A Prognostic Nomogram via Competing Risk Model with Patterns of Extracranial Metastasis in Elderly NSCLC Patients with Synchronous Brain-metastasis: A Large Population-based Study

A Prognostic Nomogram via Competing Risk Model with Patterns of Extracranial Metastasis in Elderly NSCLC Patients with Synchronous Brain-metastasis: A Large Population-based 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 A Prognostic Nomogram via Competing Risk Model with Patterns of Extracranial Metastasis in Elderly NSCLC Patients with Synchronous Brain-metastasis: A Large Population-based Study Mingwei Zhang, Zijing Zhu, Wenying Jiang, Shaoli Peng, Weitong Zhou, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1357913/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction : To determine whether the patterns of extracranial metastasis (ECMs) provide supplementary prognositc information to DS-GPA in elderly NSCLC patients with synchronous BM. Methods : This study included 4974 NSCLC patients with initial BM diagnosed from 2010 to 2015 using the Surveillance Epidemiology and End Results (SEER) program. Patients were divided randomly into training and hold-out test sets. Patterns of ECMs were established based on the difference of survival via competing risk analysis in the training set. A nomogram prediction of 6-month, 12-month, and 18-month disease-specific survival (DSS) was built using independent prognostic factors. Results : Three patterns of ECM were recognized: MA (neither liver, bone, nor lung involvement), MB (without liver involvement), and MC (with liver involvement). Comparing MA, MB and MC showed significant correlation to survival (SHR, 1.126, 95% CI, 1.053-1.205, P<0.001; SHR, 1.46, 95% CI, 1.339-1.592, P<0.001, respectively). In the hold-out test set, the AUC of the ROC curve for the 6-month DSS prediction reached 0.778, whereas reaching 0.757 in the training set. The calibration curves did not deviate from the reference line. Decision curve analyses revealed the net benefit of the nomogram for clinical utility. Conclusions : These results help clinicians make decisions for brain-metastatic NSCLC in the era of precision therapy. The risk stratification of extracranial involvements indicates differential treatment for elderly NSCLC patients with synchronous brain-metastasis. NSCLC brain metastasis extracranial metastasis geriatric population competing risk model Figures Figure 1 Figure 2 Figure 3 1. Introduction Globally, lung cancer (LC) is the number one cause of cancer-associated death[ 1 ]. Most LC-related deaths are due to metastasis, with brain being a common site. Non-small cell lung cancer (NSCLC) is the most prevalent subtype and comprises about 85% of cases[ 2 ]. The incidence of brain metastasis (BM) in NSCLC is growing due to imaging technique advancements, improved survival from novel regimens and population aging[ 3 ]. The conventional therapies for NSCLC with BM include surgery, radiation and chemotherapy with a median overall survival (mOS) ranging from 4.0 to 31.0 months[ 4 ]. The introduction of tyrosine kinase inhibitors and the immune checkpoint inhibitors has improved the 5-year survival rate from 30–60% in selected patients with metastatic NSCLC[ 5 ]. NSCLC with de novo BM has been recognized as an entity with heterogeneous prognosis[ 6 ] and its risk stratification is a critical component of standard management. Several prognostic models have been applied to predict BM patients’ outcomes, such as the Disease-Specific Graded Prognostic Assessment (DS-GPA). Besides age, DS-GPA considers four clinical factors: Karnofsky Performance Status (KPS)[ 7 ], extracranial metastasis (ECM) and BM lesions. Genomic and proteomic biomarkers have been reported to stratify patients, which necessitates invasive biopsy and costly molecular sequencing[ 6 ]. The invasive nature of biopsy and the high cost of genomic tests limit their large-scale application. Meanwhile, utilizing readily available clinical data, recombination of the M1 stage for stage IV breast cancer can forecast prognosis and effects of primary surgery better[ 8 ]. This study on breast cancer has added additional details to DS-GPA, but no such research to modify DS-GPA has been conducted for NSCLC yet. While more ECM lesions have proven to have a worse prognosis in NSCLC patients with BM [ 9 ], the subdivision of ECM, other than the total number, might more sufficiently address the medical challenges in NSCLC with BM. Noteworthy, half NSCLC patients are over 70-years-old[ 10 ]. Current epidemiological trends make LC in the geriatric population increasingly recognized as a public health concern[ 11 ]. Yet, their prognostic estimates mainly stem from younger, healthier patients enrolled in clinical trials[ 12 ], because patients over 70 years old have historically been excluded from major landmark trials[ 13 ]. According to the WHO and published estimation, cancer is not the number one cause of death in people older than 70-years[ 14 ]. Compared with the Cox proportional hazards model, the competing risk model improves the stratification of senior patients with cancer by the risk of dying from cancer relative to overall mortality[ 15 ]. A hospital-based cohort study of elderly NSCLC patients with de novo BM would be extremely challenging, regarding to obtain samples sufficient to detect ECM patterns’ impact on survival. Due to limitations of registries, the incidence of BM remains indistinct. Yet, the Surveillance Epidemiology and End Results (SEER) program revealed data relevant to with or without BM at diagnosis of malignancies in 2016[ 16 ]. Given that it is a large population-based database, selection bias is largely reduced. Exploiting NSCLC cohorts in SEER datasets, we analyzed the prognostic value of the ECM patterns in septuagenarians and octogenarians with de novo BM and plotted a nomogram for clinical usage. 2. Materials And Methods 2.1 Patients The study data set was retrieved from the SEER cancer registries, which are freely accessible via SEER*Stat 8.3.8 ( https://seer.cancer.gov ). The SEER registry comprises 18 cancer registries across America, taking up about 30% of the US population. The SEER data are periodically updated with clinical information, such as tumor basics, therapies, secondary tumors, demographics and cause of death. The present study was conducted in compliance with the Helsinki Declaration. Given that the SEER registry contains only de-identified information, ethical review and informed consent requirements were waived. Following the ICD-O-3, all lung cancer cases (site codes C34.0-34.8) diagnosed between 2010 and 2015 were potentially included in our study. Restrictive inclusion items were applied and were as follows: 1) NSCLC (histology codes, 8012/ 8013/ 8014/ 8046/ 8052/ 8070–8078/ 8083/ 8084/ 8140/ 8141/ 8143/ 8144/ 8146/ 8147/ 8250–8255/ 8260/ 8310/ 8323/ 8481/ 8560); 2) malignant behavior; 3) microscopically confirmed primary lung cancer; 4) diagnosed between 2010 and 2015, 5) de novo BM cases; 3) AJCC T1 to Tx stages. Given that elderly metastatic lung cancer patients rarely underwent surgeries, those who received surgery (< 5%) were excluded from this study to improve the homogeneity of the study population. Other exclusion criteria were: 1) aged younger than 70 years; 2) with prior or subsequent cancers; 3) underwent surgical treatments; 4) less than one month of follow-up; 5) patients with unavailable information regarding survival duration, status, cause of death or other important items. We included clinical characteristics, such as gender, race, age, American Joint Committee on Cancer (AJCC) T and N stage, histologic type, chemoradiotherapy, and ECM involvement. The included patients were then randomly divided into a training and a hold-out test set in a 50/50 ratio. The former was used to train the weights of the model, while the latter was to validate the predictive ability of the trained model. 2.2 Statistics Based on the accessibility of the SEER databank, ECM involvement in de novo brain-metastatic NSCLC patients were separated into eight categories: none, bone alone, liver alone, lung alone, both bone and liver, bone and lung, liver and lung, or all of the three. The main outcome was disease-specific survival (DSS), and a competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. The univariable and multivariable analyses of the competing risk model were conducted via Fine and Gray’s regression, which led to the recombination of extracranial involvements for further investigation. Multivariable analysis of the competing risk model was recalculated in the training, test, and whole sets, followed by subgroup analysis and interaction tests. Multivariate analysis of overall survival (OS) was carried out via Cox proportional hazards model. Sensitivity analyses were done to support the primary analysis. Cumulative incidence curves for DSS and OS were estimated via Gray’s and log-rank tests, respectively. A nomogram of independent factors predicting DSS was depicted based on multivariate analysis of the competing risk model. A stepwise variable selection method was applied. For calibration, estimated probabilities were contrasted with the actual ones. Time-dependent ROC curves were used to judge discrimination. Decision curves analysis (DCA) was applied to visualize the clinical benefits of alternative models by calculating net benefits under different threshold probabilities. Curves of therapy plan (highest cost) and no plan (no benefit) were used as references. Chi-square and Fisher exact test were used for categorical data. Statistical significance level was set at a P value (two-tailed) < 0.05, except for those that were specifically stated. Chi-square, Fisher exact test, Cox proportional hazards model, Kaplan-Meier method, Gray’s test, and Fine-Gray regression model were performed using R (version 3.4.3; The R foundation for statistics, Vienna, Austria). 3. Results 3.1 characteristics In total, 4,974 cases were identified as de novo brain-metastatic NSCLC aged over 70 years, with both training and hold-out test sets allocated 2,487 cases each. The most frequently involved organs were bone, contra-lateral lung and liver. Clinicopathologic characteristics were well-balanced between the two data sets. Specific basic characteristics are outlined in Table 1 and supplemental Table 1. Table 1 Baseline Demographic Data Total (n = 4974) Train (n = 2487) Test (n = 2487) P value Age, n (%) 0.588 74 2728 (55) 1374 (55) 1354 (54) Race, n (%) 0.196 Black 460 ( 9) 230 ( 9) 230 ( 9) White 3954 (79) 1997 (80) 1957 (79) Others 560 (11) 260 (10) 300 (12) Sex, n (%) 0.461 Male 2451 (49) 1212 (49) 1239 (50) Female 2523 (51) 1275 (51) 1248 (50) AJCC.T, n (%) 0.203 T1 532 (11) 256 (10) 276 (11) T2 1362 (27) 697 (28) 665 (27) T3 1103 (22) 558 (22) 545 (22) T4 1400 (28) 671 (27) 729 (29) Tx 577 (12) 305 (12) 272 (11) AJCC.N, n (%) 0.146 N0 1188 (24) 565 (23) 623 (25) N1 419 ( 8) 199 ( 8) 220 ( 9) N2 2225 (45) 1131 (45) 1094 (44) N3 844 (17) 431 (17) 413 (17) Nx 298 ( 6) 161 ( 6) 137 ( 6) Histology, n (%) 0.669 AC 3574 (72) 1775 (71) 1799 (72) SSC 756 (15) 380 (15) 376 (15) NSCLC 644 (13) 332 (13) 312 (13) Chemo-radiotherapy, n (%) 0.633 None 880 (18) 425 (17) 455 (18) Radiation 1762 (35) 890 (36) 872 (35) Chemotherapy 384 ( 8) 199 ( 8) 185 ( 7) Both 1948 (39) 973 (39) 975 (39) Chemo-radiotherapy_2, n (%) 0.281 None 880 (18) 425 (17) 455 (18) Yes 4094 (82) 2062 (83) 2032 (82) Bone involvement, n (%) 0.386 None 3274 (66) 1652 (66) 1622 (65) Yes 1700 (34) 835 (34) 865 (35) Liver involvement, n (%) 0.532 None 4070 (82) 2026 (81) 2044 (82) Yes 904 (18) 461 (19) 443 (18) Lung involvement, n (%) 0.448 None 3589 (72) 1782 (72) 1807 (73) Yes 1385 (28) 705 (28) 680 (27) Abbreviations: AC, adenocarcinoma; SSC, squamous carcinoma; NSCLC, non-small cell lung carcinoma, nothing otherwise special. 3.2 Correlation between patterns of distant metastasis and DSS As depicted in Table 2 , univariate analysis indicated that involvement of the liver, whether alone, with concomitant bone or lung metastasis, or with concomitant both bone and lung involvement was correlated with worse DSS (SHR, 1.43, 95% CI, 1.17–1.76, P < 0.01; SHR, 1.30, 95% CI, 1.09–1.55, P < 0.01; SHR, 1.46, 95% CI, 1.16–1.85 P < 0.01; SHR, 1.33, 95% CI, 1.08–1.64, P < 0.01, respectively). Multivariable analysis confirmed that these four patterns of distant metastasis were independent prognostic factors (SHR, 1.42, 95% CI, 1.15–1.76, P < 0.01; SHR, 1.35, 95% CI, 1.10–1.65, P < 0.01; SHR, 1.47, 95% CI, 1.17–1.85, P < 0.01; SHR, 1.40, 95% CI, 1.13–1.74, P < 0.01, respectively). Regarding bone involvement alone, it was associated with worse DSS. Although its univariate analysis is not statistically significant (SHR, 1.09, 95% CI, 1.04–1.22, P = 0.16), multivariable analysis gained significance (SHR, 1.18, 95% CI, 1.05–1.33, P < 0.01). However, no significant difference was observed with contra-lateral lung metastasis alone (SHR, 1.11, 95% CI, 0.98–1.24, P = 0.08; SHR, 1.07, 95% CI, 0.93–1.21, P = 0.35, respectively). Table 2 Univariate and Multivariate Analysis via Competing Risk Regression Model Univariate Analysis Multivariate Analysis SHR(95%CI) P value SHR(95%CI) P value Age.cat 74 1.1(1.02–1.19) 0.01 1.06(0.97–1.15) 0.19 Race Black Ref Ref White 0.977(0.86–1.11) 0.73 1.01(0.88–1.17) 0.87 Others 0.801(0.67–0.95) 0.01 0.85(0.70–1.03) 0.09 Sex Male Ref Ref Female 0.917(0.85–0.99) 0.03 0.96(0.88–1.04) 0.31 AJCC.T T1 Ref Ref T2 1.17(1.01–1.35) 0.03 1.17(1.01–1.35) 0.04 T3 1.29(1.11–1.49) < 0.01 1.24(1.06–1.46) < 0.01 T4 1.27(1.10–1.46) < 0.01 1.24(1.06–1.44) < 0.01 Tx 1.22(1.03–1.45) 0.02 1.21(1.02–1.45) 0.03 AJCC.N N0 Ref Ref N1 0.879(0.74–1.04) 0.13 0.86(0.72–1.04) 0.12 N2 1.118(1.01–1.24) 0.03 1.14(1.02–1.27) 0.02 N3 1.13(0.99–1.28) 0.05 1.22(1.07–1.40) < 0.01 Nx 1.06(0.89–1.26) 0.5 0.93(0.76–1.15) 0.51 Chemoradiotherapy None Ref Ref Radiation 0.904(0.79–1.04) 0.15 0.91(0.79–1.04) 0.16 Chemotherapy 0.482(0.40–0.57) < 0.01 0.48(0.40–0.57) < 0.01 Both 0.518(0.46–0.59) < 0.01 0.51(0.44–0.58) < 0.01 Histology AC Ref Ref SSC 1.38(1.24–1.54) < 0.01 1.29(1.14–1.45) < 0.01 NSCLC 1.32(1.18–1.48) < 0.01 1.25(1.10–1.41) < 0.01 Involvement None Ref Ref Bone 1.09(1.04–1.22) 0.16 1.18(1.05–1.33) < 0.01 Liver 1.43(1.17–1.76) < 0.01 1.42(1.15–1.76) < 0.01 Lung 1.11(0.98–1.24) 0.08 1.07(0.93–1.21) 0.35 Bone + Liver 1.30(1.09–1.55) < 0.01 1.35(1.10–1.65) < 0.01 Bone + Lung 1.07(0.94–1.26) 0.45 1.06(0.89–1.27) 0.05 Liver + Lung 1.46(1.16–1.85) < 0.01 1.47(1.17–1.85) < 0.01 All 1.33(1.08–1.64) < 0.01 1.40(1.13–1.74) < 0.01 Abbreviations: AC, adenocarcinoma; SSC, squamous carcinoma; NSCLC, non-small cell lung carcinoma, nothing otherwise special; Ref, reference. 3.3 Subcategories of M1 cohort Based on independent prognostic factors from multivariable analysis of competing risk model, the eight groups of extracranial involvements were recombined into MA, MB and MC subcategories (Supplemental Table 2). The group with all three lesions topped the list, while the group without ECM ranked at the bottom. Given that the SHR of the other three groups with liver metastasis was higher than that of the bone solo group, they ranked higher. The two groups without independent prognosis ranked after the bone solo group. Only two of the seven P values were < 0.05, making the cutoff among MA, MB, and MC. The significance of subcategory prognostic difference was confirmed by SHR (MC VS. MB 1.15, 95%CI 1.05–1.26; MB VS. MA 1.11, 95%CI 1.02–1.2). 3.4 Multivariable analysis of the competing risk model Validation of the M1 subcategory system is described and depicted in supplemental Table 3. Multivariate analysis indicated that the MC and MB subcategories were risk factors in the training set (SHR, 1.097, 95% CI, 0.998–1.206, P = 0.05; SHR, 1.392, 95% CI, 1.232–1.573, P < 0.001), hold-out test set (SHR, 1.145, 95% CI, 1.04–1.261, P = 0.006; SHR, 1.534, 95% CI, 1.363–1.725, P < 0.001), and whole dataset (SHR, 1.126, 95% CI, 1.053–1.205, P < 0.001; SHR, 1.46, 95% CI, 1.339–1.592, P < 0.001). 3.5 Cumulative incidence curves of DSS and OS Cumulative incidence curves plotted the mortality augments across M1 subcategories in training, test and whole data sets. Again, the validation of the M1 subdivision was confirmed (Fig. 1 ). 3.6 Subgroup analysis and interaction tests To study the interaction between treatment mortality and M1 subcategories, MB and MC were combined and compared to MA (Fig. 2 ). In the subgroups of no-chemoradiotherapy, radiotherapy, and chemoradiotherapy, presence of ECM was associated with unfavorable prognosis (SHR, 1.44, 95% CI, 1.21–1.71, P < 0.01; SHR, 1.22, 95% CI, 1.08–1.37, P < 0.01; SHR, 1.23, 95% CI, 1.07–1.41, P < 0.01, respectively). Further analysis of interaction was applied, and significant interaction from the therapy regimen was observed. Compared to no-chemoradiotherapy, both chemotherapy and chemoradiotherapy have synergistic modification effects on ECM (adjusted SHR, 0.67, 95% CI, 0.67–0.81, P < 0.01; adjusted SHR, 0.73, 95% CI, 0.67–0.81, P < 0.01, respectively), while solo radiotherapy imposed antagonistic modification effects on ECM (adjusted SHR, 1.25, 95% CI, 1.12–1.39, P < 0.01). 3.7 Kaplan-Meier curves and the Cox proportional hazard model Kaplan-Meier curves were depicted to investigate survival differences between the covariates (Fig. 3 ). Compared to MA, MB and MC were associated with increased risk for OS (Supplemental Table 4). HR of MB was 1.248 (95%CI, 1.133–1.374), 1.203 (95%CI, 1.094–1.323), and 1.226 (95%CI, 1.146–1.312) in the training, test and whole datasets (P < 0.001), while HR of MC was 1.892 (95%CI, 1.685–2.124), 1.815 (95%CI, 1.612–2.044) and 1.856 (95%CI, 1.709–2.016) in the training, test and whole datasets (P < 0.001). 3.8 Construction and validation of a competing risk nomogram in the training set A competing risk nomogram was built via the Fine and Gray model to predict the 6-month, 12-month and 18-month cumulative death probabilities (Fig. 4A). Calculation details are listed in Supplemental Table 5. As depicted in Fig. 4B, the calibration curves for DSS were close to the standard curves. The ROC curve used to assess the nomogram of DSS is shown in Fig. 5. AUC of DSS was 0.757, 0.748 and 0.738 for 6-month, 12-month and 18-month prediction(Fig. 4C), respectively. DCA analysis proved the clinical value of this model for 6- and 12-month prediction. (Fig. 4D-F). 3.9 Validation of the nomogram in the hold-out test set The calibration curves for DSS in the hold-out test set were plot in Fig. 5A. Again, calibration curves were close to standard curves. The ROC curve used to assess the nomogram of DSS is shown in Fig. 5. AUC of DSS was 0.778, 0.769 and 0.756 for the 6-month, 12-month, and 18-month prediction(Fig. 5B), respectively. The DCA analysis indicated the good value of this model for 6- and 12-month predictions. (Fig. 5C-E). 4. Discussion In the present study, the Fine and Gray proportional sub-distribution hazard model revealed that the anatomic extent of ECM is an independent prognostic factor for DSS in NSCLC with de novo BM. After recombination of ECM into three subtypes (MA, MB and MC), multivariable analysis based on the competing risk model was re-performed, with ECM subdivision, AJCC T and N, chemoradiotherapy, and histology found significantly associated with DSS. Furthermore, these independent predictive factors were employed to create 6-month, 12-month, and 18-month nomograms for DSS. While in the training cohort, the AUC for the 6-month DSS prediction reached 0.757, it reached 0.778 in the internal validation cohort. The calibration curves did not deviate from the reference line. Decision curve analyses (DCA) revealed the net benefit of the nomogram for clinical utility. The TNM 8th classification of LC addressed the existence of synchronous oligo-metastatic patients with a single extrathoracic metastatic lesion (M1b), which is almost the same in prognosis to those presenting with the intra-thoracic metastatic disease only (M1a)[ 17 ]. In terms of prognosis, these two groups stood out from the other metastatic population (M1c), which involved multiple metastatic organs. Identification of this new category can be related to several factors, including improvement in diagnostic and imaging accuracy, the continuous increase in systemic oncological treatment efficacy, and the favorable tolerability of local ablative treatments[ 18 ]. Given the outcome variability within M1c stages, identifying independent, but complementary prognostic factors is of intense interest[ 19 ]. As mentioned in the introduction, various models have been proposed to prognosticate patients with brain metastasis[ 20 – 22 ]. The DS-GPA incorporates the presence/ absence of ECM, but a wide range of individualized survival probability was found within each DS-GPA class. In addition to its presence alone, more ECM lesions were associated with a poorer prognosis in NSCLC patients with BM[ 9 , 23 ]. According to our analyses, ECM patterns in NSCLC with BM is qualified to stratify prognosis. Patients were divided into MA (neither liver, bone, nor lung involvement), MB (without liver involvement), and MC (with liver involvement). The MA subcategory had relatively favorable prognosis, while the MC subcategory had significant survival disadvantage. Our stratification is consistent with previous research[ 9 , 24 ]. It has been proposed that distant metastases in LC are nonrandom and there may be specific patterns of distant metastases[ 25 ]. According to the seed and soil hypothesis[ 26 ], there are complex ways for disseminated cells to interact with the host organ microenvironment, hence different interactions may result in distinctive patterns of metastatic events. According to our analyses, once the liver becomes involved, patient survival would be impaired. One reported theory is that, as the liver is an immunosuppressive organ when metastases occur, liver metastasis impedes the liver’s immune surveillance of other ongoing metastases[ 27 ]. Similarly, NSCLC patients with metastasis to their brain and liver were more likely to progress when treated with PD-1 inhibitors. Multivariable analysis of the competing risk model, as well as CIF, validated the independent prognostic role of ECM subdivision in NSCLC with de novo BM. In a cohort of 4974 patients, our analysis harbored a large number of NSCLC cases with initial BM, hence enough statistical power to examine the potential correlation between ECM subdivision and DSS. The competing risk model was somehow different from the Cox model, suggesting that the improvement in the competing risk model was relevant. Elderly LC patients with BM have disadvantages in various aspects, such as immuno-senescence and decreased physiologic reserve[ 28 ]. The competing risk model can improve stratification of elderly cancer patients with cancer-specific death to overall mortality[ 15 , 29 ]. Although the competing risk model was less than perfect, it was validated in our data. The nomogram allowed for individualized estimation of DSS and highlighted the potential to tailor predictions to individual patients rather than group-wise estimations from other models like the DS-GPA. This nomogram can be readily used in clinical practice to provide patients and their physician with an individualized survival estimate, using readily available variables. As early as 2012, Jill S proposed a nomogram for individual estimation of survival among patients with BM. They incorporated age, KPS, primary sites, histology, status of primary disease, metastatic spread (brain alone or with ECM), and number of brain lesions. After direct comparison with the DS-GPA, our nomogram was superior in individualized estimations of survival. Compared to Jill S’ nomogram, our nomogram can provide an even more detailed prognosis for elderly NSCLC patients with BM, although direct comparison between these two nomograms wasn’t available due to lack of relevant information in SEER. Another nomogram was plotted by Heng Shen to predict early death of LC patients with synchronous BM[ 30 ]. Age, race, gender, Gleason grade, histological type, T stage, N stage, bone metastasis, liver metastasis and marital status were included in the nomogram. The AUC of the ROC curve was 0.794 (95% CI: 0.788–0.799). Similarly, in Heng Shen’s nomogram, synchronous liver metastasis in LC patients with BM significantly increases the risk of early death. Hence, this nomogram is consistent with ours. This study had several limitations. The demographic and clinical information provided by SEER isn’t complete.It also lacks detailed records of treatment, such as chemotherapy and radiotherapy. Both targeted therapy and immunotherapy have become popular in the comprehensive treatment of NSCLC with BM. However, the SEER database has neither specific information about these novel therapies nor mutation data. Third, head-to-head comparisons between our nomogram and the DS-PGA were unavailable because DS-PGA couldn’t be calculated based on SEER data. Additionally, we used internal samples to validate the competing risk nomogram. It would be better if external validation from another dataset was available. Thus, more studies are necessary to validate our results in the future. 5. Conclusion In conclusion, the extracranial subdivision of M1 categories harbors the type and number of metastatic sites and provides extra information for prognosis in septuagenarians and octogenarians with De Novo Brain-metastatic NSCLC. A created competing risk nomogram incorporated the pattern of ECM, AJCC T and N, histology, and chemoradiotherapy. This may act as a useful implement for clinicians to evaluate DSS and help to choose appropriate medical care. Declarations Author contributions Mingwei Zhang and Xiaoping Chen conceived and designed the study. Zijing Zhu analyzed the data. Wenying Jiang authored the manuscript. Shaoli Peng and Weitong Zhou prepared the figures and tables. Jinfu Zhuang supervised the study. Jinsheng Hong revised the manuscript. All authors reviewed the final manuscript. Acknowledgement This study was supported by National Natural Science Foundation of China (82003386 and U1805263); the Special Projects of the Central Government Guiding Local Science and Technology Development (2021L3018); Natural Science Foundation of Fujian Province (2021J01658); the program for Probability and Statistics: Theory and Application (IRTL1704) and Innovative Research Team in Science and Technology in Fujian Province University (IRTSTFJ); Fujian Provincial Finance Project (No.2019B032). 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Curr Oncol Rep 23(1):11. https://doi.org/10.1007/s11912-020-01000-y WHO. Global Health Estimates (2020) : Deaths by Cause, Age, Sex, by Country and by region, 2000–2019. whoint/ data/gho/data/theme s/morta lity-andgloba l-healt h-estim ates/ghe-leadi ngcause s-of-death. 2020, who .int/ data/gho/data/theme s/morta lity-andgloba l-healt h-estim ates/ghe-leadi ngcause s-of-death Carmona R, Zakeri K, Green G, Hwang L, Gulaya S, Xu B, Verma R, Williamson CW, Triplett DP, Rose BS, Shen H, Vaida F, Murphy JD, Mell LK (2016) Improved Method to Stratify Elderly Patients With Cancer at Risk for Competing Events. J Clin oncology: official J Am Soc Clin Oncol 34(11):1270–1277. .https://doi.org/10.1200/JCO.2015.65.0739 Lamba N, Wen PY, Aizer AA (2021) Epidemiology of brain metastases and leptomeningeal disease. Neuro Oncol 23(9):1447–1456. https://doi.org/10.1093/neuonc/noab101 Eberhardt WEE, Mitchell A, Crowley J, Kondo H, Kim YT, Turrisi A, Goldstraw P, Rami-Porta R (2015) The IASLC Lung Cancer Staging Project: Proposals for the Revision of the M Descriptors in the Forthcoming Eighth Edition of the TNM Classification of Lung Cancer. J Thorac Oncol 10(11):1515–1522. .https://doi.org/10.1097/JTO.0000000000000673 deSouza NM, Liu Y, Chiti A, Oprea-Lager D, Gebhart G, Van Beers BE, Herrmann K, Lecouvet FE (2018) Strategies and technical challenges for imaging oligometastatic disease: Recommendations from the European Organisation for Research and Treatment of Cancer imaging group. Eur J Cancer 91:153–163. https://doi.org/10.1016/j.ejca.2017.12.012 Smeltzer MP, Faris NR, Ray MA, Osarogiagbon RU (2018) Association of Pathologic Nodal Staging Quality With Survival Among Patients With Non-Small Cell Lung Cancer After Resection With Curative Intent. JAMA Oncol 4(1):80–87. https://doi.org/10.1001/jamaoncol.2017.2993 Gaspar L, Scott C, Rotman M, Asbell S, Phillips T, Wasserman T, McKenna WG, Byhardt R (1997) Recursive partitioning analysis (RPA) of prognostic factors in three Radiation Therapy Oncology Group (RTOG) brain metastases trials. Int J Radiat Oncol Biol Phys 37(4):745–751. https://pubmed.ncbi.nlm.nih.gov/9128946 Sperduto PW, Kased N, Roberge D, Xu Z, Shanley R, Luo X, Sneed PK, Chao ST, Weil RJ, Suh J, Bhatt A, Jensen AW, Brown PD, Shih HA, Kirkpatrick J, Gaspar LE, Fiveash JB, Chiang V, Knisely JPS, Sperduto CM, Lin N, Mehta M (2012) Summary report on the graded prognostic assessment: an accurate and facile diagnosis-specific tool to estimate survival for patients with brain metastases. J Clin oncology: official J Am Soc Clin Oncol 30(4):419–425. https://doi.org/10.1200/JCO.2011.38.0527 Sperduto PW, Yang TJ, Beal K, Pan H, Brown PD, Bangdiwala A, Shanley R, Yeh N, Gaspar LE, Braunstein S, Sneed P, Boyle J, Kirkpatrick JP, Mak KS, Shih HA, Engelman A, Roberge D, Arvold ND, Alexander B, Awad MM, Contessa J, Chiang V, Hardie J, Ma D, Lou E, Sperduto W, Mehta MP (2017) Estimating Survival in Patients With Lung Cancer and Brain Metastases: An Update of the Graded Prognostic Assessment for Lung Cancer Using Molecular Markers (Lung-molGPA). JAMA Oncol 3(6):827–831. https://doi.org/10.1001/jamaoncol.2016.3834 Higuera Gómez O, Moreno Paul A, Ortega Granados AL, Ros Martínez S, Pérez Parente D, Ruiz Gracia P, Sáenz Cuervo-Arango L, Vilà L (2021) "High Tumor Burden" in Metastatic Non-Small Cell Lung Cancer: Defining the Concept. Cancer Manage Res 13:4665–4670. .https://doi.org/10.2147/CMAR.S302928 Li J, Zhu H, Sun L, Xu W, Wang X (2019) Prognostic value of site-specific metastases in lung cancer: A population based study. J Cancer 10(14):3079–3086. .https://doi.org/10.7150/jca.30463 Huang Y, Zhu L, Guo T, Chen W, Zhang Z, Li W, Pan X (2021) Metastatic sites as predictors in advanced NSCLC treated with PD-1 inhibitors: a systematic review and meta-analysis. Hum Vaccin Immunother 17(5):1278–1287. .https://doi.org/10.1080/21645515.2020.1823779 Belluomini L, Dodi A, Caldart A, Kadrija D, Sposito M, Casali M, Sartori G, Ferrara MG, Avancini A, Bria E, Menis J, Milella M, Pilotto S (2021) A narrative review on tumor microenvironment in oligometastatic and oligoprogressive non-small cell lung cancer: a lot remains to be done. Transl Lung Cancer Res 10(7):3369–3384. .https://doi.org/10.21037/tlcr-20-1134 Fan Z, Huang Z, Tong Y, Zhu Z, Huang X, Sun H (2021) Sites of Synchronous Distant Metastases, Prognosis, and Nomogram for Small Cell Lung Cancer Patients with Bone Metastasis: A Large Cohort Retrospective Study. J Oncol. 2021:9949714.https://doi.org/10.1155/2021/9949714 Gu Y, Zhang J, Zhou Z, Liu D, Zhu H, Wen J, Xu X, Chen T, Fan M (2020) Metastasis Patterns and Prognosis of Octogenarians with NSCLC: A Population-based Study. Aging Dis 11(1):82–92. https://doi.org/10.14336/AD.2019.0414 Dignam JJ, Kocherginsky MN (2008) Choice and interpretation of statistical tests used when competing risks are present. J Clin oncology: official J Am Soc Clin Oncol 26(24):4027–4034. .https://doi.org/10.1200/JCO.2007.12.9866 Shen H, Deng G, Chen Q, Qian J (2021) The incidence, risk factors and predictive nomograms for early death of lung cancer with synchronous brain metastasis: a retrospective study in the SEER database. BMC Cancer 21(1):825. https://doi.org/10.1186/s12885-021-08490-4 Additional Declarations No competing interests reported. Supplementary Files S1table.docx Detailed Baseline Demographic Data S2table.docx Description of Groups and Subcategories Based on Independent Prognostic Factors S3table.docx Multivariate Analysis of DSS via Competing Risk Regression S4table.docx Multivariable Cox proportional hazards models for OS S5table.docx Calculation details based on the Fine and Gray model Supplementalfigure1.tif Subgroup and interaction analysisAbbreviations: AC, adenocarcinoma; SSC, squamous carcinoma; NSCLC , non-small cell lung carcinoma, nothing otherwise special. Supplementalfigure2.tif Kaplan-Meier curves of OS (A-H) and DSS (I-P) in elderly NSCLC patients with synchronous BM: age (A&I), sex (B&J), race (C&K), AJCC T (D&L), AJCC N (E&M), histologic type (F&N), chemoradiotherapy (G&O) and ECM patterns (H&P).Abbreviations: AC, adenocarcinoma; SSC, squamous carcinoma; NSCLC , non-small cell lung carcinoma, nothing otherwise special; Mets subdivision, extracranial metastasis subdivision. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-1357913","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":84207949,"identity":"7c4a1f2a-e822-467d-84d3-3d1c9067e69b","order_by":0,"name":"Mingwei Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mingwei","middleName":"","lastName":"Zhang","suffix":""},{"id":84207950,"identity":"77a434a5-a7ce-4d1d-96d0-dd4e3253f407","order_by":1,"name":"Zijing Zhu","email":"","orcid":"","institution":"Fujian Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zijing","middleName":"","lastName":"Zhu","suffix":""},{"id":84207951,"identity":"0b644faa-598c-41bf-9504-1ebf18d22c68","order_by":2,"name":"Wenying Jiang","email":"","orcid":"","institution":"The Third Affiliated Hospital of Soochow University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenying","middleName":"","lastName":"Jiang","suffix":""},{"id":84207952,"identity":"17bd87c0-4df7-4785-8c08-5b6ea56dde2a","order_by":3,"name":"Shaoli Peng","email":"","orcid":"","institution":"The First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shaoli","middleName":"","lastName":"Peng","suffix":""},{"id":84207953,"identity":"65d9e4df-1528-4686-ac21-652e57fefc87","order_by":4,"name":"Weitong Zhou","email":"","orcid":"","institution":"The First Affiliated Hospital of Fujian Medical 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Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYHACNiC24WdsgLGJ1JIm2UiqlsOSDXA2IWBwu/nZg487zkswz+4xYPhQdpiBf3YDfi2Sc46ZG848c1uCcc4ZA8YZ5w4zSNw5gF8Lv0QOmzRv2+06xhk5Bsy8bYcZDCQSCHgEpOVv2zkJsJa/xGgB28LYdgCihZEYLUC/mEn2tiUDtaQVHOw5l84jcYOAFlCISfxss5MwnJG88cGPMms5/hkEtDBIQGnDBgaGA0Cah4B6JC3yhJWOglEwCkbBSAUAsaw+uvxVrR8AAAAASUVORK5CYII=","orcid":"","institution":"Fujian Normal University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xiaoping","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2022-02-14 10:44:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1357913/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1357913/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":18364884,"identity":"85efcabb-2473-4dff-b79e-01f7edc753e8","added_by":"auto","created_at":"2022-02-18 14:28:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":509870,"visible":true,"origin":"","legend":"\u003cp\u003eCurves of Cumulative Incidence (A-C) and Log-rank Test (D-F) among ECM patterns: the training set (A\u0026amp;D), the hold-out test set (B\u0026amp;E), and the whole set (C\u0026amp;F).\u003c/p\u003e","description":"","filename":"Onlinefigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1357913/v1/62d8c4c8cc07830be0d871e2.png"},{"id":18364783,"identity":"e703e01f-cfa2-42c7-a643-ca2b4968fa22","added_by":"auto","created_at":"2022-02-18 14:25:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":446244,"visible":true,"origin":"","legend":"\u003cp\u003eCompeting risk nomogram predicting 6-month, 12-month, and 18-month cumulative probabilities of DSS in elderly NSCLC patients with synchronous BM (A), calibration curves and ROC curves with AUC for 6-month, 12-month, and 18-month prediction (B and C), DCA curves for 6-month (D), 12-month (E), and 18-month (F) prediction, in the training set.\u003c/p\u003e\u003cp\u003eAbbreviations: AC, adenocarcinoma; SSC, squamous carcinoma; NSCLC , non-small cell lung carcinoma, nothing otherwise special; Mets subdivision, extracranial metastasis subdivision.\u003c/p\u003e","description":"","filename":"Onlinefigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1357913/v1/ded6162b9def9044c63a901d.png"},{"id":18364786,"identity":"e0161baa-bb32-44f1-8212-2e311b910ebc","added_by":"auto","created_at":"2022-02-18 14:25:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":387062,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the nomogram in the hold-out test set: calibration curves and ROC curves with AUC for 6-month, 12-month, and 18-month prediction (A and B), DCA curves for 6-month (C), 12-month (D), and 18-month (E) prediction, in the hold-out test set.\u003c/p\u003e","description":"","filename":"Onlinefigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1357913/v1/736dab6ecba11a5635c44c4f.png"},{"id":23886423,"identity":"551c2819-11e8-405f-ac3f-2f7b0b278bcb","added_by":"auto","created_at":"2022-07-15 02:59:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1148861,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1357913/v1/141641f1-78a0-4a5f-a194-78276e299f4a.pdf"},{"id":18364780,"identity":"c149b5a2-227b-4b29-994e-b8156e3f4bf5","added_by":"auto","created_at":"2022-02-18 14:25:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19638,"visible":true,"origin":"","legend":"\u003cp\u003eDetailed Baseline Demographic Data\u003c/p\u003e","description":"","filename":"S1table.docx","url":"https://assets-eu.researchsquare.com/files/rs-1357913/v1/4346c4cc26d8e04b13aca12c.docx"},{"id":18364782,"identity":"fa42ce35-d836-4d43-b09a-d1a7e7d82fe8","added_by":"auto","created_at":"2022-02-18 14:25:08","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14365,"visible":true,"origin":"","legend":"\u003cp\u003eDescription of Groups and Subcategories Based on Independent Prognostic Factors\u003c/p\u003e","description":"","filename":"S2table.docx","url":"https://assets-eu.researchsquare.com/files/rs-1357913/v1/82c4d015c0aa79a8dac0f0a2.docx"},{"id":18364779,"identity":"c30aeb96-b7d5-44ea-9c2a-a28cff9d6f11","added_by":"auto","created_at":"2022-02-18 14:25:08","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17685,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate Analysis of DSS via Competing Risk Regression\u003c/p\u003e","description":"","filename":"S3table.docx","url":"https://assets-eu.researchsquare.com/files/rs-1357913/v1/8328b8f3e501a90f72aa2d57.docx"},{"id":18364882,"identity":"2b73648b-16a6-48fd-99a7-d3afdc451046","added_by":"auto","created_at":"2022-02-18 14:28:08","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":17816,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable Cox proportional hazards models for OS\u003c/p\u003e","description":"","filename":"S4table.docx","url":"https://assets-eu.researchsquare.com/files/rs-1357913/v1/7cf0ddf50ec2917751be0c27.docx"},{"id":18364883,"identity":"c717ce7e-50a8-49c7-b7d0-d51c2ee13573","added_by":"auto","created_at":"2022-02-18 14:28:08","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":13644,"visible":true,"origin":"","legend":"\u003cp\u003eCalculation details based on the Fine and Gray model\u003c/p\u003e","description":"","filename":"S5table.docx","url":"https://assets-eu.researchsquare.com/files/rs-1357913/v1/b96221333e8e4d9866699093.docx"},{"id":18364788,"identity":"03325e08-70d3-40ac-918f-475254d3bcff","added_by":"auto","created_at":"2022-02-18 14:25:10","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":45372788,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup and interaction analysis\u003c/p\u003e\u003cp\u003eAbbreviations: AC, adenocarcinoma; SSC, squamous carcinoma; NSCLC , non-small cell lung carcinoma, nothing otherwise special.\u003c/p\u003e","description":"","filename":"Supplementalfigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-1357913/v1/f76899b47abeb8a20b4afe82.tif"},{"id":18364787,"identity":"2de71b5b-575b-4e3e-91f9-4fffe3738ef0","added_by":"auto","created_at":"2022-02-18 14:25:09","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":18200948,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves of OS (A-H) and DSS (I-P) in elderly NSCLC patients with synchronous BM: age (A\u0026amp;I), sex (B\u0026amp;J), race (C\u0026amp;K), AJCC T (D\u0026amp;L), AJCC N (E\u0026amp;M), histologic type (F\u0026amp;N), chemoradiotherapy (G\u0026amp;O) and ECM patterns (H\u0026amp;P).\u003c/p\u003e\u003cp\u003eAbbreviations: AC, adenocarcinoma; SSC, squamous carcinoma; NSCLC , non-small cell lung carcinoma, nothing otherwise special; Mets subdivision, extracranial metastasis subdivision. \u003c/p\u003e","description":"","filename":"Supplementalfigure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-1357913/v1/40e453147efc0ab278bd4b9b.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Prognostic Nomogram via Competing Risk Model with Patterns of Extracranial Metastasis in Elderly NSCLC Patients with Synchronous Brain-metastasis: A Large Population-based Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobally, lung cancer (LC) is the number one cause of cancer-associated death[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Most LC-related deaths are due to metastasis, with brain being a common site. Non-small cell lung cancer (NSCLC) is the most prevalent subtype and comprises about 85% of cases[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The incidence of brain metastasis (BM) in NSCLC is growing due to imaging technique advancements, improved survival from novel regimens and population aging[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The conventional therapies for NSCLC with BM include surgery, radiation and chemotherapy with a median overall survival (mOS) ranging from 4.0 to 31.0 months[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The introduction of tyrosine kinase inhibitors and the immune checkpoint inhibitors has improved the 5-year survival rate from 30\u0026ndash;60% in selected patients with metastatic NSCLC[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. NSCLC with de novo BM has been recognized as an entity with heterogeneous prognosis[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and its risk stratification is a critical component of standard management.\u003c/p\u003e \u003cp\u003eSeveral prognostic models have been applied to predict BM patients\u0026rsquo; outcomes, such as the Disease-Specific Graded Prognostic Assessment (DS-GPA). Besides age, DS-GPA considers four clinical factors: Karnofsky Performance Status (KPS)[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], extracranial metastasis (ECM) and BM lesions. Genomic and proteomic biomarkers have been reported to stratify patients, which necessitates invasive biopsy and costly molecular sequencing[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The invasive nature of biopsy and the high cost of genomic tests limit their large-scale application. Meanwhile, utilizing readily available clinical data, recombination of the M1 stage for stage IV breast cancer can forecast prognosis and effects of primary surgery better[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study on breast cancer has added additional details to DS-GPA, but no such research to modify DS-GPA has been conducted for NSCLC yet. While more ECM lesions have proven to have a worse prognosis in NSCLC patients with BM [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], the subdivision of ECM, other than the total number, might more sufficiently address the medical challenges in NSCLC with BM. Noteworthy, half NSCLC patients are over 70-years-old[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Current epidemiological trends make LC in the geriatric population increasingly recognized as a public health concern[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Yet, their prognostic estimates mainly stem from younger, healthier patients enrolled in clinical trials[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], because patients over 70 years old have historically been excluded from major landmark trials[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. According to the WHO and published estimation, cancer is not the number one cause of death in people older than 70-years[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Compared with the Cox proportional hazards model, the competing risk model improves the stratification of senior patients with cancer by the risk of dying from cancer relative to overall mortality[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA hospital-based cohort study of elderly NSCLC patients with de novo BM would be extremely challenging, regarding to obtain samples sufficient to detect ECM patterns\u0026rsquo; impact on survival. Due to limitations of registries, the incidence of BM remains indistinct. Yet, the Surveillance Epidemiology and End Results (SEER) program revealed data relevant to with or without BM at diagnosis of malignancies in 2016[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Given that it is a large population-based database, selection bias is largely reduced. Exploiting NSCLC cohorts in SEER datasets, we analyzed the prognostic value of the ECM patterns in septuagenarians and octogenarians with de novo BM and plotted a nomogram for clinical usage.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patients\u003c/h2\u003e \u003cp\u003eThe study data set was retrieved from the SEER cancer registries, which are freely accessible via SEER*Stat 8.3.8 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://seer.cancer.gov\u003c/span\u003e\u003c/span\u003e). The SEER registry comprises 18 cancer registries across America, taking up about 30% of the US population. The SEER data are periodically updated with clinical information, such as tumor basics, therapies, secondary tumors, demographics and cause of death. The present study was conducted in compliance with the Helsinki Declaration. Given that the SEER registry contains only de-identified information, ethical review and informed consent requirements were waived.\u003c/p\u003e \u003cp\u003eFollowing the ICD-O-3, all lung cancer cases (site codes C34.0-34.8) diagnosed between 2010 and 2015 were potentially included in our study. Restrictive inclusion items were applied and were as follows: 1) NSCLC (histology codes, 8012/ 8013/ 8014/ 8046/ 8052/ 8070\u0026ndash;8078/ 8083/ 8084/ 8140/ 8141/ 8143/ 8144/ 8146/ 8147/ 8250\u0026ndash;8255/ 8260/ 8310/ 8323/ 8481/ 8560); 2) malignant behavior; 3) microscopically confirmed primary lung cancer; 4) diagnosed between 2010 and 2015, 5) de novo BM cases; 3) AJCC T1 to Tx stages.\u003c/p\u003e \u003cp\u003eGiven that elderly metastatic lung cancer patients rarely underwent surgeries, those who received surgery (\u0026lt;\u0026thinsp;5%) were excluded from this study to improve the homogeneity of the study population. Other exclusion criteria were: 1) aged younger than 70 years; 2) with prior or subsequent cancers; 3) underwent surgical treatments; 4) less than one month of follow-up; 5) patients with unavailable information regarding survival duration, status, cause of death or other important items.\u003c/p\u003e \u003cp\u003eWe included clinical characteristics, such as gender, race, age, American Joint Committee on Cancer (AJCC) T and N stage, histologic type, chemoradiotherapy, and ECM involvement. The included patients were then randomly divided into a training and a hold-out test set in a 50/50 ratio. The former was used to train the weights of the model, while the latter was to validate the predictive ability of the trained model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Statistics\u003c/h2\u003e \u003cp\u003eBased on the accessibility of the SEER databank, ECM involvement in de novo brain-metastatic NSCLC patients were separated into eight categories: none, bone alone, liver alone, lung alone, both bone and liver, bone and lung, liver and lung, or all of the three. The main outcome was disease-specific survival (DSS), and a competing risk is an event whose occurrence precludes the occurrence of the primary event of interest.\u003c/p\u003e \u003cp\u003eThe univariable and multivariable analyses of the competing risk model were conducted via Fine and Gray\u0026rsquo;s regression, which led to the recombination of extracranial involvements for further investigation. Multivariable analysis of the competing risk model was recalculated in the training, test, and whole sets, followed by subgroup analysis and interaction tests. Multivariate analysis of overall survival (OS) was carried out via Cox proportional hazards model. Sensitivity analyses were done to support the primary analysis. Cumulative incidence curves for DSS and OS were estimated via Gray\u0026rsquo;s and log-rank tests, respectively.\u003c/p\u003e \u003cp\u003eA nomogram of independent factors predicting DSS was depicted based on multivariate analysis of the competing risk model. A stepwise variable selection method was applied. For calibration, estimated probabilities were contrasted with the actual ones. Time-dependent ROC curves were used to judge discrimination. Decision curves analysis (DCA) was applied to visualize the clinical benefits of alternative models by calculating net benefits under different threshold probabilities. Curves of therapy plan (highest cost) and no plan (no benefit) were used as references.\u003c/p\u003e \u003cp\u003eChi-square and Fisher exact test were used for categorical data. Statistical significance level was set at a P value (two-tailed)\u0026thinsp;\u0026lt;\u0026thinsp;0.05, except for those that were specifically stated. Chi-square, Fisher exact test, Cox proportional hazards model, Kaplan-Meier method, Gray\u0026rsquo;s test, and Fine-Gray regression model were performed using R (version 3.4.3; The R foundation for statistics, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 characteristics\u003c/h2\u003e \u003cp\u003eIn total, 4,974 cases were identified as de novo brain-metastatic NSCLC aged over 70 years, with both training and hold-out test sets allocated 2,487 cases each. The most frequently involved organs were bone, contra-lateral lung and liver. Clinicopathologic characteristics were well-balanced between the two data sets. Specific basic characteristics are outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and supplemental Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Demographic Data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;4974)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrain (n\u0026thinsp;=\u0026thinsp;2487)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTest (n\u0026thinsp;=\u0026thinsp;2487)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;=74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2246 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1113 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1133 (46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2728 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1374 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1354 (54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRace, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e460 ( 9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e230 ( 9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e230 ( 9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3954 (79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1997 (80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1957 (79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e560 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e260 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e300 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2451 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1212 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1239 (50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2523 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1275 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1248 (50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAJCC.T, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e532 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e256 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e276 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1362 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e697 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e665 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1103 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e558 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e545 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1400 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e671 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e729 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e577 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e305 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e272 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAJCC.N, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1188 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e565 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e623 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e419 ( 8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e199 ( 8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220 ( 9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2225 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1131 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1094 (44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e844 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e431 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e413 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e298 ( 6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161 ( 6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e137 ( 6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHistology, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3574 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1775 (71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1799 (72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e756 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e380 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e376 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e644 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e332 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e312 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eChemo-radiotherapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e880 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e425 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e455 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1762 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e890 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e872 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e384 ( 8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e199 ( 8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e185 ( 7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1948 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e973 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e975 (39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eChemo-radiotherapy_2, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e880 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e425 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e455 (18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4094 (82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2062 (83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2032 (82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBone involvement, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3274 (66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1652 (66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1622 (65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1700 (34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e835 (34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e865 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eLiver involvement, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4070 (82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2026 (81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2044 (82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e904 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e461 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e443 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLung involvement, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3589 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1782 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1807 (73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1385 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e705 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e680 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: AC, adenocarcinoma; SSC, squamous carcinoma; NSCLC, non-small cell lung carcinoma, nothing otherwise special.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Correlation between patterns of distant metastasis and DSS\u003c/h2\u003e \u003cp\u003eAs depicted in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, univariate analysis indicated that involvement of the liver, whether alone, with concomitant bone or lung metastasis, or with concomitant both bone and lung involvement was correlated with worse DSS (SHR, 1.43, 95% CI, 1.17\u0026ndash;1.76, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; SHR, 1.30, 95% CI, 1.09\u0026ndash;1.55, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; SHR, 1.46, 95% CI, 1.16\u0026ndash;1.85 P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; SHR, 1.33, 95% CI, 1.08\u0026ndash;1.64, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, respectively). Multivariable analysis confirmed that these four patterns of distant metastasis were independent prognostic factors (SHR, 1.42, 95% CI, 1.15\u0026ndash;1.76, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; SHR, 1.35, 95% CI, 1.10\u0026ndash;1.65, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; SHR, 1.47, 95% CI, 1.17\u0026ndash;1.85, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; SHR, 1.40, 95% CI, 1.13\u0026ndash;1.74, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, respectively). Regarding bone involvement alone, it was associated with worse DSS. Although its univariate analysis is not statistically significant (SHR, 1.09, 95% CI, 1.04\u0026ndash;1.22, P\u0026thinsp;=\u0026thinsp;0.16), multivariable analysis gained significance (SHR, 1.18, 95% CI, 1.05\u0026ndash;1.33, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, no significant difference was observed with contra-lateral lung metastasis alone (SHR, 1.11, 95% CI, 0.98\u0026ndash;1.24, P\u0026thinsp;=\u0026thinsp;0.08; SHR, 1.07, 95% CI, 0.93\u0026ndash;1.21, P\u0026thinsp;=\u0026thinsp;0.35, respectively).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and Multivariate Analysis via Competing Risk Regression Model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSHR(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSHR(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge.cat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;=74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1(1.02\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06(0.97\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.977(0.86\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01(0.88\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.801(0.67\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85(0.70\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.917(0.85\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96(0.88\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAJCC.T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17(1.01\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17(1.01\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29(1.11\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24(1.06\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27(1.10\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24(1.06\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22(1.03\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21(1.02\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAJCC.N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.879(0.74\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86(0.72\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.118(1.01\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14(1.02\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13(0.99\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22(1.07\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06(0.89\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93(0.76\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eChemoradiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.904(0.79\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91(0.79\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.482(0.40\u0026ndash;0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48(0.40\u0026ndash;0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.518(0.46\u0026ndash;0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51(0.44\u0026ndash;0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.38(1.24\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.29(1.14\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32(1.18\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25(1.10\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvolvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09(1.04\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18(1.05\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.43(1.17\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42(1.15\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11(0.98\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07(0.93\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone\u0026thinsp;+\u0026thinsp;Liver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30(1.09\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35(1.10\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone\u0026thinsp;+\u0026thinsp;Lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07(0.94\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06(0.89\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver\u0026thinsp;+\u0026thinsp;Lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.46(1.16\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47(1.17\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33(1.08\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.40(1.13\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: AC, adenocarcinoma; SSC, squamous carcinoma; NSCLC, non-small cell lung carcinoma, nothing otherwise special; Ref, reference.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Subcategories of M1 cohort\u003c/h2\u003e \u003cp\u003eBased on independent prognostic factors from multivariable analysis of competing risk model, the eight groups of extracranial involvements were recombined into MA, MB and MC subcategories (Supplemental Table\u0026nbsp;2). The group with all three lesions topped the list, while the group without ECM ranked at the bottom. Given that the SHR of the other three groups with liver metastasis was higher than that of the bone solo group, they ranked higher. The two groups without independent prognosis ranked after the bone solo group. Only two of the seven P values were \u0026lt;\u0026thinsp;0.05, making the cutoff among MA, MB, and MC. The significance of subcategory prognostic difference was confirmed by SHR (MC VS. MB 1.15, 95%CI 1.05\u0026ndash;1.26; MB VS. MA 1.11, 95%CI 1.02\u0026ndash;1.2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Multivariable analysis of the competing risk model\u003c/h2\u003e \u003cp\u003eValidation of the M1 subcategory system is described and depicted in supplemental Table\u0026nbsp;3. Multivariate analysis indicated that the MC and MB subcategories were risk factors in the training set (SHR, 1.097, 95% CI, 0.998\u0026ndash;1.206, P\u0026thinsp;=\u0026thinsp;0.05; SHR, 1.392, 95% CI, 1.232\u0026ndash;1.573, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hold-out test set (SHR, 1.145, 95% CI, 1.04\u0026ndash;1.261, P\u0026thinsp;=\u0026thinsp;0.006; SHR, 1.534, 95% CI, 1.363\u0026ndash;1.725, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and whole dataset (SHR, 1.126, 95% CI, 1.053\u0026ndash;1.205, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; SHR, 1.46, 95% CI, 1.339\u0026ndash;1.592, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Cumulative incidence curves of DSS and OS\u003c/h2\u003e \u003cp\u003eCumulative incidence curves plotted the mortality augments across M1 subcategories in training, test and whole data sets. Again, the validation of the M1 subdivision was confirmed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Subgroup analysis and interaction tests\u003c/h2\u003e \u003cp\u003eTo study the interaction between treatment mortality and M1 subcategories, MB and MC were combined and compared to MA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the subgroups of no-chemoradiotherapy, radiotherapy, and chemoradiotherapy, presence of ECM was associated with unfavorable prognosis (SHR, 1.44, 95% CI, 1.21\u0026ndash;1.71, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; SHR, 1.22, 95% CI, 1.08\u0026ndash;1.37, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; SHR, 1.23, 95% CI, 1.07\u0026ndash;1.41, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, respectively).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther analysis of interaction was applied, and significant interaction from the therapy regimen was observed. Compared to no-chemoradiotherapy, both chemotherapy and chemoradiotherapy have synergistic modification effects on ECM (adjusted SHR, 0.67, 95% CI, 0.67\u0026ndash;0.81, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; adjusted SHR, 0.73, 95% CI, 0.67\u0026ndash;0.81, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, respectively), while solo radiotherapy imposed antagonistic modification effects on ECM (adjusted SHR, 1.25, 95% CI, 1.12\u0026ndash;1.39, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Kaplan-Meier curves and the Cox proportional hazard model\u003c/h2\u003e \u003cp\u003eKaplan-Meier curves were depicted to investigate survival differences between the covariates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Compared to MA, MB and MC were associated with increased risk for OS (Supplemental Table\u0026nbsp;4). HR of MB was 1.248 (95%CI, 1.133\u0026ndash;1.374), 1.203 (95%CI, 1.094\u0026ndash;1.323), and 1.226 (95%CI, 1.146\u0026ndash;1.312) in the training, test and whole datasets (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while HR of MC was 1.892 (95%CI, 1.685\u0026ndash;2.124), 1.815 (95%CI, 1.612\u0026ndash;2.044) and 1.856 (95%CI, 1.709\u0026ndash;2.016) in the training, test and whole datasets (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Construction and validation of a competing risk nomogram in the training set\u003c/h2\u003e \u003cp\u003eA competing risk nomogram was built via the Fine and Gray model to predict the 6-month, 12-month and 18-month cumulative death probabilities (Fig.\u0026nbsp;4A). Calculation details are listed in Supplemental Table\u0026nbsp;5.\u003c/p\u003e \u003cp\u003eAs depicted in Fig.\u0026nbsp;4B, the calibration curves for DSS were close to the standard curves. The ROC curve used to assess the nomogram of DSS is shown in Fig.\u0026nbsp;5. AUC of DSS was 0.757, 0.748 and 0.738 for 6-month, 12-month and 18-month prediction(Fig.\u0026nbsp;4C), respectively. DCA analysis proved the clinical value of this model for 6- and 12-month prediction. (Fig.\u0026nbsp;4D-F).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Validation of the nomogram in the hold-out test set\u003c/h2\u003e \u003cp\u003eThe calibration curves for DSS in the hold-out test set were plot in Fig.\u0026nbsp;5A. Again, calibration curves were close to standard curves. The ROC curve used to assess the nomogram of DSS is shown in Fig.\u0026nbsp;5. AUC of DSS was 0.778, 0.769 and 0.756 for the 6-month, 12-month, and 18-month prediction(Fig.\u0026nbsp;5B), respectively. The DCA analysis indicated the good value of this model for 6- and 12-month predictions. (Fig.\u0026nbsp;5C-E).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn the present study, the Fine and Gray proportional sub-distribution hazard model revealed that the anatomic extent of ECM is an independent prognostic factor for DSS in NSCLC with de novo BM. After recombination of ECM into three subtypes (MA, MB and MC), multivariable analysis based on the competing risk model was re-performed, with ECM subdivision, AJCC T and N, chemoradiotherapy, and histology found significantly associated with DSS. Furthermore, these independent predictive factors were employed to create 6-month, 12-month, and 18-month nomograms for DSS. While in the training cohort, the AUC for the 6-month DSS prediction reached 0.757, it reached 0.778 in the internal validation cohort. The calibration curves did not deviate from the reference line. Decision curve analyses (DCA) revealed the net benefit of the nomogram for clinical utility.\u003c/p\u003e \u003cp\u003eThe TNM\u003csup\u003e8th\u003c/sup\u003e classification of LC addressed the existence of synchronous oligo-metastatic patients with a single extrathoracic metastatic lesion (M1b), which is almost the same in prognosis to those presenting with the intra-thoracic metastatic disease only (M1a)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In terms of prognosis, these two groups stood out from the other metastatic population (M1c), which involved multiple metastatic organs. Identification of this new category can be related to several factors, including improvement in diagnostic and imaging accuracy, the continuous increase in systemic oncological treatment efficacy, and the favorable tolerability of local ablative treatments[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Given the outcome variability within M1c stages, identifying independent, but complementary prognostic factors is of intense interest[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. As mentioned in the introduction, various models have been proposed to prognosticate patients with brain metastasis[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The DS-GPA incorporates the presence/ absence of ECM, but a wide range of individualized survival probability was found within each DS-GPA class. In addition to its presence alone, more ECM lesions were associated with a poorer prognosis in NSCLC patients with BM[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to our analyses, ECM patterns in NSCLC with BM is qualified to stratify prognosis. Patients were divided into MA (neither liver, bone, nor lung involvement), MB (without liver involvement), and MC (with liver involvement). The MA subcategory had relatively favorable prognosis, while the MC subcategory had significant survival disadvantage. Our stratification is consistent with previous research[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. It has been proposed that distant metastases in LC are nonrandom and there may be specific patterns of distant metastases[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. According to the seed and soil hypothesis[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], there are complex ways for disseminated cells to interact with the host organ microenvironment, hence different interactions may result in distinctive patterns of metastatic events. According to our analyses, once the liver becomes involved, patient survival would be impaired. One reported theory is that, as the liver is an immunosuppressive organ when metastases occur, liver metastasis impedes the liver\u0026rsquo;s immune surveillance of other ongoing metastases[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Similarly, NSCLC patients with metastasis to their brain and liver were more likely to progress when treated with PD-1 inhibitors.\u003c/p\u003e \u003cp\u003eMultivariable analysis of the competing risk model, as well as CIF, validated the independent prognostic role of ECM subdivision in NSCLC with de novo BM. In a cohort of 4974 patients, our analysis harbored a large number of NSCLC cases with initial BM, hence enough statistical power to examine the potential correlation between ECM subdivision and DSS. The competing risk model was somehow different from the Cox model, suggesting that the improvement in the competing risk model was relevant. Elderly LC patients with BM have disadvantages in various aspects, such as immuno-senescence and decreased physiologic reserve[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The competing risk model can improve stratification of elderly cancer patients with cancer-specific death to overall mortality[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Although the competing risk model was less than perfect, it was validated in our data.\u003c/p\u003e \u003cp\u003eThe nomogram allowed for individualized estimation of DSS and highlighted the potential to tailor predictions to individual patients rather than group-wise estimations from other models like the DS-GPA. This nomogram can be readily used in clinical practice to provide patients and their physician with an individualized survival estimate, using readily available variables. As early as 2012, Jill S proposed a nomogram for individual estimation of survival among patients with BM. They incorporated age, KPS, primary sites, histology, status of primary disease, metastatic spread (brain alone or with ECM), and number of brain lesions. After direct comparison with the DS-GPA, our nomogram was superior in individualized estimations of survival. Compared to Jill S\u0026rsquo; nomogram, our nomogram can provide an even more detailed prognosis for elderly NSCLC patients with BM, although direct comparison between these two nomograms wasn\u0026rsquo;t available due to lack of relevant information in SEER. Another nomogram was plotted by Heng Shen to predict early death of LC patients with synchronous BM[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Age, race, gender, Gleason grade, histological type, T stage, N stage, bone metastasis, liver metastasis and marital status were included in the nomogram. The AUC of the ROC curve was 0.794 (95% CI: 0.788\u0026ndash;0.799). Similarly, in Heng Shen\u0026rsquo;s nomogram, synchronous liver metastasis in LC patients with BM significantly increases the risk of early death. Hence, this nomogram is consistent with ours.\u003c/p\u003e \u003cp\u003eThis study had several limitations. The demographic and clinical information provided by SEER isn\u0026rsquo;t complete.It also lacks detailed records of treatment, such as chemotherapy and radiotherapy. Both targeted therapy and immunotherapy have become popular in the comprehensive treatment of NSCLC with BM. However, the SEER database has neither specific information about these novel therapies nor mutation data. Third, head-to-head comparisons between our nomogram and the DS-PGA were unavailable because DS-PGA couldn\u0026rsquo;t be calculated based on SEER data. Additionally, we used internal samples to validate the competing risk nomogram. It would be better if external validation from another dataset was available. Thus, more studies are necessary to validate our results in the future.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, the extracranial subdivision of M1 categories harbors the type and number of metastatic sites and provides extra information for prognosis in septuagenarians and octogenarians with De Novo Brain-metastatic NSCLC. A created competing risk nomogram incorporated the pattern of ECM, AJCC T and N, histology, and chemoradiotherapy. This may act as a useful implement for clinicians to evaluate DSS and help to choose appropriate medical care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMingwei Zhang\u0026nbsp;and\u0026nbsp;Xiaoping Chen conceived and designed the study.\u0026nbsp;Zijing Zhu\u0026nbsp;analyzed the data.\u0026nbsp;Wenying Jiang\u0026nbsp;authored the\u0026nbsp;manuscript.\u0026nbsp;Shaoli Peng\u0026nbsp;and\u0026nbsp;Weitong Zhou\u0026nbsp;prepared the figures and tables.\u0026nbsp;Jinfu Zhuang supervised the study. Jinsheng Hong revised the\u0026nbsp;manuscript.\u0026nbsp;All authors reviewed the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by National Natural Science Foundation of China (82003386 and U1805263); the Special Projects of the Central Government Guiding Local Science and Technology Development (2021L3018); Natural Science Foundation of Fujian Province (2021J01658); the program for Probability and Statistics: Theory and Application (IRTL1704) and Innovative Research Team in Science and Technology in Fujian Province University (IRTSTFJ); Fujian Provincial Finance Project (No.2019B032).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest:\u0026nbsp;\u003c/strong\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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BMC Cancer 21(1):825. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12885-021-08490-4\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"NSCLC, brain metastasis, extracranial metastasis, geriatric population, competing risk model","lastPublishedDoi":"10.21203/rs.3.rs-1357913/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1357913/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e: To determine whether the patterns of extracranial metastasis (ECMs) provide supplementary prognositc information to DS-GPA in elderly NSCLC patients with synchronous BM. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This study included 4974 NSCLC patients with initial BM diagnosed from 2010 to 2015 using the Surveillance Epidemiology and End Results (SEER) program. Patients were divided randomly into training and hold-out test sets. Patterns of ECMs were established based on the difference of survival via competing risk analysis in the training set. A nomogram prediction of 6-month, 12-month, and 18-month disease-specific survival (DSS) was built using independent prognostic factors. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Three patterns of ECM were recognized: MA (neither liver, bone, nor lung involvement), MB (without liver involvement), and MC (with liver involvement). Comparing MA, MB and MC showed significant correlation to survival (SHR, 1.126, 95% CI, 1.053-1.205, P\u0026lt;0.001; SHR, 1.46, 95% CI, 1.339-1.592, P\u0026lt;0.001, respectively). In the hold-out test set, the AUC of the ROC curve for the 6-month DSS prediction reached 0.778, whereas reaching 0.757 in the training set. The calibration curves did not deviate from the reference line. Decision curve analyses revealed the net benefit of the nomogram for clinical utility. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: These results help clinicians make decisions for brain-metastatic NSCLC in the era of precision therapy.\u0026nbsp;The risk stratification of extracranial involvements indicates differential treatment for elderly NSCLC patients with synchronous brain-metastasis.\u003c/p\u003e","manuscriptTitle":"A Prognostic Nomogram via Competing Risk Model with Patterns of Extracranial Metastasis in Elderly NSCLC Patients with Synchronous Brain-metastasis: A Large Population-based Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-02-18 14:25:06","doi":"10.21203/rs.3.rs-1357913/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"40d86129-c2dc-4c1e-a0b5-c109c3f6cac0","owner":[],"postedDate":"February 18th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-07-15T02:59:17+00:00","versionOfRecord":[],"versionCreatedAt":"2022-02-18 14:25:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1357913","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1357913","identity":"rs-1357913","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}