risk of progression (HR\u0026thinsp;=\u0026thinsp;1.69, 95% CI: 1.11\u0026ndash;2.57, P\u0026thinsp;=\u0026thinsp;0.013). Clinical stage was also a significant prognostic factor, with stage IIIB (HR\u0026thinsp;=\u0026thinsp;2.14, 95% CI: 1.39\u0026ndash;3.28, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and stage IIIC (HR\u0026thinsp;=\u0026thinsp;3.94, 95% CI: 2.47\u0026ndash;6.28, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) identified as adverse predictors of PFS, while no significant difference was observed between stage IIIA and stage IIB (P\u0026thinsp;=\u0026thinsp;0.199). Regarding treatment response indicators, patients achieving a favorable response based on RECIST1.1 had a significantly lower risk of disease progression (HR\u0026thinsp;=\u0026thinsp;0.65, 95% CI: 0.46\u0026ndash;0.92, P\u0026thinsp;=\u0026thinsp;0.016), indicating a protective effect. Similarly, lower TRG grades were associated with improved prognosis (HR\u0026thinsp;=\u0026thinsp;0.56, 95% CI: 0.39\u0026ndash;0.82, P\u0026thinsp;=\u0026thinsp;0.003). Overall, tumor differentiation, lymph node metastasis, and clinical stage were identified as independent adverse prognostic factors, whereas RECIST1.1 response and TRG served as protective factors, jointly influencing PFS in patients with advanced GC.\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of multivariate Cox proportional hazards regression analyses\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eEvent N\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDifferentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.78, 2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePoorly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.19, 2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ecN stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.11, 2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ecTNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIIIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.81, 2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIIIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.39, 3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIIIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.47, 6.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRECIST1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIneffective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEffective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.46, 0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTRG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIneffective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEffective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.39, 0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cstrong\u003eNote: RECIST: Response Evaluation Criteria in Solid Tumors; TRG: Tumor regression grade\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eDevelopment of a nomogram for progression-free survival\u003c/h2\u003e\n \u003cp\u003eBased on the independent prognostic factors identified in the multivariable Cox regression analysis (tumor differentiation, lymph node status, clinical TNM stage, RECIST1.1 response, and TRG), a nomogram was developed to predict PFS in patients with advanced GC (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In this model, each variable was assigned a corresponding score, and the total points were calculated by summing the scores across all variables. The total score was then mapped to an individual linear predictor and the corresponding probabilities of 18, 24, and 36-month PFS. The nomogram demonstrated that clinical TNM stage contributed the greatest weight to the model, followed by lymph node status and tumor differentiation, highlighting the critical role of tumor stage in prognosis. Meanwhile, treatment response indicators, including RECIST1.1 and TRG, also contributed substantially, indicating their significant predictive value for patient outcomes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003eModel validation\u003c/h2\u003e\n \u003cp\u003eThe discriminatory performance of the model was evaluated using time-dependent ROC curve analysis. ROC curves were plotted in both the training and validation sets to assess the predictive accuracy for PFS at different time points (Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In the training set, the model demonstrated good discrimination across all time points, with an AUC of 0.843 (95% CI: 0.777\u0026ndash;0.909) at 18 months, indicating high short-term predictive accuracy; the AUC was 0.810 (95% CI: 0.750\u0026ndash;0.869) at 24 months, remaining at a favorable level; and 0.858 (95% CI: 0.754\u0026ndash;0.963) at 36 months, suggesting strong long-term predictive performance. In the validation set, the model showed similarly stable performance, with AUCs of 0.837 (95% CI: 0.740\u0026ndash;0.933) and 0.835 (95% CI: 0.747\u0026ndash;0.922) at 18 and 24 months, respectively, comparable to those in the training set, indicating good reproducibility and stability. At 36 months, the AUC was 0.748 (95% CI: 0.609\u0026ndash;0.887), slightly lower than that in the training set but still within an acceptable range. Overall, the model consistently achieved AUC values above 0.75 in both cohorts, with particularly strong performance at 18 and 24 months, highlighting its accuracy in short- to mid-term PFS prediction. The concordance between the training and validation sets further supports its robustness, generalizability, and potential clinical utility.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003cp\u003eCalibration curves were plotted to assess the agreement between predicted probabilities and observed outcomes for PFS at 18, 24, and 36 months in both the training and validation sets (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In the training set, the model demonstrated good calibration across all time points. At 18 months, the calibration curve closely aligned with the ideal diagonal line, indicating excellent agreement (Brier score: 12.6%). Similarly, good calibration was observed at 24 months (Brier score: 18.0%) and 36 months (Brier score: 14.5%). In the validation set, the model also showed stable calibration performance. The 18-month calibration curve closely approximated the reference line (Brier score: 17.0%), and good agreement was maintained at 24 months (Brier score: 17.7%). Although a slight deviation was observed at 36 months, the overall trend remained consistent with the ideal line (Brier score: 19.3%). Overall, the calibration curves in both the training and validation sets were close to the ideal diagonal, indicating good agreement between predicted and observed outcomes. The relatively low Brier scores across all time points further support the accuracy and stability of the model.\u003c/p\u003e\n \u003cp\u003eDCA was performed to evaluate the clinical utility of the model at 18, 24, and 36 months in both the training and validation sets (Fig. \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In the training set, the model demonstrated a consistently higher net benefit across a wide range of threshold probabilities at all time points. At 18 months, the model curve remained above both the \u0026ldquo;treat-all\u0026rdquo; and \u0026ldquo;treat-none\u0026rdquo; reference strategies, indicating a clear advantage in clinical decision-making. Similar trends were observed at 24 and 36 months, where the model provided greater net benefit across most threshold probability ranges, suggesting stable clinical applicability over time. In the validation set, the model also showed good clinical usefulness. At 18 and 24 months, the model curve consistently outperformed the reference strategies across a broad range of thresholds, indicating robust performance in an independent dataset. Although slight fluctuations in net benefit were observed at 36 months, the model still generally outperformed the \u0026ldquo;treat-all\u0026rdquo; and \u0026ldquo;treat-none\u0026rdquo; approaches. Overall, the model demonstrated favorable decision-making benefits in both cohorts, supporting its potential value in guiding clinical risk assessment and individualized treatment strategies.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e This single-center, real-world retrospective cohort study included 298 patients with locally advanced, resectable gastric adenocarcinoma (stage IIB\u0026ndash;IIIC) who underwent R0 resection and D2 gastrectomy, and compared three treatment strategies: PSOX, FOLFOX, and SOX plus sintilimab (XDL). In terms of short-term efficacy, no statistically significant differences were observed among the three groups based on RECIST1.1 response rates; however, TRG assessment demonstrated a significantly higher response rate in the SOX\u0026thinsp;+\u0026thinsp;XDL group (P\u0026thinsp;=\u0026thinsp;0.048). Regarding long-term outcomes, Kaplan\u0026ndash;Meier analysis indicated that the SOX\u0026thinsp;+\u0026thinsp;XDL group achieved superior PFS and OS compared with the other groups (log-rank: P\u0026thinsp;=\u0026thinsp;0.017 for PFS and P\u0026thinsp;=\u0026thinsp;0.002 for OS). Overall safety was acceptable across all regimens, although the incidence of alopecia differed significantly among groups (P\u0026thinsp;=\u0026thinsp;0.002). For prognostic modeling, LASSO regression was applied for variable selection, followed by construction of a multivariable Cox model incorporating tumor differentiation, lymph node status (clinical N stage), clinical TNM stage, RECIST 1.1 response, and TRG. The resulting nomogram demonstrated favorable predictive performance, with relatively high AUC values for 18, 24, and 36-month PFS in both the training and validation sets; however, the decline in AUC at 36 months in the validation set suggests some uncertainty in long-term extrapolation. Internationally, triplet perioperative chemotherapy regimens represented by FLOT have been established as a standard of care, whereas in East Asia, doublet regimens such as SOX and FOLFOX remain widely used, with evidence supporting their feasibility and efficacy. Notably, some randomized studies have demonstrated the non-inferiority and potential interchangeability of SOX compared with FOLFOX, further supporting the clinical relevance of the present findings [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn terms of short-term efficacy, no significant differences in response rates were observed among the three groups based on RECIST1.1, whereas the TRG-defined response rate was significantly higher in the SOX\u0026thinsp;+\u0026thinsp;XDL group. This discrepancy is consistent with the well-recognized challenges in evaluating neoadjuvant immunotherapy. On the one hand, the combination of immunotherapy and chemotherapy may result in concurrent tumor cell clearance and inflammatory infiltration, leading to a mismatch between radiological tumor size changes and the actual reduction in tumor burden [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. On the other hand, pathological response indicators (such as pCR, MPR, and TRG) more directly reflect tumor cell eradication and are therefore more sensitive [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Previous studies have also emphasized the need for confirmatory assessment of atypical response patterns associated with immunotherapy, such as pseudoprogression, further highlighting the limitations of RECIST criteria alone in this context. Consistent with prior evidence, the TRG advantage observed in this study aligns with the reported trend that SOX combined with PD-1 inhibitors improves pathological response. For instance, propensity score\u0026ndash;matched studies of perioperative SOX plus PD-1 inhibitors have shown enhanced pathological response rates, particularly in subgroups with higher PD-L1 combined positive score (CPS), Epstein\u0026ndash;Barr virus (EBV) positivity, or deficient mismatch repair (dMMR) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In addition, phase II studies of neoadjuvant XDL combined with chemotherapy (e.g., FLOT or CapeOx backbone) in resectable gastric or gastroesophageal junction (G/GEJ) cancer have demonstrated promising pathological responses with manageable safety profiles, and patients achieving pCR were more likely to exhibit favorable subsequent survival outcomes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn terms of long-term outcomes, this study demonstrated that the SOX\u0026thinsp;+\u0026thinsp;XDL regimen was associated with superior PFS and OS compared with PSOX and FOLFOX. This finding is directionally consistent with randomized evidence from advanced or unresectable GC, where XDL combined with chemotherapy has been shown to improve survival. In the ORIENT-16 trial, conducted in Chinese patients with unresectable locally advanced, recurrent, or metastatic G/GEJ adenocarcinoma, XDL plus chemotherapy significantly improved overall survival compared with placebo plus chemotherapy [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This observation aligns with multiple recent large-scale clinical studies indicating that PD-1 inhibitors combined with chemotherapy can significantly improve survival outcomes in advanced GC. The underlying mechanism may involve not only the direct cytotoxic effects of chemotherapy but also its ability to induce immunogenic cell death, leading to the release of tumor antigens and enhancement of antitumor immune responses, thereby producing a synergistic effect with immunotherapy [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Although ORIENT-16 was conducted in the first-line treatment setting for advanced disease rather than in the neoadjuvant setting for resectable tumors, it provides important evidence supporting the clinical synergy between XDL and fluoropyrimidine\u0026ndash;platinum-based chemotherapy [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The separation of survival curves observed in the present study further supports the biological plausibility and clinical relevance of this combination strategy.\u003c/p\u003e \u003cp\u003eIn terms of safety, all three regimens demonstrated acceptable tolerability, with most adverse events being manageable grade 1\u0026ndash;2 toxicities. Except for alopecia, the incidence of common adverse events did not differ significantly among the groups. The overall toxicity profiles\u0026mdash;including nausea and vomiting, liver function abnormalities, peripheral sensory neuropathy, myelosuppression, and anemia\u0026mdash;were comparable across regimens. Notably, neither the oxaliplatin\u0026ndash;fluoropyrimidine\u0026ndash;based doublet regimens nor the addition of XDL to the SOX backbone resulted in an unacceptable increase in toxicity. The primary difference among groups was a higher incidence of alopecia in the PSOX group, which is consistent with the known pharmacological characteristics of nab-paclitaxel. Importantly, the SOX\u0026thinsp;+\u0026thinsp;XDL regimen did not show a higher overall incidence of adverse events compared with conventional chemotherapy, which is consistent with previous reports. In prior studies, the most common hematologic toxicities included leukopenia, neutropenia, and anemia, while the most frequent non-hematologic toxicities were elevated transaminases, vomiting, and pneumonia [\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These findings are largely consistent with the present study, in which myelosuppression and gastrointestinal reactions were the predominant adverse events and were generally manageable.\u003c/p\u003e \u003cp\u003eXDL is a fully human recombinant IgG4 monoclonal antibody targeting PD-1, which blocks the interaction between PD-1 and its ligands PD-L1/PD-L2, thereby restoring T-cell activity. In multiple clinical studies and reviews, XDL is commonly administered at a dose of 200 mg every 3 weeks (Q3W), with relatively linear pharmacokinetics and a half-life of approximately two weeks (with some variation across studies and tumor types), which is consistent with its use in perioperative combination regimens based on a 21-day cycle [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Caution is warranted when interpreting claims regarding \u0026ldquo;stronger binding affinity\u0026rdquo; or \u0026ldquo;higher receptor occupancy\u0026rdquo; compared with other PD-1 inhibitors. Several studies have suggested that XDL may exhibit high PD-1 binding capacity based on in vitro affinity, receptor occupancy, or structural epitope analyses. PD-1 is a key inhibitory receptor expressed on T cells, and upon ligand binding, it recruits intracellular phosphatases through its cytoplasmic motifs, thereby attenuating co-stimulatory signaling and suppressing T-cell activation, cytokine production, and effector function [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Mechanistically, PD-1\u0026ndash;mediated inhibition is largely dependent on disruption of the CD28 co-stimulatory pathway; experimental studies have shown that CD28 is preferentially dephosphorylated by PD-1 signaling, indicating that effective PD-1 blockade depends not only on the antibody itself but also on tumor antigen presentation and the co-stimulatory microenvironment. Furthermore, interactions between PD-1 signaling and molecules such as SHP2, as well as its dynamic assembly within the immunological synapse and signaling complexes, constitute key molecular mechanisms underlying immune suppression and T-cell exhaustion [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These biological mechanisms provide a rationale for the favorable therapeutic effects observed with PD-1 blockade, including XDL, in patients with advanced GC.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSeveral limitations of this study should be acknowledged. First, as a single-center retrospective analysis, it is inherently subject to potential selection and information biases. Although baseline characteristics were generally comparable among the three groups, residual confounding cannot be fully excluded. Second, the sample sizes across treatment groups were imbalanced, particularly with a relatively small number of patients in the FOLFOX group, which may have affected the stability of statistical analyses and the power of intergroup comparisons. Third, key immunological biomarkers\u0026mdash;such as PD-L1 expression, microsatellite instability (MSI) status, tumor mutational burden (TMB), and tumor immune microenvironment-related indicators\u0026mdash;were not included. These factors are known to be closely associated with the efficacy of immunotherapy, and their absence may limit mechanistic interpretation of the observed differences in treatment outcomes, as well as the predictive accuracy of the model in immunotherapy-treated populations. In addition, the prediction model was primarily based on clinicopathological variables and treatment response indicators, without incorporating molecular subtyping or multi-omics data, which may constrain its predictive performance and external applicability. Although internal validation was performed using a training and validation set, external validation in an independent cohort is still lacking, and the generalizability of the model requires further evaluation. Finally, the follow-up duration was relatively limited, and some patients had not yet reached endpoint events, which may affect the assessment of long-term survival outcomes. Future studies with multicenter, large-scale, and prospective designs, incorporating immunological biomarkers and molecular characteristics, are warranted to further validate and optimize both therapeutic strategies and predictive models.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, the present study demonstrates that, compared with PSOX and FOLFOX, the SOX\u0026thinsp;+\u0026thinsp;XDL regimen significantly improves pathological response rates in the neoadjuvant treatment of advanced GC and provides superior PFS and OS, without a significant increase in severe adverse events, indicating a favorable and manageable safety profile. In addition, the predictive model developed based on LASSO regression and multivariable Cox analysis showed good discrimination and calibration, suggesting that integrating clinicopathological features with treatment response indicators can enhance the accuracy of prognostic assessment. These findings support SOX\u0026thinsp;+\u0026thinsp;XDL as a promising perioperative treatment strategy with a balanced profile of efficacy and safety, and provide valuable evidence for individualized treatment decision-making in patients with advanced GC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGC: gastric cancer; PFS: progression-free survival; OS: overall survival; PSOX: nab-paclitaxel plus oxaliplatin and S-1; FOLFOX: oxaliplatin plus leucovorin and fluorouracil; SOX+XDL: S-1 plus oxaliplatin combined with sintilimab; LASSO: least absolute shrinkage and selection operator; TRG: tumor regression grade; RECIST: Response Evaluation Criteria in Solid Tumors; CTCAE: Common Terminology Criteria for Adverse Events; ROC: receiver operating characteristic; AUC: area under the curve; CI: confidence interval; HR: hazard ratio; AJCC: American Joint Committee on Cancer; UICC: Union for International Cancer Control; ECOG: Eastern Cooperative Oncology Group; ASA: American Society of Anesthesiologists.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the Affiliated Hospital of Qinghai University (Approval No. SL-2022-035). Written informed consent was obtained from all participants prior to inclusion in the study.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final manuscript and consented to its publication.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding authors on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Qinghai Provincial Science and Technology Program (Grant No. 2023-ZJ-787), entitled \u0026ldquo;Biological Function and Molecular Mechanisms of ZFP36 in Gastric Cancer\u0026rdquo;. The funding body had no role in the design of the study; collection, analysis, or interpretation of data; or in writing the manuscript.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eS.-W.J. and C.-G.Z. contributed equally to this work. S.-W.J., C.-G.Z. and H.-L.W. conceived and designed the study. K.-D.W. and K.-P.S. collected and organized the clinical data. S.-W.J. and C.-G.Z. performed the statistical analyses and drafted the main manuscript. P.-J.Y. and H.-L.W. supervised the study, interpreted the data, and critically revised the manuscript. All authors reviewed the manuscript and approved the final version.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all patients who participated in this study and the staff of the Department of Gastrointestinal Oncology, Qinghai University Affiliated Hospital, for their support in data collection and clinical management.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024 May-Jun;74(3):229-263. doi: 10.3322/caac.21834. Epub 2024 Apr 4. PMID: 38572751.\u003c/li\u003e\n\u003cli\u003eTung I, Sahu A. The treatment of resectable gastric cancer: a literature review of an evolving landscape. J Gastrointest Oncol. 2022 Apr;13(2):871-884. doi: 10.21037/jgo-21-721. 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PMID: 32437509; PMCID: PMC7265324.\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":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"gastric cancer, neoadjuvant therapy, LASSO, Cox regression, prognostic model","lastPublishedDoi":"10.21203/rs.3.rs-9278473/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9278473/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e This study aimed to compare the efficacy and safety of three neoadjuvant treatment regimens—nab-paclitaxel plus oxaliplatin and S-1 (PSOX), oxaliplatin plus leucovorin and fluorouracil (FOLFOX), and S-1 combined with sintilimab and oxaliplatin (SOX+XDL)—in patients with advanced gastric cancer (GC). Additionally, independent prognostic factors associated with progression-free survival (PFS) were identified, and a predictive model was developed to enable individualized risk stratification and prognostic assessment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This retrospective study included 298 patients with advanced GC who met the inclusion and exclusion criteria. Patients were randomly divided into a training set and a validation set at a 7:3 ratio using a fixed random seed. In the training set, least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation was used to select variables based on the λ.1se criterion. Variables with non-zero coefficients were entered into a multivariable Cox proportional hazards model to identify independent factors associated with PFS, with HRs and 95% CIs calculated. The model was developed in the training set and validated in the validation set. Short-term efficacy, survival outcomes, and adverse events were compared among the three groups. Model performance was evaluated using receiver operating characteristic (ROC) curves and calibration plots.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e LASSO regression identified five variables with non-zero coefficients, including tumor differentiation, N stage, TNM stage, RECIST 1.1 response, and tumor regression grade (TRG). Among these, TNM stage IIIC showed the largest coefficient, indicating the strongest impact on prognosis. These variables were subsequently included in a multivariable Cox proportional hazards model. The results demonstrated that poor differentiation (HR = 1.86, 95% CI: 1.19–2.91, P = 0.006), lymph node metastasis (HR = 1.69, 95% CI: 1.11–2.57, P = 0.013), and advanced clinical stage (cTNM stage IIIC; HR = 3.94, 95% CI: 2.47–6.28, P \u0026lt; 0.001) were independent risk factors for PFS in patients with GC. In contrast, a favorable response based on RECIST 1.1 (HR = 0.65, 95% CI: 0.46–0.92, P = 0.016) and a lower TRG grade (HR = 0.56, 95% CI: 0.39–0.82, P = 0.003) were identified as protective factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This study demonstrated that the SOX+XDL regimen achieved a higher pathological response rate than PSOX and FOLFOX in patients with advanced GC, and showed superior outcomes in both PFS and OS, with an overall acceptable safety profile. The predictive model constructed based on LASSO and multivariable Cox regression exhibited good discrimination and calibration, and may serve as a useful tool for individualized risk assessment and clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Comparison of PSOX, FOLFOX and SOX Plus Sintilimab in advanced gastric cancer: a LASSO- Cox prognostic modeling study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-09 00:34:07","doi":"10.21203/rs.3.rs-9278473/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-24T16:55:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-23T07:28:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-02T23:46:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T16:46:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-04-02T15:42:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ab6c5d59-4e42-4122-990e-0677d745d358","owner":[],"postedDate":"May 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T00:34:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-09 00:34:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9278473","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9278473","identity":"rs-9278473","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}