{"paper_id":"377dc057-87cd-4f86-a327-bcfbc6b5fe29","body_text":"Predicting Survival Rates: The Power of Prognostic Nomograms in Distal Cholangiocarcinoma | 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 Predicting Survival Rates: The Power of Prognostic Nomograms in Distal Cholangiocarcinoma Jiangfeng Hu, Yuping Shi, Bensong Duan, Lihua Jin, Suhong Yi, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4401724/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 Objective : The purpose of this research is to establish a prognostic nomogram for patients with distal cholangiocarcinoma（dCCA）. Methods : We obtained clinical data from 2401 patients diagnosed with distal cholangiocarcinoma (dCCA) between 2010 and 2020 from the Surveillance, Epidemiology, and End Results database. These patients were randomly assigned to either the training or validation group in a ratio of 1:1. 228 patients were enrolled from 9 hospitals in China as the external validation cohort. Univariate and multifactorial Cox regression analyses were conducted to ascertain prognostic factors and prognostic nomograms were developed utilizing LASSO logistic regression analysis. We used the calibration curve, and area under the curve to validate the nomograms. Decision curve analysis was used to evaluate the model and its clinical applicability. Results: The findings demonstrated that Grade, M stages, Surgery, and Chemotherapy emerged as autonomous prognostic factors for the survival of individuals with dCCA. The developed nomograms exhibited satisfactory accuracy in forecasting 1-year, 3-year, and 5-year survival probabilities. Furthermore, the calibration curves indicated a strong concordance between the anticipated and observed outcomes. Conclusion： The nomograms that have been suggested demonstrate strong predictive capability. These tools can assist medical professionals in assessing the prognosis of patients with dCCA and in devising more accurate treatment strategies for them. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1.1. INTRODUCTION Cholangiocarcinoma is an infrequent yet highly aggressive cancer that originates from the biliary epithelium, with an estimated yearly occurrence of about 1–2 cases per 100,000 people in Western nations [ 1 ]. The disease is classified based on its anatomical location into intrahepatic, perihilar, and distal cholangiocarcinoma, with distal cholangiocarcinoma accounting for approximately 20–30% of all cholangiocarcinoma cases [ 2 ]. Surgical intervention continues to be the primary approach for curative management of distal cholangiocarcinoma and negative surgical margins are the most important predictor of long-term survival [ 3 ]. Despite curative-intent resection, many patients experience disease recurrence, necessitating the use of adjuvant therapies such as chemotherapy to improve outcomes [ 4 – 6 ]. The TNM staging system, which integrates tumor dimensions (T), lymph node participation (N), and distant metastasis (M), is frequently employed for prognostic purposes in cholangiocarcinoma. However, this system has limitations in predicting individual patient outcomes and guiding treatment decisions [ 7 ]. In recent times, there have been endeavors to identify additional prognostic factors and develop prediction models to improve the accuracy of survival predictions in patients with cholangiocarcinoma. Numerous research studies have examined the predictive importance of clinicopathological factors in distal cholangiocarcinoma. These factors encompass direct bilirubin (DBIL), perineural invasion, lymph node metastasis, resection margin status, tumor size, and various others [ 8 – 11 ]. Additionally, molecular markers such as KRAS mutations and programmed death-ligand 1 (PD-L1) have been explored as potential prognostic indicators and targets for novel therapies in cholangiocarcinoma [ 12 – 14 ]. In clinical practice, accurate prediction of survival outcomes is crucial for treatment planning and counseling of patients with distal cholangiocarcinoma. Nomograms, which are graphical representations of prediction models, have been increasingly utilized to estimate individualized survival probabilities based on multiple prognostic factors[ 15 ]. Furthermore, the use of nomograms allows for the integration of diverse prognostic variables and facilitates risk stratification in clinical decision-making[ 16 ]. Due to the intricate nature and constraints of existing prognostic instruments for distal cholangiocarcinoma, there is a requirement for comprehensive clinical prediction models that can effectively categorize patients according to their prognosis. In this study, we aim to develop and validate a prediction model for survival outcomes in patients with distal cholangiocarcinoma, incorporating a range of clinicopathological and treatment-related factors. The use of Cox regression and the construction of a nomogram will enable us to create a practical tool for predicting individualized survival probabilities in this patient population. 1.2. MATERIALS AND METHODS 1.2.1. Patient Data The data utilized in this study were acquired from SEER∗Stat (version 8.4.0.1). Inclusion criteria for patients encompassed the following: (1) the year of diagnosis falling from 2010 to 2020, (2) site code: C24.0, and ICD-O- histology/behavior codes: 8140,8160. Patients who satisfied any of the specified criteria were excluded from the study: (1) those with a second primary cancer, and (2) those with a survival period of less than 30 days. Some of the variables were regrouped for the analysis. Patients were re-evaluated based on the staging principles outlined in the eighth edition of the American Joint Committee on Cancer guidelines. Overall survival was defined as the duration between the date of diagnosis and the date of death from any cause or the date of the last follow-up visit. Furthermore, the external validation set of this study was obtained from 9 hospitals in China, including Shanghai General Hospital, Shanghai Tenth People's Hospital, Shanghai East Hospital, etc. The flowchart shows the whole process of data screening and data analysis in this study (Figure 1 ). 1.2.2. Data Collection The following demographic and clinical data were obtained in this research: Age, Sex, Race, Grade, TNM stage, Bone metastases, Brain metastases, Liver metastases, Lung metastases, Surgery, Radiation, Chemotherapy, and Marital. The data of outcome and vital status, were also collected. 1.2.3. Statistical Analysis The dataset was partitioned into training and validation cohorts randomly, with a 1:1 ratio, and the variables were subsequently analyzed for comparison. In the univariate analysis, categorical variables were analyzed using either the chi-square test or Fisher's exact test, while continuous variables were examined using either the Student’s t-test or rank-sum test. In the training cohort, multivariate analysis was conducted using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to ascertain independent risk factors of dCCA and construct a prediction nomogram for the survival probability of dCCA. The nomogram's performance was assessed by employing the ROC curve and calibration curve. The AUC curve was utilized to measure the discriminant ability, with values ranging from 0.5 (indicating no discriminant ability) to 1 (indicating complete discriminant ability). Additionally, a decision curve analysis (DCA) was conducted to establish the net benefit threshold of prediction. Survival analysis was conducted using the Kaplan-Meier method. Statistical significance was determined by results with a p-value of less than 0.05. All statistical analyses were carried out using the R software (version 4.2.2). 1.3. RESULTS Patient Characteristics In this study, we analyzed the initial demographic and clinical characteristics of 2,689 individuals across different cohorts (Table 1 ). The overall distribution of age showed a significant difference (p = 0.028) among the cohorts, with the percentage of patients aged < 55 varying from 9.3% in the training cohort to 15.6% in the external test cohort. Sex distribution did not differ significantly (p = 0.132) between the cohorts, with females representing 42.4–49.0% of the different groups. In terms of race, a significant difference (p < 0.001) was observed, with the proportion of White patients ranging from 73.9% in the training cohort to 0 in the external test cohort. The distribution of tumor grade also varied significantly (p < 0.001) across the cohorts, with the proportion of Grade III-IV tumors ranging from 15.8–30.2%. Tumor stage (T) did not show significant differences across the cohorts (p = 0.252), with T3 tumors being the most prevalent in all groups. Lymph node involvement (N) also did not differ significantly (p = 0.625) between the cohorts. Most patients were N0, with proportions ranging from 52.4–57.1% across the cohorts. The presence of distant metastases (M) showed a significant difference (p = 0.010) across the cohorts, with the proportion of M1 disease ranging from 21.5–29.9%. The distribution of liver and lung metastases did not differ significantly across the cohorts, with proportions ranging from 76.0–82.3% for liver metastases and from 92.4–93.5% for lung metastases. The proportions of patients who received surgery, chemotherapy, and radiation did not differ significantly across the cohorts, with all p-values > 0.05. Marital status also did not differ significantly (p = 0.444) across the cohorts, with the majority of patients being married in all groups, ranging from 55.1–57.8%. These findings indicate variability in demographic and clinical characteristics across different cohorts, highlighting the importance of considering these factors in predictive research. Table 1. Demographic and clinical characteristics of patients across different cohors Characteristic Cohort p-value 2 Overall, N = 2,689 1 Training Cohort, N = 1,201 1 Internal Test Cohort, N = 1,200 1 External Test Cohort, N = 288 1 Age 0.028 < 55 278 (10.3%) 112 (9.3%) 121 (10.1%) 45 (15.6%) 55–64 574 (21.3%) 261 (21.7%) 264 (22.0%) 49 (17.0%) 65–74 790 (29.4%) 361 (30.1%) 355 (29.6%) 74 (25.7%) ≥ 75 1,047 (38.9%) 467 (38.9%) 460 (38.3%) 120 (41.7%) Sex 0.132 Female 1,176 (43.7%) 526 (43.8%) 509 (42.4%) 141 (49.0%) Male 1,513 (56.3%) 675 (56.2%) 691 (57.6%) 147 (51.0%) Race < 0.001 White 1,804 (67.1%) 887 (73.9%) 917 (76.4%) 0 (0.0%) Black 211 (7.8%) 110 (9.2%) 101 (8.4%) 0 (0.0%) Asian 674 (25.1%) 204 (17.0%) 182 (15.2%) 288 (100.0%) Grade < 0.001 Grade I 136 (5.1%) 48 (4.0%) 72 (6.0%) 16 (5.6%) Grade II 609 (22.6%) 250 (20.8%) 265 (22.1%) 94 (32.6%) Grade III-IV 513 (19.1%) 236 (19.7%) 190 (15.8%) 87 (30.2%) Unknown 1,431 (53.2%) 667 (55.5%) 673 (56.1%) 91 (31.6%) T 0.252 T1 413 (15.4%) 176 (14.7%) 190 (15.8%) 47 (16.3%) T2 254 (9.4%) 105 (8.7%) 110 (9.2%) 39 (13.5%) T3 1,011 (37.6%) 465 (38.7%) 439 (36.6%) 107 (37.2%) T4 250 (9.3%) 119 (9.9%) 110 (9.2%) 21 (7.3%) TX 761 (28.3%) 336 (28.0%) 351 (29.3%) 74 (25.7%) N 0.625 N0 1,502 (55.9%) 666 (55.5%) 685 (57.1%) 151 (52.4%) N1 665 (24.7%) 300 (25.0%) 282 (23.5%) 83 (28.8%) N2 92 (3.4%) 38 (3.2%) 44 (3.7%) 10 (3.5%) NX 430 (16.0%) 197 (16.4%) 189 (15.8%) 44 (15.3%) M 0.010 M0 2,063 (76.7%) 919 (76.5%) 942 (78.5%) 202 (70.1%) M1 626 (23.3%) 282 (23.5%) 258 (21.5%) 86 (29.9%) Bone metastases 0.827 No 2,547 (94.7%) 1,132 (94.3%) 1,142 (95.2%) 273 (94.8%) Yes 49 (1.8%) 22 (1.8%) 21 (1.8%) 6 (2.1%) Unknown 93 (3.5%) 47 (3.9%) 37 (3.1%) 9 (3.1%) Brain metastases 0.023 No 2,589 (96.3%) 1,150 (95.8%) 1,162 (96.8%) 277 (96.2%) Yes 2 (0.1%) 0 (0.0%) 0 (0.0%) 2 (0.7%) Unknown 98 (3.6%) 51 (4.2%) 38 (3.2%) 9 (3.1%) Liver metastases 0.047 No 2,179 (81.0%) 973 (81.0%) 987 (82.3%) 219 (76.0%) Yes 412 (15.3%) 180 (15.0%) 171 (14.3%) 61 (21.2%) Unknown 98 (3.6%) 48 (4.0%) 42 (3.5%) 8 (2.8%) Lung metastases 0.715 No 2,501 (93.0%) 1,113 (92.7%) 1,122 (93.5%) 266 (92.4%) Yes 85 (3.2%) 36 (3.0%) 38 (3.2%) 11 (3.8%) Unknown 103 (3.8%) 52 (4.3%) 40 (3.3%) 11 (3.8%) Surgery 0.864 No/Unknown 1,506 (56.0%) 667 (55.5%) 679 (56.6%) 160 (55.6%) Yes 1,183 (44.0%) 534 (44.5%) 521 (43.4%) 128 (44.4%) Chemotherapy 0.457 No/Unknown 1,363 (50.7%) 603 (50.2%) 604 (50.3%) 156 (54.2%) Yes 1,326 (49.3%) 598 (49.8%) 596 (49.7%) 132 (45.8%) Radiation 0.520 No/Unknown 2,148 (79.9%) 963 (80.2%) 949 (79.1%) 236 (81.9%) Yes 541 (20.1%) 238 (19.8%) 251 (20.9%) 52 (18.1%) Marital 0.444 Married 1,521 (56.6%) 662 (55.1%) 693 (57.8%) 166 (57.6%) Single 361 (13.4%) 167 (13.9%) 150 (12.5%) 44 (15.3%) Other 807 (30.0%) 372 (31.0%) 357 (29.8%) 78 (27.1%) 1 n (%) 2 Pearson's Chi-squared test; Fisher's exact test The Results of Univariate Cox regression Firstly, a univariate analysis was employed to conduct an initial screening of the variables included in the study. A significance level of P < 0.05 was utilized. The findings of the univariate regression analysis are detailed in Table 2 . It was observed that variables such as age, sex, T stage, N stage, M stage, surgery, chemotherapy, radiotherapy, bone metastasis, brain metastasis, lung metastasis, liver metastasis, and marital status may potentially serve as independent risk factors for overall survival. Table 2. Results of Univariate Cox regression Characteristic N Event N HR 1 95% CI 1 p-value Age <55 112 78 — — 55-64 261 176 0.94 0.72, 1.22 0.626 65-74 361 259 1.04 0.81, 1.34 0.755 ≥75 467 385 1.61 1.26, 2.05 <0.001 Sex Female 526 423 — — Male 675 475 0.81 0.71, 0.92 0.002 Race White 887 672 — — Black 110 82 1.08 0.86, 1.36 0.490 Asian 204 144 0.91 0.76, 1.09 0.321 Grade Grade I 48 29 — — Grade II 250 150 1.04 0.70, 1.55 0.840 Grade III-IV 236 173 1.50 1.01, 2.22 0.045 Unknown 667 546 3.07 2.11, 4.47 <0.001 T T1 176 153 — — T2 105 74 0.58 0.44, 0.77 <0.001 T3 465 321 0.75 0.62, 0.91 0.004 T4 119 78 0.86 0.66, 1.13 0.291 TX 336 272 1.60 1.31, 1.96 <0.001 N N0 666 490 — — N1 300 235 0.92 0.79, 1.08 0.307 N2 38 16 0.64 0.39, 1.05 0.076 NX 197 157 1.92 1.60, 2.30 <0.001 M M0 919 657 — — M1 282 241 2.43 2.08, 2.83 <0.001 Bone metastases No 1,132 833 — — Yes 22 21 3.90 2.52, 6.04 <0.001 Unknown 47 44 1.73 1.28, 2.35 <0.001 Brain metastases No 1,150 850 — — Yes 0 0 Unknown 51 48 1.76 1.31, 2.35 <0.001 Liver metastases No 973 697 — — Yes 180 156 2.49 2.08, 2.97 <0.001 Unknown 48 45 1.92 1.42, 2.60 <0.001 Lung metastases No 1,113 816 — — Yes 36 33 3.05 2.14, 4.33 <0.001 Unknown 52 49 1.71 1.28, 2.29 <0.001 Surgery No/Unknown 667 579 — — Yes 534 319 0.29 0.25, 0.33 <0.001 Radiation No/Unknown 963 736 — — Yes 238 162 0.56 0.47, 0.66 <0.001 Chemotherapy No/Unknown 603 501 — — Yes 598 397 0.53 0.46, 0.60 <0.001 Marital Married 662 472 — — Single 167 125 1.36 1.12, 1.66 0.002 Other 372 301 1.54 1.34, 1.79 <0.001 1 HR = Hazard Ratio, CI = Confidence Interval Multivariate analysis of risk factors for dCCA The original model included the candidate predictors of Age, Sex, T stage, N stage, M stage, Surgery, Chemotherapy, Radiotherapy, Bone metastasis, Brain metastasis, Lung metastasis, Liver metastasis, and Marital status. The training cohort was subjected to LASSO regression analysis to determine the significant variables. Figure 1 B depicts the Cross-Validation Plot for Lasso Regression, with the optimal logλ indicated by the intersection of the red dotted vertical line, denoting the minimum value for the LASSO regression model. The two dotted lines represent one standard deviation from the minimum value. Figure 1 C illustrates the LASSO coefficient profiles of the potential factors. Each curve corresponds to a coefficient, with the x-axis representing the regularization penalty parameter. As λ changes, a coefficient that becomes non-zero enters the LASSO regression model. The resultant model includes four potential predictors: Grade, M stage, Surgery, and Chemotherapy. The study revealed that all four variables were individually able to predict the survival of dCCA. According to Table 3 , patients with Grade III-IV exhibited poorer survival rates in comparison to those with Grade I (HR = 1.50, 95% CI 1.01–2.22, p < 0.05). Table 3: Results of Multivariate Cox regression for Training Cohort Characteristic N Event N HR 1 95% CI 1 p-value Grade Grade I 48 29 — — Grade II 250 150 1.18 0.79, 1.75 0.421 Grade III-IV 236 173 1.71 1.15, 2.54 0.008 Unknown 667 546 1.49 1.01, 2.22 0.047 M M0 919 657 — — M1 282 241 2.13 1.80, 2.52 <0.001 Surgery No/Unknown 667 579 — — Yes 534 319 0.39 0.32, 0.47 <0.001 Chemotherapy No/Unknown 603 501 — — Yes 598 397 0.54 0.47, 0.62 <0.001 1 HR = Hazard Ratio, CI = Confidence Interval Furthermore, M stage (M1 vs M0, HR = 2.43, 95% CI 2.08–2.83, p < 0.001), Surgery( Yes vs No/unknown, HR = 0.29, 95%CI 0.25–0.33, p < 0.001), Chemotherapy (Yes vs No/unknown, HR = 0.53, 95%CI 0.46–0.60, p < 0.001) were identified as independent risk factors for predicting survival. Additionally, the survival outcomes for patients with distal cholangiocarcinoma (dCCA) in relation to Grade, Surgery, Chemotherapy, and M stage were illustrated in supplementary figures for the training cohort, internal cohort, and validation cohort. Development of a prediction model for predicting dCCA The ROC analysis of the aforementioned variables yielded area under the AUC curve values exceeding 0.5, as depicted in Fig. 2 . Consequently, these variables were integrated into the construction of a nomogram, as illustrated in Fig. 3 . The AUC measurements of this model for the training and testing groups were presented. The calibration diagrams of the nomogram in the various cohorts are illustrated in the following figures, indicating a robust association between the observed and estimated risk, as depicted in Fig. 4 . These results suggest that the initial nomogram continued to demonstrate effectiveness and accuracy in predicting outcomes when tested on validation sets, closely resembling the ideal curve in the calibration curve. Decision Curve Analysis Decision curve analysis is a novel method that has received approval from many respected academic journals. The net benefit of the prediction models can be computed to assess their clinical utility, a crucial factor for the ultimate implementation of these models in practice. There is not much research in the field of dCCA that uses this method to evaluate the overall benefit of using prediction models because they are relatively new. The DCA curves depicted in Fig. 5 demonstrate the benefits of the nomogram for clinical use, as indicated by this study. It shows that the nomogram provides significant net advantages through its DCA curve. 1.4. DISCUSSION The majority of patients with dCCA face a grim outlook, as the likelihood of recurrence is high and their life expectancy varies widely, posing challenges in determining survival. Due to the relatively low incidence of dCCA, there is a lack of sufficient sample size to construct an accurate prediction model. We obtained a large number of dCCA patients from the SEER database and several hospitals in China. Extrahepatic cholangiocarcinoma is categorized as either perihilar cholangiocarcinoma (pCCA) or distal cholangiocarcinoma (dCCA). At present, many new studies still focus on extrahepatic cholangiocarcinoma[ 17 – 19 ]. However, EASL, ILCA, WHO, and other organizations no longer use the concept of extrahepatic cholangiocarcinoma [ 20 ]. Because pCCA and dCCA are not only different in location but also have different etiology, risk factors, pathobiology, molecular biology and clinical management [ 12 , 21 – 23 ]. Previous studies mainly focused on constructing a nomogram among dCCA patients after surgery. However, the sample size included was too small to affect accuracy, and there was a lack of survival prediction for dCCA patients who did not undergo surgery. Our findings align with the existing literature in some respects. For instance, tumor differentiation has been previously identified as a significant predictor by Ali Belkouz [ 24 ]. In the current study, we created and tested a nomogram to forecast the survival of dCCA using data from 2689 patients. The main predictors incorporated into the nomogram included Grade, M, Surgery, and Chemotherapy, which were statistically significant in Cox regression analysis. Different from some other studies, there are only four variables in our nomogram. It is suggested that our nomogram is very simple and convenient in actual clinical application, and can quickly help medical workers make judgments. Although the SEER database does not provide specific surgical methods and specific methods of chemotherapy. However, it seems that surgical methods and specific chemotherapy do not affect the use of our nomogram. dCCA located in bravery manager Vater ampulla and cystic duct and hepatic duct junction, usually for pancreatic duodenal resection. Such as the classical Whipple (the CW) and pylorus-preserving pancreaticoduodenectomy (PPW). There was no significant difference in the prognosis of patients treated with different surgical procedures [ 25 , 26 ]. For many years, chemotherapy has been the mainstay of first-line treatment for advanced biliary tract tumors. According to the NCCN guidelines, the first-line treatment mainly includes gemcitabine alone or gemcitabine combined with cisplatin. In our external validation set, we found that the majority of dCCA patients received consistent chemotherapy regimens. Recently, new therapies for biliary tract cancer, such as immunotherapy, are attracting more and more attention. PD-L1 can be utilized in individuals who have high microsatellite instability or mismatch repair deficiency. However, immunotherapy for biliary tract cancer is currently in the early stages of investigation. The factors used to create the nomogram in this research were easily accessible, and the nomogram developed using the U.S. group still demonstrated high precision when applied to the Asian group. Hence, they exhibit strong fault tolerance and broad applicability, thereby mitigating the impact of variations in medical expertise across different healthcare facilities on the accuracy of predictive outcomes. For dCCA, surgery is the only way to cure patients, but many patients are not suitable for surgical treatment [ 27 – 29 ]. To the best of our understanding, this research is the initial attempt to develop a prognostic nomogram that applies to all patients with distal cholangiocarcinoma, regardless of whether they have undergone surgical intervention. Furthermore, there is a scarcity of treatment approaches tailored to the dCCA population. The utilization of a nomogram has the potential to identify individuals at high risk and facilitate the development of targeted clinical trials, thereby offering valuable insights for the management of dCCA. 1.5. Limitations Our study has several limitations that should be acknowledged. First, retrospective cohort studies are inevitably subject to selection bias. In order to mitigate this limitation to the greatest extent possible, we incorporated extensive cohorts and partitioned them into a training set and a validation set. The nomogram was developed through randomization and subsequently validated both internally and externally. This approach serves to mitigate potential biases that may arise from retrospective data analysis. While we have utilized external data from various centers for validation, we aim to confirm the generalizability of our nomograms by replicating our findings in extensive data from additional centers in the future. Furthermore, the incorporation of new predictors or biomarkers may improve the predictive precision of the nomogram, thus necessitating additional research. 1.6. Conclusion Based on the SEER database, our study identified independent risk factors associated with death in patients with dCCA. Using the identified risk factors, a predictive nomogram was subsequently constructed and rigorously validated in the Chinese dCCA population. The resulting model shows a satisfactory performance. Thus, these nomograms may have profound clinical implications. Their application can largely help medical workers design comprehensive clinical studies, determine personalized treatment strategies, and modify follow-up programs. Taken together, these factors have the potential to significantly improve survival outcomes in patients with dCCA. Declarations 1.7. Data availability statement The clinical data of this study population were obtained from publicly available datasets. These data are available here: Surveillance, Epidemiology and End Results (SEER) database. 1.8. Ethics statement The study was a retrospective analysis, and it did not necessitate informed consent from the patients. 1.9. Author contributions Jiangfeng Hu, Yang Jiang, Yuwei Dong: manuscript conception, design, data collection and writing. Yuping Shi, Bensong Duan, Lihua Jing: data collection, statistical analysis. Suhong Yi, Jinsuo Chen, Dadong Wan, Jingnan Chen, Weixin Ye, Yajing Zhang: data collection, manuscript proofreading and revision. 1.10. 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Oliveira IS, Kilcoyne A, Everett JM, Mino-Kenudson M, Harisinghani MG, Ganesan K: Cholangiocarcinoma: classification, diagnosis, staging, imaging features, and management . Abdom Radiol (NY) 2017, 42 (6):1637-1649. Buckholz AP, Brown RS, Jr.: Cholangiocarcinoma: Diagnosis and Management . Clin Liver Dis 2020, 24 (3):421-436. Surya H, Abdullah M, Nelwan EJ, Syam AF, Prasetya IB, Stefanus B, Rumende CM, Shatri H: Current Updates on Diagnosis and Management of Cholangiocarcinoma: from Surgery to Targeted Therapy . Acta Med Indones 2023, 55 (3):361-370. Additional Declarations No competing interests reported. Supplementary Files Figuresupplementary.docx 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. 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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-4401724\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":305498557,\"identity\":\"0b8cb78b-ae1a-4cfc-b2ea-fbe7db58e1ec\",\"order_by\":0,\"name\":\"Jiangfeng Hu\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIie3QMQ6CMBSA4ZKXtMsTVwgeAsKuZzFdSTyAgxIGrwAXYXIgaSILB2BwQElwhRvYYowb1M3E/kPToV/aV0JMpl+seK3ATnGjdmhrEOuoCFbCHwnVJQRrPhIyS+wyeTTD+crQba12Ea1XlMDtXk8Qt7oEcdp1gB6HIMu5fBgNw2iC+HVkJVgI2Hi7izPkIAlST4ugK6izzQ/fEAeovEXMEzVLliqC4ywlUpiZRf5Y0/eF4MiE/LF8v1my5NZOkXf8swWN46q15jmTyWT6x57eFEg+NeG6kgAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Shanghai Jiao Tong University School of Medicine\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Jiangfeng\",\"middleName\":\"\",\"lastName\":\"Hu\",\"suffix\":\"\"},{\"id\":305498558,\"identity\":\"baa43ed9-defe-4d1e-9925-ade9bf01e560\",\"order_by\":1,\"name\":\"Yuping Shi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Jiao Tong University School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yuping\",\"middleName\":\"\",\"lastName\":\"Shi\",\"suffix\":\"\"},{\"id\":305498559,\"identity\":\"eb4a76eb-ec4e-458b-8f3f-759de0f528f9\",\"order_by\":2,\"name\":\"Bensong Duan\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tongji University School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Bensong\",\"middleName\":\"\",\"lastName\":\"Duan\",\"suffix\":\"\"},{\"id\":305498560,\"identity\":\"3ec868e8-bfd3-4f0a-a4c4-42118a89f7a4\",\"order_by\":3,\"name\":\"Lihua Jin\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Jiao Tong University School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Lihua\",\"middleName\":\"\",\"lastName\":\"Jin\",\"suffix\":\"\"},{\"id\":305498562,\"identity\":\"e9cd406f-e30f-4743-8d2d-0a305df547ae\",\"order_by\":4,\"name\":\"Suhong Yi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xinyu People’s Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Suhong\",\"middleName\":\"\",\"lastName\":\"Yi\",\"suffix\":\"\"},{\"id\":305498563,\"identity\":\"b01ea712-0cc8-4337-a739-4c9ee6db5b13\",\"order_by\":5,\"name\":\"Jinsuo Chen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Jiangnan University Medical Centre\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jinsuo\",\"middleName\":\"\",\"lastName\":\"Chen\",\"suffix\":\"\"},{\"id\":305498565,\"identity\":\"0e9bb087-6a09-4d88-b370-b967c7ce8402\",\"order_by\":6,\"name\":\"Dadong Wan\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Fuyang Women \\u0026 Children's Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Dadong\",\"middleName\":\"\",\"lastName\":\"Wan\",\"suffix\":\"\"},{\"id\":305498567,\"identity\":\"e0dde4d7-d936-4148-9121-622573a51d59\",\"order_by\":7,\"name\":\"Weixin Ye\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xining Second People's Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Weixin\",\"middleName\":\"\",\"lastName\":\"Ye\",\"suffix\":\"\"},{\"id\":305498569,\"identity\":\"67e88cf7-4f70-435e-a005-5d6f1a038295\",\"order_by\":8,\"name\":\"Jingnan Chen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Jiao Tong University School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jingnan\",\"middleName\":\"\",\"lastName\":\"Chen\",\"suffix\":\"\"},{\"id\":305498572,\"identity\":\"2343fd44-266c-46df-97ed-99b2b71c0323\",\"order_by\":9,\"name\":\"Yajing Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yajing\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":305498574,\"identity\":\"7b46d02a-8949-4273-8028-c411e3931feb\",\"order_by\":10,\"name\":\"Yang Jiang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tongji University School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yang\",\"middleName\":\"\",\"lastName\":\"Jiang\",\"suffix\":\"\"},{\"id\":305498576,\"identity\":\"1330cf97-45be-459b-a910-b099d52d9932\",\"order_by\":11,\"name\":\"Yuwei Dong\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Jiao Tong University School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yuwei\",\"middleName\":\"\",\"lastName\":\"Dong\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-05-10 15:51:38\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4401724/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4401724/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":57720710,\"identity\":\"7e7eae58-58b6-4fd3-a4dc-151b6d8d7494\",\"added_by\":\"auto\",\"created_at\":\"2024-06-04 18:54:15\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":80314,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eA. Flowchart for selection of research subjects B. Lasso Regression Cross-Validation Plot C. \\u0026nbsp;Lasso Regression Coefficient Path Plot\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4401724/v1/b89a0f7b2c7a6248a61fc837.jpg\"},{\"id\":57722289,\"identity\":\"20d4269c-d334-44bd-9fbb-52181336d7d7\",\"added_by\":\"auto\",\"created_at\":\"2024-06-04 19:02:15\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":58109,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe AUCs of the model in the different cohorts were shown. A. ROC curves of the nomogram prediction model in training cohort. B. ROC curves of the nomogram prediction model in internal test cohort. C. ROC curves of the nomogram prediction model in external test cohort\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4401724/v1/cee5c34d02d46cba4c5a54db.jpg\"},{\"id\":57722724,\"identity\":\"6d3132bb-8d08-46e3-bbe2-ddadf3af444a\",\"added_by\":\"auto\",\"created_at\":\"2024-06-04 19:10:16\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":51556,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eNomogram prediction model of dCCA\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4401724/v1/abe00c1ccb742873d3632a02.jpg\"},{\"id\":57720712,\"identity\":\"954afa38-1dae-43c8-99e9-cac06cfe1e67\",\"added_by\":\"auto\",\"created_at\":\"2024-06-04 18:54:15\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":79725,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCalibration curves for overall survival nomogram: A. 1 year survival probability in training cohort B. 3 year survival probability in training cohort C.5 year survival probability in training cohort D. 1 year survival probability in validation cohort E. 3 year survival probability in validation cohort F. 5 year survival probability in validation cohort G. 1 year survival probability in external cohort H. 3 year survival probability in external cohort I. 5 year survival probability\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4401724/v1/f88a5ae10f62144470b7edd4.jpg\"},{\"id\":57720715,\"identity\":\"f403ec25-b379-42b2-a9e8-b973ac650d49\",\"added_by\":\"auto\",\"created_at\":\"2024-06-04 18:54:15\",\"extension\":\"jpg\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":83841,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSee image above for figure legend\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4401724/v1/597a43af64ce81c8a9f39018.jpg\"},{\"id\":61399461,\"identity\":\"5e9e20fe-286a-4ee9-a2b3-4383da650e88\",\"added_by\":\"auto\",\"created_at\":\"2024-07-30 09:36:24\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2187143,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4401724/v1/92e7bbc4-9aef-4bb8-8b47-e0827f14c858.pdf\"},{\"id\":57722291,\"identity\":\"6519850c-0123-4df9-9903-9ff80a8fb5e0\",\"added_by\":\"auto\",\"created_at\":\"2024-06-04 19:02:15\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":581177,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Figuresupplementary.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4401724/v1/07d632d4bfe4ad806e1e8555.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Predicting Survival Rates: The Power of Prognostic Nomograms in Distal Cholangiocarcinoma\",\"fulltext\":[{\"header\":\"1.1. INTRODUCTION\",\"content\":\"\\u003cp\\u003eCholangiocarcinoma is an infrequent yet highly aggressive cancer that originates from the biliary epithelium, with an estimated yearly occurrence of about 1\\u0026ndash;2 cases per 100,000 people in Western nations [\\u003cspan class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. The disease is classified based on its anatomical location into intrahepatic, perihilar, and distal cholangiocarcinoma, with distal cholangiocarcinoma accounting for approximately 20\\u0026ndash;30% of all cholangiocarcinoma cases [\\u003cspan class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003cp\\u003eSurgical intervention continues to be the primary approach for curative management of distal cholangiocarcinoma and negative surgical margins are the most important predictor of long-term survival [\\u003cspan class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Despite curative-intent resection, many patients experience disease recurrence, necessitating the use of adjuvant therapies such as chemotherapy to improve outcomes [\\u003cspan class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u0026ndash;\\u003cspan class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003cp\\u003eThe TNM staging system, which integrates tumor dimensions (T), lymph node participation (N), and distant metastasis (M), is frequently employed for prognostic purposes in cholangiocarcinoma. However, this system has limitations in predicting individual patient outcomes and guiding treatment decisions [\\u003cspan class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. In recent times, there have been endeavors to identify additional prognostic factors and develop prediction models to improve the accuracy of survival predictions in patients with cholangiocarcinoma.\\u003c/p\\u003e\\n\\u003cp\\u003eNumerous research studies have examined the predictive importance of clinicopathological factors in distal cholangiocarcinoma. These factors encompass direct bilirubin (DBIL), perineural invasion, lymph node metastasis, resection margin status, tumor size, and various others [\\u003cspan class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u0026ndash;\\u003cspan class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Additionally, molecular markers such as KRAS mutations and programmed death-ligand 1 (PD-L1) have been explored as potential prognostic indicators and targets for novel therapies in cholangiocarcinoma [\\u003cspan class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u0026ndash;\\u003cspan class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003cp\\u003eIn clinical practice, accurate prediction of survival outcomes is crucial for treatment planning and counseling of patients with distal cholangiocarcinoma. Nomograms, which are graphical representations of prediction models, have been increasingly utilized to estimate individualized survival probabilities based on multiple prognostic factors[\\u003cspan class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Furthermore, the use of nomograms allows for the integration of diverse prognostic variables and facilitates risk stratification in clinical decision-making[\\u003cspan class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003cp\\u003eDue to the intricate nature and constraints of existing prognostic instruments for distal cholangiocarcinoma, there is a requirement for comprehensive clinical prediction models that can effectively categorize patients according to their prognosis. In this study, we aim to develop and validate a prediction model for survival outcomes in patients with distal cholangiocarcinoma, incorporating a range of clinicopathological and treatment-related factors. The use of Cox regression and the construction of a nomogram will enable us to create a practical tool for predicting individualized survival probabilities in this patient population.\\u003c/p\\u003e\"},{\"header\":\"1.2. MATERIALS AND METHODS\",\"content\":\"\\u003ch2\\u003e1.2.1. Patient Data\\u003c/h2\\u003e\\u003cp\\u003eThe data utilized in this study were acquired from SEER∗Stat (version 8.4.0.1). Inclusion criteria for patients encompassed the following: (1) the year of diagnosis falling from 2010 to 2020, (2) site code: C24.0, and ICD-O- histology/behavior codes: 8140,8160. Patients who satisfied any of the specified criteria were excluded from the study: (1) those with a second primary cancer, and (2) those with a survival period of less than 30 days. Some of the variables were regrouped for the analysis. Patients were re-evaluated based on the staging principles outlined in the eighth edition of the American Joint Committee on Cancer guidelines. Overall survival was defined as the duration between the date of diagnosis and the date of death from any cause or the date of the last follow-up visit.\\u003c/p\\u003e\\u003cp\\u003eFurthermore, the external validation set of this study was obtained from 9 hospitals in China, including Shanghai General Hospital, Shanghai Tenth People's Hospital, Shanghai East Hospital, etc. The flowchart shows the whole process of data screening and data analysis in this study (Figure \\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003ch2\\u003e1.2.2. Data Collection\\u003c/h2\\u003e\\u003cp\\u003eThe following demographic and clinical data were obtained in this research: Age, Sex, Race, Grade, TNM stage, Bone metastases, Brain metastases, Liver metastases, Lung metastases, Surgery, Radiation, Chemotherapy, and Marital. The data of outcome and vital status, were also collected.\\u003c/p\\u003e\\u003ch2\\u003e1.2.3. Statistical Analysis\\u003c/h2\\u003e\\u003cp\\u003eThe dataset was partitioned into training and validation cohorts randomly, with a 1:1 ratio, and the variables were subsequently analyzed for comparison. In the univariate analysis, categorical variables were analyzed using either the chi-square test or Fisher's exact test, while continuous variables were examined using either the Student’s t-test or rank-sum test. In the training cohort, multivariate analysis was conducted using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to ascertain independent risk factors of dCCA and construct a prediction nomogram for the survival probability of dCCA. The nomogram's performance was assessed by employing the ROC curve and calibration curve. The AUC curve was utilized to measure the discriminant ability, with values ranging from 0.5 (indicating no discriminant ability) to 1 (indicating complete discriminant ability). Additionally, a decision curve analysis (DCA) was conducted to establish the net benefit threshold of prediction. Survival analysis was conducted using the Kaplan-Meier method. Statistical significance was determined by results with a p-value of less than 0.05. All statistical analyses were carried out using the R software (version 4.2.2).\\u003c/p\\u003e\"},{\"header\":\"1.3. RESULTS\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003ePatient Characteristics\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn this study, we analyzed the initial demographic and clinical characteristics of 2,689 individuals across different cohorts (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The overall distribution of age showed a significant difference (p\\u0026thinsp;=\\u0026thinsp;0.028) among the cohorts, with the percentage of patients aged\\u0026thinsp;\\u0026lt;\\u0026thinsp;55 varying from 9.3% in the training cohort to 15.6% in the external test cohort. Sex distribution did not differ significantly (p\\u0026thinsp;=\\u0026thinsp;0.132) between the cohorts, with females representing 42.4\\u0026ndash;49.0% of the different groups. In terms of race, a significant difference (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) was observed, with the proportion of White patients ranging from 73.9% in the training cohort to 0 in the external test cohort. The distribution of tumor grade also varied significantly (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) across the cohorts, with the proportion of Grade III-IV tumors ranging from 15.8\\u0026ndash;30.2%. Tumor stage (T) did not show significant differences across the cohorts (p\\u0026thinsp;=\\u0026thinsp;0.252), with T3 tumors being the most prevalent in all groups. Lymph node involvement (N) also did not differ significantly (p\\u0026thinsp;=\\u0026thinsp;0.625) between the cohorts. Most patients were N0, with proportions ranging from 52.4\\u0026ndash;57.1% across the cohorts. The presence of distant metastases (M) showed a significant difference (p\\u0026thinsp;=\\u0026thinsp;0.010) across the cohorts, with the proportion of M1 disease ranging from 21.5\\u0026ndash;29.9%. The distribution of liver and lung metastases did not differ significantly across the cohorts, with proportions ranging from 76.0\\u0026ndash;82.3% for liver metastases and from 92.4\\u0026ndash;93.5% for lung metastases. The proportions of patients who received surgery, chemotherapy, and radiation did not differ significantly across the cohorts, with all p-values\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05. Marital status also did not differ significantly (p\\u0026thinsp;=\\u0026thinsp;0.444) across the cohorts, with the majority of patients being married in all groups, ranging from 55.1\\u0026ndash;57.8%. These findings indicate variability in demographic and clinical characteristics across different cohorts, highlighting the importance of considering these factors in predictive research.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 1. Demographic and clinical characteristics of patients across different cohors\\u003c/p\\u003e\\n\\u003ctable id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eCharacteristic\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"4\\\"\\u003e\\n \\u003cp\\u003eCohort\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003ep-value\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eOverall, N\\u0026thinsp;=\\u0026thinsp;2,689\\u003csup\\u003e1\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTraining Cohort, N\\u0026thinsp;=\\u0026thinsp;1,201\\u003csup\\u003e1\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eInternal Test Cohort, N\\u0026thinsp;=\\u0026thinsp;1,200\\u003csup\\u003e1\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eExternal Test Cohort, N\\u0026thinsp;=\\u0026thinsp;288\\u003csup\\u003e1\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAge\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.028\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;55\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e278 (10.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e112 (9.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e121 (10.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e45 (15.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e55\\u0026ndash;64\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e574 (21.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e261 (21.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e264 (22.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e49 (17.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e65\\u0026ndash;74\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e790 (29.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e361 (30.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e355 (29.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e74 (25.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026ge;\\u0026thinsp;75\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,047 (38.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e467 (38.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e460 (38.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e120 (41.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSex\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.132\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,176 (43.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e526 (43.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e509 (42.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e141 (49.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,513 (56.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e675 (56.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e691 (57.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e147 (51.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eRace\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWhite\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,804 (67.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e887 (73.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e917 (76.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0 (0.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBlack\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e211 (7.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e110 (9.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e101 (8.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0 (0.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAsian\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e674 (25.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e204 (17.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e182 (15.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e288 (100.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGrade\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGrade I\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e136 (5.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e48 (4.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e72 (6.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e16 (5.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGrade II\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e609 (22.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e250 (20.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e265 (22.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e94 (32.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGrade III-IV\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e513 (19.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e236 (19.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e190 (15.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e87 (30.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUnknown\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,431 (53.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e667 (55.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e673 (56.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e91 (31.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eT\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.252\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e413 (15.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e176 (14.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e190 (15.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e47 (16.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eT2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e254 (9.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e105 (8.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e110 (9.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e39 (13.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eT3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,011 (37.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e465 (38.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e439 (36.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e107 (37.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eT4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e250 (9.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e119 (9.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e110 (9.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e21 (7.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTX\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e761 (28.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e336 (28.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e351 (29.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e74 (25.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eN\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.625\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eN0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,502 (55.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e666 (55.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e685 (57.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e151 (52.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eN1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e665 (24.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e300 (25.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e282 (23.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e83 (28.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eN2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e92 (3.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e38 (3.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e44 (3.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e10 (3.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNX\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e430 (16.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e197 (16.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e189 (15.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e44 (15.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eM\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.010\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eM0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2,063 (76.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e919 (76.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e942 (78.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e202 (70.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eM1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e626 (23.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e282 (23.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e258 (21.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e86 (29.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eBone metastases\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.827\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2,547 (94.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,132 (94.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,142 (95.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e273 (94.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e49 (1.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e22 (1.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e21 (1.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6 (2.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUnknown\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e93 (3.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e47 (3.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e37 (3.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9 (3.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eBrain metastases\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.023\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2,589 (96.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,150 (95.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,162 (96.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e277 (96.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (0.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0 (0.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0 (0.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (0.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUnknown\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e98 (3.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e51 (4.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e38 (3.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9 (3.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLiver metastases\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.047\\u003c/p\\u003e\\n 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align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e412 (15.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e180 (15.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e171 (14.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e61 (21.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUnknown\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e98 (3.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e48 (4.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e42 (3.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8 (2.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLung metastases\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.715\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2,501 (93.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,113 (92.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,122 (93.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e266 (92.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e85 (3.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e36 (3.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e38 (3.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e11 (3.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUnknown\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e103 (3.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e52 (4.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e40 (3.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e11 (3.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSurgery\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.864\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo/Unknown\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,506 (56.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e667 (55.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e679 (56.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e160 (55.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,183 (44.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e534 (44.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e521 (43.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e128 (44.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eChemotherapy\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.457\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo/Unknown\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,363 (50.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e603 (50.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e604 (50.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e156 (54.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,326 (49.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e598 (49.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e596 (49.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e132 (45.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eRadiation\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.520\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo/Unknown\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2,148 (79.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e963 (80.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e949 (79.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e236 (81.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e541 (20.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e238 (19.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e251 (20.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e52 (18.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMarital\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.444\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMarried\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,521 (56.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e662 (55.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e693 (57.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e166 (57.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSingle\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e361 (13.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e167 (13.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e150 (12.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e44 (15.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eOther\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e807 (30.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e372 (31.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e357 (29.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e78 (27.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\"\\u003e\\n \\u003cp\\u003e\\u003csup\\u003e1\\u003c/sup\\u003en (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\"\\u003e\\n \\u003cp\\u003e\\u003csup\\u003e2\\u003c/sup\\u003ePearson\\u0026apos;s Chi-squared test; Fisher\\u0026apos;s exact test\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eThe Results of Univariate Cox regression\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFirstly, a univariate analysis was employed to conduct an initial screening of the variables included in the study. A significance level of P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was utilized. The findings of the univariate regression analysis are detailed in Table \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. It was observed that variables such as age, sex, T stage, N stage, M stage, surgery, chemotherapy, radiotherapy, bone metastasis, brain metastasis, lung metastasis, liver metastasis, and marital status may potentially serve as independent risk factors for overall survival.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 2. Results of Univariate Cox regression\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCharacteristic\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eN\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEvent N\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHR\\u003c/strong\\u003e\\u003csup\\u003e1\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e95% CI\\u003c/strong\\u003e\\u003csup\\u003e1\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ep-value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eAge\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;55\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e112\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e78\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e55-64\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e261\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e176\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.94\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.72, 1.22\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.626\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e65-74\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e361\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e259\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.81, 1.34\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.755\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026ge;75\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e467\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e385\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.61\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.26, 2.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eSex\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eFemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e526\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e423\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eMale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e675\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e475\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.81\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.71, 0.92\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.002\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eRace\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eWhite\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e887\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e672\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBlack\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e110\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e82\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.86, 1.36\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.490\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eAsian\\u003c/p\\u003e\\n 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width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eM1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e282\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e241\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e2.43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e2.08, 2.83\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd 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valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1,113\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e816\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e36\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e3.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e2.14, 4.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eUnknown\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e52\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e49\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.71\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.28, 2.29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eSurgery\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eNo/Unknown\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e667\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e579\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e534\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e319\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.25, 0.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eRadiation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eNo/Unknown\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e963\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e736\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e238\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e162\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.56\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.47, 0.66\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eChemotherapy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eNo/Unknown\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e603\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e501\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e598\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e397\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.53\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.46, 0.60\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eMarital\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eMarried\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e662\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e472\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eSingle\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e167\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e125\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.36\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.12, 1.66\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.002\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"28.688524590163933%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eOther\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.021857923497267%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e372\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.846994535519126%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e301\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.109289617486338%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.54\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.0327868852459%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.34, 1.79\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.300546448087431%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"100%\\\" colspan=\\\"6\\\"\\u003e\\n \\u003cp\\u003e\\u003csup\\u003e1\\u003c/sup\\u003eHR = Hazard Ratio, CI = Confidence Interval\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMultivariate analysis of risk factors for dCCA\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe original model included the candidate predictors of Age, Sex, T stage, N stage, M stage, Surgery, Chemotherapy, Radiotherapy, Bone metastasis, Brain metastasis, Lung metastasis, Liver metastasis, and Marital status. The training cohort was subjected to LASSO regression analysis to determine the significant variables. Figure \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB depicts the Cross-Validation Plot for Lasso Regression, with the optimal log\\u0026lambda; indicated by the intersection of the red dotted vertical line, denoting the minimum value for the LASSO regression model. The two dotted lines represent one standard deviation from the minimum value. Figure \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC illustrates the LASSO coefficient profiles of the potential factors. Each curve corresponds to a coefficient, with the x-axis representing the regularization penalty parameter.\\u003c/p\\u003e\\n\\u003cp\\u003eAs \\u0026lambda; changes, a coefficient that becomes non-zero enters the LASSO regression model. The resultant model includes four potential predictors: Grade, M stage, Surgery, and Chemotherapy. The study revealed that all four variables were individually able to predict the survival of dCCA. According to Table \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, patients with Grade III-IV exhibited poorer survival rates in comparison to those with Grade I (HR\\u0026thinsp;=\\u0026thinsp;1.50, 95% CI 1.01\\u0026ndash;2.22, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05).\\u003c/p\\u003e\\n\\u003cp\\u003eTable 3: Results of Multivariate Cox regression for Training Cohort\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"0%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCharacteristic\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eN\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEvent N\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHR\\u003c/strong\\u003e\\u003csup\\u003e1\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e95% CI\\u003c/strong\\u003e\\u003csup\\u003e1\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ep-value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eGrade\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eGrade I\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e48\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eGrade II\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e250\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e150\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.79, 1.75\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.421\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eGrade III-IV\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e236\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e173\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.71\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.15, 2.54\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.008\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eUnknown\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e667\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e546\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.49\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.01, 2.22\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.047\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eM0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e919\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e657\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eM1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e282\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e241\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e2.13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.80, 2.52\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eSurgery\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eNo/Unknown\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e667\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e579\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e534\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e319\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.39\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.32, 0.47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eChemotherapy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eNo/Unknown\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e603\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e501\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e598\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e397\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.54\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.47, 0.62\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003csup\\u003e1\\u003c/sup\\u003eHR = Hazard Ratio, CI = Confidence Interval\\u003c/p\\u003e\\n\\u003cp\\u003eFurthermore, M stage (M1 vs M0, HR\\u0026thinsp;=\\u0026thinsp;2.43, 95% CI 2.08\\u0026ndash;2.83, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), Surgery( Yes vs No/unknown, HR\\u0026thinsp;=\\u0026thinsp;0.29, 95%CI 0.25\\u0026ndash;0.33, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), Chemotherapy (Yes vs No/unknown, HR\\u0026thinsp;=\\u0026thinsp;0.53, 95%CI 0.46\\u0026ndash;0.60, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) were identified as independent risk factors for predicting survival. Additionally, the survival outcomes for patients with distal cholangiocarcinoma (dCCA) in relation to Grade, Surgery, Chemotherapy, and M stage were illustrated in supplementary figures for the training cohort, internal cohort, and validation cohort.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDevelopment of a prediction model for predicting dCCA\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe ROC analysis of the aforementioned variables yielded area under the AUC curve values exceeding 0.5, as depicted in Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. Consequently, these variables were integrated into the construction of a nomogram, as illustrated in Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. The AUC measurements of this model for the training and testing groups were presented. The calibration diagrams of the nomogram in the various cohorts are illustrated in the following figures, indicating a robust association between the observed and estimated risk, as depicted in Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. These results suggest that the initial nomogram continued to demonstrate effectiveness and accuracy in predicting outcomes when tested on validation sets, closely resembling the ideal curve in the calibration curve.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDecision Curve Analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eDecision curve analysis is a novel method that has received approval from many respected academic journals. The net benefit of the prediction models can be computed to assess their clinical utility, a crucial factor for the ultimate implementation of these models in practice. There is not much research in the field of dCCA that uses this method to evaluate the overall benefit of using prediction models because they are relatively new. The DCA curves depicted in Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e demonstrate the benefits of the nomogram for clinical use, as indicated by this study. It shows that the nomogram provides significant net advantages through its DCA curve.\\u003c/p\\u003e\"},{\"header\":\"1.4. DISCUSSION\",\"content\":\"\\u003cp\\u003eThe majority of patients with dCCA face a grim outlook, as the likelihood of recurrence is high and their life expectancy varies widely, posing challenges in determining survival. Due to the relatively low incidence of dCCA, there is a lack of sufficient sample size to construct an accurate prediction model. We obtained a large number of dCCA patients from the SEER database and several hospitals in China. Extrahepatic cholangiocarcinoma is categorized as either perihilar cholangiocarcinoma (pCCA) or distal cholangiocarcinoma (dCCA). At present, many new studies still focus on extrahepatic cholangiocarcinoma[\\u003cspan additionalcitationids=\\\"CR18\\\" citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e–\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. However, EASL, ILCA, WHO, and other organizations no longer use the concept of extrahepatic cholangiocarcinoma [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. Because pCCA and dCCA are not only different in location but also have different etiology, risk factors, pathobiology, molecular biology and clinical management [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR22\\\" citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e–\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003ePrevious studies mainly focused on constructing a nomogram among dCCA patients after surgery. However, the sample size included was too small to affect accuracy, and there was a lack of survival prediction for dCCA patients who did not undergo surgery. Our findings align with the existing literature in some respects. For instance, tumor differentiation has been previously identified as a significant predictor by Ali Belkouz [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. In the current study, we created and tested a nomogram to forecast the survival of dCCA using data from 2689 patients. The main predictors incorporated into the nomogram included Grade, M, Surgery, and Chemotherapy, which were statistically significant in Cox regression analysis. Different from some other studies, there are only four variables in our nomogram. It is suggested that our nomogram is very simple and convenient in actual clinical application, and can quickly help medical workers make judgments.\\u003c/p\\u003e\\u003cp\\u003eAlthough the SEER database does not provide specific surgical methods and specific methods of chemotherapy. However, it seems that surgical methods and specific chemotherapy do not affect the use of our nomogram. dCCA located in bravery manager Vater ampulla and cystic duct and hepatic duct junction, usually for pancreatic duodenal resection. Such as the classical Whipple (the CW) and pylorus-preserving pancreaticoduodenectomy (PPW). There was no significant difference in the prognosis of patients treated with different surgical procedures [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. For many years, chemotherapy has been the mainstay of first-line treatment for advanced biliary tract tumors. According to the NCCN guidelines, the first-line treatment mainly includes gemcitabine alone or gemcitabine combined with cisplatin. In our external validation set, we found that the majority of dCCA patients received consistent chemotherapy regimens. Recently, new therapies for biliary tract cancer, such as immunotherapy, are attracting more and more attention. PD-L1 can be utilized in individuals who have high microsatellite instability or mismatch repair deficiency. However, immunotherapy for biliary tract cancer is currently in the early stages of investigation. The factors used to create the nomogram in this research were easily accessible, and the nomogram developed using the U.S. group still demonstrated high precision when applied to the Asian group. Hence, they exhibit strong fault tolerance and broad applicability, thereby mitigating the impact of variations in medical expertise across different healthcare facilities on the accuracy of predictive outcomes. For dCCA, surgery is the only way to cure patients, but many patients are not suitable for surgical treatment [\\u003cspan additionalcitationids=\\\"CR28\\\" citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e–\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. To the best of our understanding, this research is the initial attempt to develop a prognostic nomogram that applies to all patients with distal cholangiocarcinoma, regardless of whether they have undergone surgical intervention.\\u003c/p\\u003e\\u003cp\\u003eFurthermore, there is a scarcity of treatment approaches tailored to the dCCA population. The utilization of a nomogram has the potential to identify individuals at high risk and facilitate the development of targeted clinical trials, thereby offering valuable insights for the management of dCCA.\\u003c/p\\u003e\\u003ch2\\u003e1.5. Limitations\\u003c/h2\\u003e\\u003cp\\u003eOur study has several limitations that should be acknowledged. First, retrospective cohort studies are inevitably subject to selection bias. In order to mitigate this limitation to the greatest extent possible, we incorporated extensive cohorts and partitioned them into a training set and a validation set. The nomogram was developed through randomization and subsequently validated both internally and externally. This approach serves to mitigate potential biases that may arise from retrospective data analysis.\\u003c/p\\u003e\\u003cp\\u003eWhile we have utilized external data from various centers for validation, we aim to confirm the generalizability of our nomograms by replicating our findings in extensive data from additional centers in the future. Furthermore, the incorporation of new predictors or biomarkers may improve the predictive precision of the nomogram, thus necessitating additional research.\\u003c/p\\u003e\"},{\"header\":\"1.6. Conclusion\",\"content\":\"\\u003cp\\u003eBased on the SEER database, our study identified independent risk factors associated with death in patients with dCCA. Using the identified risk factors, a predictive nomogram was subsequently constructed and rigorously validated in the Chinese dCCA population. The resulting model shows a satisfactory performance. Thus, these nomograms may have profound clinical implications. Their application can largely help medical workers design comprehensive clinical studies, determine personalized treatment strategies, and modify follow-up programs. Taken together, these factors have the potential to significantly improve survival outcomes in patients with dCCA.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003e1.7. Data availability statement\\u003c/h2\\u003e\\n\\u003cp\\u003eThe clinical data of this study population were obtained from publicly available datasets. These data are available here: Surveillance, Epidemiology and End Results (SEER) database.\\u003c/p\\u003e\\n\\u003ch2\\u003e1.8. Ethics statement\\u003c/h2\\u003e\\n\\u003cp\\u003eThe study was a retrospective analysis, and it did not necessitate informed consent from the patients.\\u003c/p\\u003e\\n\\u003ch2\\u003e1.9. Author contributions\\u003c/h2\\u003e\\n\\u003cp\\u003eJiangfeng Hu, Yang Jiang, Yuwei Dong: manuscript conception, design, data collection and writing. \\u0026nbsp;Yuping Shi, Bensong Duan, Lihua Jing: data collection, statistical analysis. Suhong Yi, Jinsuo Chen, Dadong Wan, Jingnan Chen, Weixin Ye, Yajing Zhang: data collection, manuscript proofreading and revision.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ch2\\u003e1.10. Funding\\u003c/h2\\u003e\\n\\u003cp\\u003eThe study received financial support from the National Natural Science Foundation of China ( 81900544，82200689).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ch2\\u003e1.11. 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S, Wu L, Wan T: \\u003cstrong\\u003eSurvival after surgical resection of distal cholangiocarcinoma: A systematic review and meta-analysis of prognostic factors\\u003c/strong\\u003e. \\u003cem\\u003eAsian J Surg \\u003c/em\\u003e2017, \\u003cstrong\\u003e40\\u003c/strong\\u003e(2):129-138.\\u003c/li\\u003e\\n\\u003cli\\u003eGorji L, Beal EW: \\u003cstrong\\u003eSurgical Treatment of Distal Cholangiocarcinoma\\u003c/strong\\u003e. \\u003cem\\u003eCurr Oncol \\u003c/em\\u003e2022, \\u003cstrong\\u003e29\\u003c/strong\\u003e(9):6674-6687.\\u003c/li\\u003e\\n\\u003cli\\u003eOliveira IS, Kilcoyne A, Everett JM, Mino-Kenudson M, Harisinghani MG, Ganesan K: \\u003cstrong\\u003eCholangiocarcinoma: classification, diagnosis, staging, imaging features, and management\\u003c/strong\\u003e. \\u003cem\\u003eAbdom Radiol (NY) \\u003c/em\\u003e2017, \\u003cstrong\\u003e42\\u003c/strong\\u003e(6):1637-1649.\\u003c/li\\u003e\\n\\u003cli\\u003eBuckholz AP, Brown RS, Jr.: \\u003cstrong\\u003eCholangiocarcinoma: Diagnosis and Management\\u003c/strong\\u003e. \\u003cem\\u003eClin Liver Dis \\u003c/em\\u003e2020, \\u003cstrong\\u003e24\\u003c/strong\\u003e(3):421-436.\\u003c/li\\u003e\\n\\u003cli\\u003eSurya H, Abdullah M, Nelwan EJ, Syam AF, Prasetya IB, Stefanus B, Rumende CM, Shatri H: \\u003cstrong\\u003eCurrent Updates on Diagnosis and Management of Cholangiocarcinoma: from Surgery to Targeted Therapy\\u003c/strong\\u003e. \\u003cem\\u003eActa Med Indones \\u003c/em\\u003e2023, \\u003cstrong\\u003e55\\u003c/strong\\u003e(3):361-370.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"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\":\"info@researchsquare.com\",\"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\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4401724/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4401724/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eObjective\\u003c/strong\\u003e: The purpose of this research is to establish a prognostic nomogram for\\u003c/p\\u003e\\n\\u003cp\\u003epatients with distal cholangiocarcinoma（dCCA）.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods\\u003c/strong\\u003e: We obtained clinical data from 2401 patients diagnosed with distal cholangiocarcinoma (dCCA) between 2010 and 2020 from the Surveillance, Epidemiology, and End Results database. These patients were randomly assigned to either the training or validation group in a ratio of 1:1. 228 patients were enrolled from 9 hospitals in China as the external validation cohort. Univariate and multifactorial Cox regression analyses were conducted to ascertain prognostic factors and prognostic nomograms were developed utilizing LASSO logistic regression analysis.\\u003c/p\\u003e\\n\\u003cp\\u003eWe used the calibration curve, and area under the curve to validate the nomograms. Decision curve analysis was used to evaluate the model and its clinical applicability.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults:\\u003c/strong\\u003e The findings demonstrated that Grade, M stages, Surgery, and Chemotherapy emerged as autonomous prognostic factors for the survival of individuals with dCCA. The developed nomograms exhibited satisfactory accuracy in forecasting 1-year, 3-year, and 5-year survival probabilities. Furthermore, the calibration curves indicated a strong concordance between the anticipated and observed outcomes.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusion：\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe nomograms that have been suggested demonstrate strong predictive capability. These tools can assist medical professionals in assessing the prognosis of patients with dCCA and in devising more accurate treatment strategies for them.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Predicting Survival Rates: The Power of Prognostic Nomograms in Distal Cholangiocarcinoma\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-06-04 18:54:10\",\"doi\":\"10.21203/rs.3.rs-4401724/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"5b31984f-dfeb-4586-9e4e-41f58da08e86\",\"owner\":[],\"postedDate\":\"June 4th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-07-30T09:28:16+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-06-04 18:54:10\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4401724\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4401724\",\"identity\":\"rs-4401724\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}