Clinical features, treatment and prognosis analysis of distant metastatic esophageal cancer

Clinical features, treatment and prognosis analysis of distant metastatic esophageal cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Clinical features, treatment and prognosis analysis of distant metastatic esophageal cancer Shuang Li, Yanwei Lu, Ruiqi Liu, Luanluan Huang, Ding Nan, Xiaoyan Chen, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4666614/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Backgrounds : Esophageal cancer (EC) is one of the most common malignant tumors in China. EC is characterized by poor clinical prognosis, with many patients being diagnosed at advanced stages. This study utilized data from the the Surveillance, Epidemiology, and End Results (SEER) database. The clinical features, treatment and prognostic factors of patients with distant metastatic esophageal cancer were screened and analyzed, a nomogram was drawn to construct a prognostic model. Methods Eligible patients with distant metastatic esophageal cancer diagnosed from January 2004 to December 2015 were extracted from SEER database. Propensity score matching(PSM)was used to eliminate baseline differences between groups. The data were divided into training cohort (1116 cases) and validation cohort (426 cases) by using R software and random sampling function at the ratio of 7: 3. The baseline table was plotted using x2 or Fisher's exact test. Kaplan-Meier curve, log rank test and Cox regression were used for survival analysis. C index and AUC were used to evaluate the performance of prognosis model. Calibration curve was used to evaluate the calibration of the model. Using the data of the validation cohort, external validation is used to create prediction model. Results After applying the inclusion and exclusion criteria and PSM, a total of 1,542 cases diagnosed between 2004 and 2015 were included in the study. Before and after PSM, we analyzed Kaplan-Meier survival of patients with metastatic esophageal cancer with different treatment methods. The results showed that radiotherapy, chemotherapy or surgical treatment brought significant survival benefits to patients with metastatic esophageal cancer(P < 0.05). Univariate and Multivariate regression analysis showed that T-stage, M-stage, primary site, surgery, chemotherapy and radiotherapy were independent prognostic factors affecting the prognosis of distant metastatic oesophageal cancer (P < 0.05). Evaluating the predictive ability of nomogram, the C index of the training cohort was 0.69(95%CI:0.67–0.71), and the C index of the validation cohort was 0.659 (95% CI:0.627–0.693). The AUC values for the training and validation cohort for the 1-year OS ranged from 0.50 to 0.70, and the AUC for the rest of the training and validation cohort ranged from 0.70 to 0.90, which suggests that the model is moderately discriminating. The calibration curves of 1 year, 2 years and 3 years in the two groups are very close to the 45°reference line, suggesting that the models exhibit a good degree of calibration. The C-index, AUC and calibration curves suggest that the models have good discriminating and calibration. Conclusion The results reveal that T stage, M stage, primary tumor site, surgery, chemotherapy and radiotherapy play an important role in influencing the treatment effect and prognosis of patients. The nomogram prediction model based on the above independent risk factors shows good discriminating and calibration. Biological sciences/Cancer/Cancer models Health sciences/Oncology/Cancer Biological sciences/Cancer Health sciences/Diseases Health sciences/Oncology Distant metastatic oesophageal cancer PSM SEER database Prediction model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Esophageal cancer is the seventh most common malignant tumor in the world, ranking sixth among all cancer-related causes of death [ 1 ] . Esophageal cancer was found to be the fourth leading cause of cancer deaths in Chinese men over the age of 45 in the National Cancer Center Registry in 2016 [ 2 ] . China is a new case of esophageal cancer, accounting for more than half of the global esophageal cancer cases [ 3 ] .The main histological subtype of esophageal cancer is squamous cell carcinoma [ 2 ] . In 2020, the number of new cases of esophageal cancer will reach 604,000 and the number of deaths will reach 544,000 [ 1 ] . The prognosis of esophageal cancer is extremely poor, and the 5-year survival rate is about 20%. Esophageal cancer is usually diagnosed in the late stage, mainly due to the lack of early clinical symptoms. With the continuous progress of comprehensive treatment of esophageal cancer, the prognosis of esophageal cancer has improved by means of endoscopic treatment, surgical techniques, radiotherapy, chemotherapy, immunotherapy and targeted therapy. Identifying, exploring and intervening all potential risk factors may reduce the incidence of esophageal cancer. The risk factors of esophageal cancer include gastroesophageal reflux disease, obesity, smoking, drinking hot tea and alcohol, eating pickled vegetables, low fruit intake, hot food, drinks, malnutrition and low social and economic status [ 4 , 5 ] . Studies have shown that Barrett's esophagus is also an important risk factor for esophageal cancer. The risk factors of esophageal squamous cell carcinoma and adenocarcinoma are still different [ 4 ] . Therefore, identifying risk factors and establishing a prediction models can benefit patients. It can also help clinicians to make more appropriate medical decisions for patients. Studies have shown that concurrent chemoradiotherapy(CCRT) is the first choice for unresectable locally advanced esophageal cancer [ 6 ] . At present, 5- fluorouracil or capecitabine combined with oxaliplatin or cisplatin is commonly used to treat locally advanced or metastatic esophageal squamous cell carcinoma [ 7 ] . One study found that definitive radiotherapy improved overall survival (OS) in newly diagnosed metastatic esophageal cancer [ 8 ] . Short-term radiotherapy is used to relieve dysphagia, malnutrition and chronic bleeding in esophageal cancer [ 9 ] . Comprehensive treatment is very important for the relief of esophageal cancer. Endoscopic therapy and surgical intervention for early tumors. Chemotherapy, targeted drug therapy and immunotherapy are used for the treatment of unresectable locally advanced or advanced patients. Currently there are fewer drugs for targeted therapy of esophageal cancer, mostly targeting human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR), and inositol 3-kinase/mammalian target of rapamycin (PI3K/mTOR) [ 10 ] .It is reported that adoptive T cell transfer technology is also used for distant metastatic esophageal cancer [ 11 ] . Of course, it also needs a lot of clinical trial data to verify. Clinical trials such as KEYNOTE-590, CheckMate648, KEYNOTE-811 and ATTRACTION-3 all show that immunotherapy can improve the prognosis of advanced esophageal cancer patients. Preference score matching (PSM) is a statistical method to reduce data bias and the influence of confounding variables in observation and research. PSM is often used to balance baseline characteristics and eliminate the bias between the treatment population and the control population. The SEER database is a large tumor database established by the National Cancer Institute. It is a public database commonly used clinically for surveillance and research in the field of cancer. Nomogram are widely used for cancer prognosis. In recent years, there are few prognostic models for patients with metastatic esophageal cancer. Metastatic esophageal cancer is not clear in this field at present. The prognosis model has been constructed, which has a good prediction effect on patients with metastatic esophageal cancer and also has certain clinical references. Methods Patients and selection criteria Patients with distant metastatic esophageal cancer from January 2004 to December 2015 were selected from SEER database. The database (Incidence-SEER Research Plus Data,18 Registries, Nov 2020 Sub2000-2018 varying)includes radiotherapy and chemotherapy information. Patients with metastatic esophageal cancer were identified based on the following inclusion criteria: From January, 2004 to December, 2015, patients with esophageal cancer, AJCC 6th edition, M1 stage,; Patients must have complete survival time data; The effectiveness of follow-up must be guaranteed; Esophageal cancer was diagnosed by pathology.The exclusion criteria: Patients with missing or insufficient information such as staging, survival rate, follow-up time or cause of death; The treatment is not clear; The patient has not only one primary malignant tumor; The patients diagnosed only by autopsy results or death certificates. Ethical approval is not required as all data in the SEER database is available through a public method. Study variables Collect the following patient information includes: age, race, sex, primary site, histological type, degree of differentiation, TNM stage, radiotherapy, chemotherapy, surgery, survival time, survival status and so on. The end point of our research is OS. OS was defined as the time from the start of diagnosis to the time of death from any cause or the time of last follow-up cutoff. Construction of the nomogram After screening the data from the SEER database to meet the inclusion and exclusion criteria, baseline differences were eliminated by the PSM, and then the matched data were randomly divided into a training cohort and a validation cohort in a 7:3 ratio using the R software, and plotted a baseline table for each variable. Kaplan-Meier method was used to draw the survival curves of radiotherapy group and chemotherapy group. The log rank test was used to compare the differences between OS. Univariate cox regression model was used to analyze the prognostic factors of metastatic esophageal cancer (P < 0.05), The variables with p < 0.05 were analyzed by multivariate cox regression. The risk ratio (HR) and the corresponding 95% confidence interval (95%CI) are calculated. The index of independent risk factors for metastatic esophageal cancer was obtained(P<0.05).Based on these independent prognostic factors, we use R software to construct nomogram, which can predict the probability of OS in patients with metastatic esophageal cancer for 1 year, 2 years and 3 years. Discrimination and calibration of the nomogram C index and calibration curve are often used to evaluate the performance and accuracy of nomograms. The discrimination degree of prediction model is the degree of evaluating the ability of model to predict events. The degree of discrimination can reflect the prediction ability of constructing this prediction model. It can be evaluated by C index, ROC curve and AUC. In the ROC curve, the abscissa is specificity and the ordinate is sensitivity. C index and AUC of 0.5 indicate that the model has no predictive ability. The range of 0.50 ~ 0.70 indicates that the discrimination of the prediction model is low. A range of 0.70 ~ 0.90 indicates that the prediction discrimination is moderate. More than 0.90 indicates that the prediction model has high discrimination. Calibration degree, also called fitting degree, which can be evaluated by calibration curve to evaluate the degree of accurate risk estimation by the model. The higher the overlap of the model prediction curve with the diagonal standard line, this indicates a better calibration of the model. Results Case inclusion and exclusion process The inclusion and exclusion criteria of the study cases are shown in Fig. 1 . We selected patient cases from the SEER database between January 2004 and December 2015, and these patients were diagnosed with esophageal cancer in a total of 10,792 cases. Patients with esophageal cancer with M1 distant metastases were subsequently included. After that, we excluded patients with missing race (3 cases); patients with missing marital status (77 cases); patient cases with missing grading (388 cases); patient cases with missing tumor size (2,165 cases); patients with incomplete information on TNM staging (6,002 cases); and patients diagnosed only by autopsy results or death certificates (89 cases), which left 2,068 patients. These patients were treated with radiotherapy in 1087 cases, and there were 981 patients who did not receive radiotherapy. Baseline characteristics Table 1 shows the clinical baseline characteristics of all patients with metastatic esophageal cancer. From the analysis of the table, it can be seen that the number of patients in the radiotherapy group was 1,087 and the number of patients in the non-radiotherapy group was 981, and the number of patients receiving radiotherapy in metastatic esophageal cancer accounted for 52.6% of the total number of patients. Prior to the use of PSM, it was possible to observe some differences in the distribution of some variables between the two groups, which included marital status, chemotherapy treatment, tumor location, surgical treatment, and degree of differentiation. The proportion of unmarried patients who received radiotherapy increased relative to those who did not receive radiotherapy; patients who received radiotherapy were more likely to receive chemotherapy and surgery; patients with upper and mid-stage esophageal cancer were more likely to receive radiotherapy; and receipt of radiotherapy was increased in patients with lower levels of differentiation; of these, there were no statistically significant differences in the variables of gender, age, ethnicity, and histology on whether or not they received radiotherapy. By matching using the PSM method, we categorized patients into a group that did not receive radiotherapy and a group that received radiotherapy, each including 771 patients. By analyzing the baseline characteristics of the two groups, we can observe that there is no statistically significant difference in the data of patients who received radiotherapy or not after matching by PSM method, and the data after balancing for the same variable satisfy P > 0.05, which indicates that PSM has well balanced the baseline characteristics between the group that received radiotherapy and the group that did not receive radiotherapy. With this method, we eliminated the interference of confounding factors on the study results during the research process and improved the reliability of the study results. Table 1 Clinical characteristics before and after PSM according to radiotherapy or not Variables Before matching P a value After matching P value Non-radiotherapy N = 981(%) Radiotherapy N = 1087 (%) Non- radiotherapy N = 771(%) Radiotherapy N = 771(%) Sex 0.684 1.000 Male 811(82.7%) 907(83.4%) 644(83.5%) 644(83.5%) Female 170(17.3%) 180(16.6%) 127(16.5%) 127(16.5%) Age 0.407 0.758 <65 years 539(54.9%) 618(56.9%) 435(56.4%) 442(57.3%) ≥ 65 years 442(45.1%) 469(43.1%) 336(43.6%) 329(42.7%) Race 0.128 0.545 White 810(82.6%) 868(79.9%) 642(83.3%) 632(82.0%) Unwhite 171(17.4%) 219(20.1%) 129(16.7%) 139(18.0%) Marriage 0.005 0.918 Married 458(46.7%) 440(40.5%) 429(55.6%) 432(56.0%) Unmarried 523(53.3%) 647(59.5%) 342(44.4%) 339(44.0%) Chemotherapy <0.001 0.169 None 429(43.7%) 229(21.1%) 252(32.7%) 226(29.3%) Yes 552(56.3%) 858(78.9%) 519(67.3%) 545(70.7%) Primary site <0.001 0.667 Cervical/upper 36(3.7%) 65(6.0%) 31(4.0%) 30(3.9%) Thoracic/middle 170(17.3%) 207(19.0%) 142(18.4%) 137(17.8%) Abdominal/lower 699(71.3%) 768(70.7%) 558(72.4%) 561(72.8%) Overlapping 76(7.7%) 47(4.3%) 40(5.2%) 43(5.6%) Histology 0.102 1.000 Adenocarcinoma 652(66.5%) 684(62.9%) 516(66.9%) 516(66.9%) Non-adenocarcinoma 329(33.5%) 403(37.1%) 255(33.1%) 255(33.1%) Surgery <0.001 0.498 None 928(94.6%) 977(89.9%) 726(94.2%) 733(95.1%) Yes 53(5.4%) 110(10.1%) 45(5.8%) 38(4.9%) Grade 0.002 0.710 I + II 336(34.3%) 447(41.1%) 280(36.3%) 272(35.3%) III + IV 645(65.7%) 640(58.9%) 491(63.7%) 499(64.7%) T Stage 0.950 0.817 T1 284(29.0%) 244(22.4%) 217(28.1%) 197(25.6%) T2 50(5.1%) 70(6.4%) 37(4.8%) 47(6.1%) T3 208(21.2%) 361(33.2%) 176(22.8%) 214(27.8%) T4 253(25.8%) 254(23.4%) 206(26.7%) 193(25.0%) Tx 186(19.0%) 158(14.5%) 135(17.5%) 120(15.6%) N Stage 0.076 0.652 N0 268(27.3%) 288(26.5%) 211(27.4%) 206(26.7%) N1 600(61.2%) 731(67.2%) 489(63.4%) 509(66.0%) Nx 113(11.5%) 68(6.3%) 71(9.2%) 56(7.3%) M Stage <0.001 0.190 M1a 44(4.5%) 150(13.8%) 43(5.6%) 31(4.0%) M1b 937(95.5%) 937(86.2%) 728(94.4%) 740(96.0%) Vital status 0.189 0.488 Alive 20(2.0%) 32(2.9%) 15(1.9%) 19(2.5%) Death 961(98.0%) 1055(97.1%) 756(98.1%) 752(97.5%) Survival analyses In order to further demonstrate the therapeutic effects of radiotherapy, chemotherapy and surgery in patients with distant metastatic esophageal cancer, we grouped the patients according to whether they received radiotherapy, chemotherapy or not, or surgery or not, and then we performed Kaplan-Meier survival analysis of metastatic esophageal cancer patients before and after PSM, and the specific results are detailed in Figure.2. Figure 2 A showed that the survival graph of metastatic esophageal cancer patients before PSM, and there was a significant improvement in the chemotherapy group OS compared with no chemotherapy ([HR] 0.324, 95% CI: 0.294 ~ 0.358, p < 0.0001). Figure 2 B shows the survival graph after PSM in patients with metastatic esophageal cancer, and there was a significant improvement in OS in the chemotherapy group compared with no chemotherapy ([HR]0.369,95%CI:0.330 ~ 0.413, p < 0.0001). Figure 2 C shows the survival graph of metastatic esophageal cancer patients before PSM, and there was a significant improvement in OS in the radiotherapy group compared with the no-radiotherapy group ([HR]0.681,95%CI:0.624 ~ 0.744, p < 0.0001). Figure 2 D shows the survival graph of metastatic esophageal cancer patients before PSM, and there was a significant improvement in OS in the radiotherapy group compared with no radiotherapy ([HR] 0.879, 95% CI: 0.795 ~ 0.973, p = 0.0084). Figure 2 E shows the survival graph of metastatic esophageal cancer patients before PSM, and there was a significant improvement in OS in the surgical group compared with the non-surgical group ([HR] 0.455, 95% CI: 0.383 ~ 0.540, p < 0.0001). Figure 2 F shows the survival graph after PSM in patients with metastatic esophageal cancer, and there was a significant improvement in OS in the surgical group compared with the non-surgical group ([HR]0.482,95%CI:0.379 ~ 0.612, p < 0.0001). According to the results of our study, patients who had been treated with radiotherapy, chemotherapy, and surgery demonstrated longer survival than those who did not receive these treatment modalities, regardless of whether or not a propensity score matching process was performed. Comparison of clinical case data between training and validation cohort After eliminating baseline differences by the PSM method described above, a total of 1542 eligible patients were included, and we randomly divided the 1542 patients into training cohort and a validation cohort according to the ratio of 7:3, in which 1116 patients were in the training cohort and 426 patients were in the validation cohort. Among them, the case data of patients in the overall, training cohort, and validation cohort are shown in Table 2 . Table 2 Demographics and clinicopathological characteristics of patients with esophageal carcinoma Characteristics All patients(n = 1542) Training cohort (N = 1116) Validation cohort (N = 426) P value Number % Number % Number % Sex 0.857 Male 1288 83.5 931 83.4 357 83.8 Female 254 16.5 185 16.6 69 16.2 Race 0.885 White 1274 82.6 923 82.7 351 82.4 Unwhite 268 17.4 193 17.3 75 17.6 Age 0.588 <65 years 877 56.9 630 56.5 247 58.0 ≥65 years 665 43.1 486 43.5 179 42.0 Marriage 0.658 Married 861 55.8 627 56.2 234 54.9 Unmarried 681 44.2 489 43.8 192 45.1 Chemotherapy 0.534 None 478 31.0 351 31.5 127 29.8 Yes 1064 69.0 765 68.5 299 70.2 Radiotherapy 0.733 None 771 50 561 50.3 210 49.3 Yes 771 50 555 49.7 216 50.7 Histology 0.619 Adenocarcinoma 1032 66.9 751 67.3 281 66.0 Non- adenocarcinoma 510 33.1 365 32.7 145 34.0 Primary site 0.058 Cervical/upper 61 4.0 40 3.6 21 4.9 Thoracic/middle 279 18.1 201 18.0 78 18.3 Abdominal/lower 1119 72.6 805 72.1 314 73.7 Overlapping 83 5.4 70 6.3 13 3.1 Surgery 0.626 None 1459 94.6 1054 94.4 405 95.1 Yes 83 5.4 62 5.6 21 4.9 Grade 0.859 I + II 552 35.8 401 35.9 151 35.4 III + IV 990 64.2 715 64.1 275 64.6 T Stage 0.620 T1 414 26.8 295 26.4 119 27.9 T2 84 5.4 66 5.9 18 4.2 T3 390 25.3 285 25.5 105 24.6 T4 399 25.9 291 26.1 108 25.4 Tx 255 16.5 179 16.0 76 17.8 N Stage 0.684 N0 417 27.0 299 26.8 118 27.7 N1 998 64.7 721 64.6 277 65.0 Nx 127 8.2 96 8.6 31 7.3 M Stage 0.496 M1a 74 4.8 51 4.6 23 5.4 M1b 1468 95.2 1065 95.4 403 94.6 Vital status 0.814 Alive 34 2.2 24 2.2 10 2.3 Death 1508 97.8 1092 97.8 416 97.7 Of the 1542 patients with distant metastatic esophageal cancer, gender included 1288 male patients (83.5%) and 254 female patients (16.5%). Among races, there were 1274 cases of Caucasians, accounting for up to 82.6%, and 268 cases of non-Caucasians, accounting for 17.4%. Age < 65 years had 877 patients (56.9%) and age ≥ 65 years had 665 patients (43.1%). In marriage there were 861 patients (55.8%) who were married and 681 patients (44.2%) who were other (single, divorced, separated, widowed). Of chemotherapy there were 1064 patients (69.0%) who received chemotherapy and 478 patients (31.0%) who did not receive chemotherapy. The number of patients who received radiotherapy and those who did not was 771 cases, accounting for 50%, and the histologic types were adenocarcinoma in 1032 cases, accounting for 66.9%, and non-adenocarcinoma in 510 cases, accounting for 33.1%. The tumor location was located in the upper esophageal cancer in 61 cases, accounting for 4.0%, in the middle esophageal cancer in 279 cases, accounting for 18.1%, in the lower esophageal cancer in 1,119 patients, accounting for 72.6%, and in the other patients in 83 cases, accounting for 5.4%. In surgical treatment, there were 1459 patients without surgery, accounting for 94.6%, and 83 patients with surgery, accounting for 5.4%. There were a total of 990 patients in grade III + IV tumor differentiation, accounting for 64.2%, and a total of 552 patients in grade I + II, accounting for 35.8%.There were 414 patients in T1 stage in T staging, accounting for 26.8%, 84 patients in T2 stage, accounting for 5.4%, 390 patients in T3 stage, accounting for 25.3%, 399 patients in T4 stage, accounting for 25.9%, 255 patients in Tx stage, accounting for 16.5%, and 417 patients in lymph node stage N0, accounting for 27.0%, 998 patients in N1, accounting for 64.7%, and 127 patients in Nx, accounting for 8.2%. Among distant metastases, there were 74 patients with M1a (4.8%) and 1,468 patients with M1b (95.2%). A total of 1,508 (97.8%) of the 1,542 patients died and 34 (2.2%) survived. After statistical tests, it was found that there was no statistically significant difference between the training and validation cohort on the baseline balance (p > 0.05), and the differences in the various study indicators were not statistically significant, see Table 2 . After plotting the OS survival curves for the training and validation cohort, it was found that there was also no statistically significant difference between the two groups (p = 0.77), and the detailed data are shown in Fig. 3 . Independent prognostic factors in the training cohort The results of univariate and multivariate Cox regression analysis are shown in Table 3 . Univariate Cox regression analysis was performed on all variables to explore the effects of different factors on the prognosis of metastatic esophageal cancer, and the results showed that "race, age, marriage, chemotherapy, radiotherapy, histological grading, tumor site, surgery, T stage, N stage, and M stage" were the prognostic factors affecting the OS of metastatic esophageal cancer (P<0.05). and in the multifactorial Cox risk proportional regression model, chemotherapy, radiotherapy, tumor site, surgery, T stage, and M stage were independent prognostic factors affecting OS in metastatic esophageal cancer (Table 3 ). Table 3 Univariate and multivariate Cox proportional hazards regression analysis in esophageal cancer patients Variables HR Comparison Univariate analysis Multivariable analysis HR (95%CI) P value HR (95%CI) P value Sex Male . Female 1.091(0.931–1.279) 0.280 Race White Unwhite 1.332(1.138–1.559) <0.001 Age <65 years ≥ 65 years 1.170(1.038–1.318) 0.010 Marriage Married Unmarried 0.817(0.725–0.921) 0.001 Chemotherapy No vs Yes 0.338(0.295–0.386) <0.001 0.308(0.266–0.357) <0.001 Radiotherapy No vs Yes 0.874(0.776–0.985) 0.027 0.835(0.740–0.942) 0.003 * Histology Adenocarcinoma Non-adenocarcinoma 1.274(1.121–1.447) <0.001 Primary site Overlapping Cervical/upper 0.884(0.596–1.312) 0.541 0.611(0.407–0.918) 0.018 Thoracic/middle 0.997(0.757–1.313) 0.981 0.814 (0.612–1.082) 0.157 Abdominal/lower 0.739(0.576–0.947) 0.017 0.788 (0.612–1.015) 0.065 Surgery No vs Yes 0.465(0.353–0.614) <0.001 0.446 (0.333–0.596) <0.001 Grade I + II III + IV 1.124(0.993–1.272) 0.066 T Stage T1 T2 0.770(0.587–1.010) 0.059 0.879(0.668–1.158) 0.360 T3 0.814(0.690–0.960) 0.015 0.956(0.805–1.135) 0.607 T4 1.183(1.004–1.394) 0.045 1.227 (1.038–1.451) 0.017 Tx 1.216(1.008–1.467) 0.041 1.091(0.894–1.333) 0.391 N Stage N0 N1 0.959(0.837–1.099) 0.548 Nx 1.490(1.182–1.879) 0.001 M Stage M1a M1b 1.749(1.303–2.349) <0.001 1.799(1.325–2.444) <0.001 From the results of multifactorial Cox regression, we can learn that receiving chemotherapy was an independent prognostic factor for improving overall survival of metastatic esophageal cancer compared with not receiving chemotherapy (HR = 0.038,95% CI: 0.266–0.357, p < 0.001); receiving radiotherapy was also an independent prognostic factor for improving overall survival of metastatic esophageal cancer compared with not receiving radiotherapy (HR = 0.835,95% CI: 0.740–0.942, p = 0.003); receiving surgery was also an independent prognostic factor for improving OS in distant metastatic esophageal cancer compared to not receiving surgery (HR = 0.446,95% CI: 0.333–0.596, p < 0.001); and in the upper esophagus (HR = 0.611,95% CI: 0.407–0.918, p = 0.018) and lower esophagus (HR = 0.788,95%CI: 0.612–1.015, p = 0.065) had a poorer prognosis than patients with other tumor sites; patients with stage T4 (HR = 1.227,95%CI: 1.038–1.451, p = 0.017) had a poorer prognosis than patients with T1; patients with stage Nx (HR = 1.226,95% CI: 0.958–1.568, p = 0.105) patients had a worse prognosis than N0 patients; M1b stage (HR = 1.799,95% CI: 1.325–2.444, p < 0.001) patients had a worse prognosis than M1a stage patients. (Table 3 ) Prognostic nomogram for OS The statistically significant study variables obtained from the multifactorial Cox regression analysis above, such as the six independent risk factors of radiotherapy, T-stage, M-stage, surgery, chemotherapy, and tumor site, were plotted on a column-line graph (Fig. 4 ).The OS at 1 year, 2 years, and 3 years can be easily calculated, and the score value at the very top is the score obtained from the upwardly-directed vertical line made by each of the study variables below, and an individual risk score can be calculated by summing the scores obtained by all of them. The corresponding points were then found on the survival scale. The scores obtained can be summed to give a total score, an individual risk score is calculated, and the corresponding points are then found on the survival scale. Calibration and validation of the nomogram The accuracy and discriminative power of the column line drawings of distant metastatic esophageal cancer could be evaluated based on C-index and AUC. In the prediction model, the C-index of OS in the training cohort was 0.690 (95% CI:0.672–0.709), and that of OS in the validation cohort was 0.659 (95% CI:0.627–0.693), and the AUCs of OS for metastatic esophageal cancer in the training cohort for 1 year, 2 years, and 3 years were 69%, 73%, and 77.3%, respectively. The AUCs of 1, 2, and 3 years for OS of metastatic esophageal cancer in the validation cohort were 69.4%, 73.4%, and 78.1%, respectively. In the constructed model, the AUC values of the area under the curve for 1-year OS in the training cohort and validation cohort were between 0.50 and 0.70, and the AUCs of the rest of the training and validation cohorts were between 0.70 and 0.90, which indicated that the model had a medium degree of differentiation, and, it could be observed that the AUC of the area under the curve might get bigger and bigger with the prolongation of the time, and that the predictive model was getting more and more reliable, and the prediction model was getting differentiation is getting stronger. The ROC curves for the 1-year, 2-year, and 3-year OS for the training and validation cohort are shown in Fig. 5 . The calibration of the prediction model is assessed by the calibration curve, and the calibration curve is similar to the 45° diagonal line, which indicates that the calibration of the prediction model is good, and the calibration curves of the OS for 1, 2, and 3 years for the training and validation cohort are shown in Fig. 6 . Discussion Esophageal cancer remains an aggressive disease with a low survival rate [ 12 ] , with a low 5-year survival rate and distant metastases in 20–30% of patients with esophageal cancer at initial diagnosis [ 13 ] . It is necessary to construct a prognostic model for metastatic esophageal cancer. Through univariate and multivariate Cox regression analysis, we found that radiotherapy, chemotherapy, surgery, tumor site, T-stage, and M-stage were independent risk factors affecting OS. Some studies have indicated that metastatic esophageal cancer treated with surgery or radiotherapy can provide survival benefit for patients. Zhao et al. showed that the median survival of patients with M1a stage metastatic squamous esophageal cancer undergoing R0 esophagectomy after simultaneous radiotherapy could reach 36.9 months [ 14 ] . Elk et al. found that after the use of a multimodal treatment approach including surgery in patients with extraintestinal metastases or liver metastases from esophageal cancer, recurrence occurred in about 64% of R0 resection patients, and 50% of the operated patients were still alive after a median follow-up of 22 months [ 15 ] . This suggests that palliative resection or radiotherapy may be beneficial for survival. There are also some case reports suggesting that a combination of treatments such as surgery or radiotherapy treatment improves the overall survival of patients with metastatic esophageal cancer [ 16 – 20 ] . The above studies are consistent with the findings of the present study. Surgery is an option for earlier staging of esophageal cancer, and simultaneous radiochemotherapy is considered to be the preferred treatment option for locally advanced patients who are inoperable or unresectable. The results of a prospective phase II study showed that among patients with inoperable squamous esophageal cancer, those who received simultaneous radiotherapy with a chemotherapy regimen of cisplatin and doxorubicin had a more prolonged overall survival [ 22 ] .Seyedin et al. found that patients with non-visceral and non-osteosynovial metastatic esophageal cancer who underwent radical radiotherapy in combination with surgery had a higher median overall survival than those who were treated with radiotherapy alone [ 23 ] . The results of the study conducted by David et al. showed that chemotherapy combined with radical radiotherapy improved the overall survival of patients even more [ 8 ] . The results of the study in our SEER database also showed that radiotherapy improved the overall survival of patients with metastatic esophageal cancer. This study also has some limitations; the phase III study KEYNOTE-590 evaluated the efficacy of pembrolizumab in combination with chemotherapy as a first-line treatment for patients with advanced esophageal cancer [ 24 ] .ORIENT-15, also a phase III trial, compared to placebo in the first-line treatment of patients with advanced or metastatic esophageal squamous cell carcinoma, sindelizumab in combination with cisplatin in combination with paclitaxel showed significant benefits in terms of overall survival and progression-free survival [ 25 ] . In addition, similar benefits of sindilizumab in combination with cisplatin plus 5-fluorouracil showed significant potential [ 25 ] .The ESCORT-1st randomized clinical trial, a phase III trial designed to investigate the efficacy of chemotherapy combined with karelizumab treatment compared to placebo-combined chemotherapy in patients with advanced or metastatic esophageal squamous cell carcinoma, demonstrated that the chemotherapy-combined karelizumab treatment group significantly improved patients' overall survival and progression-free survival [ 26 ] . A multicenter phase III trial, the JUPITER-06 study, demonstrated that teraplizumab in combination with paclitaxel plus cisplatin chemotherapy significantly improved overall and progression-free survival in patients with primary advanced esophageal squamous carcinoma [ 27 ] .Cetuximab targeting epidermal growth factor receptor (EGFR) and bevacizumab targeting vascular endothelial growth factor (VEGF) have been shown to significantly improve the prognosis of patients with esophageal cancer [ 28 , 29 ] . Pabolizumab (PD-L1 inhibitor) has been approved for the treatment of patients with PD-L1-positive or advanced esophageal squamous cell carcinoma (ESCC), showing significant efficacy [ 30 ] . In contrast, the strategy of palliative chemotherapy combined with immunotherapy should be considered for those patients with distant metastatic disease [ 6 ] . For example, pembrolizumab combined with chemotherapy is used as first-line treatment for patients with advanced esophageal cancer. Immunotherapy is performed by activating one's own T-lymphocytes so that they recognize and kill tumor cells [ 31 ] . There are nationally renowned large clinical trials of immunotherapy for esophageal cancer, such as the study in KEYNOTE-028 which showed that pembrolizumab PD-L1-positive patients with advanced esophageal cancer demonstrated manageable toxicity and continued to exhibit antitumor activity [ 32 ] .The study in CheckMate648 showed that in patients with advanced esophageal squamous cell carcinoma, either nabulizumab in combination with chemotherapy or na nabulizumab in combination with ibritumomab as first-line treatment regimens both demonstrated significant benefits and were able to significantly prolong overall survival [ 33 ] .ATTRACTION-3, a phase III clinical trial, demonstrated that nabulizumab was associated with a significantly higher overall survival in patients with advanced esophageal squamous cell carcinoma who had received prior therapy compared to chemotherapy and a favorable safety profile was associated [ 34 ] . There are also some limitations in this study, First, the SEER database does not contain information such as immunotherapy, targeted therapy, clinical manifestations, basic diseases and surgical types. Second, although we consider chemotherapy in the nomogram, the specific scheme is unknown, different treatment schemes may lead to different results. In addition, we used SEER data mainly in America for validation and analysis, but did not use data from patients in other countries. Therefore, the reliability of the findings would be higher if data from domestic and foreign multicenter studies were integrated for external validation. The strength of our study lies in the selection of a large sample of metastatic esophageal cancer patients and the adequate statistical analysis of a column line graph of distant metastatic esophageal cancer patients, which has a better predictive effect on the prognosis of the patients and also has some clinical reference value. Moreover, with the continuous progress of immunotherapy and targeted therapies, the prediction of survival of cancer patients with different treatment combinations may be more challenging. Therefore, more multicenter studies and prospective data collection with other potential variables are needed to improve this column line graph. Conclusions We developed and validated a column-line diagram based on the SEER database, which can assist clinicians in predicting patient prognosis and also provide a convenient and reliable individualized survival prediction tool for patients with distant metastatic esophageal cancer. Our study demonstrated that T-stage, M-stage, primary tumor site, surgery, chemotherapy, and radiotherapy were independent prognostic risk factors affecting the survival of patients with distant metastatic esophageal cancer. Meanwhile radiotherapy, chemotherapy as well as surgical treatment can prolong the survival time of patients with distant metastatic esophageal cancer, which can bring survival benefit to patients with distant metastatic esophageal cancer. Declarations Conflict of Interest The authors have declared that no competing interest exists. Funding This research was funded by grants from the National Natural Science Foundation of China (82203377, to Yanwei Lu; 82003236, to Haibo Zhang); Natural Science Foundation of Zhejiang Province (Grant number: LQ22H160036, to Yanwei Lu; LY24H160022 to Haibo Zhang). Zhejiang Health Science and Technology Project (2022KY537, to Yanwei Lu, 2022KY596, to Haibo Zhang) Author Contribution Shuang Li and Yanwei Lu wrote the main manuscript text,and Ruiqi Liu ,Luanluan Huang and Ding Nan prepared Figures 1-3. Xiaoyan Chen and Wenjie Xia prepared Figures 4-5.Xiaodong Liang and Haibo Zhang prepared Figures 6.All the authors reviewed the manuscript. Acknowledgement The authors thank all patients, especially the ability to have open access to the SEER database. Data Availability All data included in this study are available upon request by contact with the corresponding author. References Sung H, Ferlay J, Siegel R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries [J]. CA Cancer J Clin, 2021, 71(3): 209–49. Zhu H, Ma X, Ye T, et al. Esophageal cancer in China: Practice and research in the new era [J]. Int J Cancer, 2023, 152(9): 1741–51. Chen W, Zheng R, Baade P D, et al. Cancer statistics in China, 2015 [J]. CA Cancer J Clin, 2016, 66(2): 115–32. Yang J, Liu X, Cao S, et al. Understanding Esophageal Cancer: The Challenges and Opportunities for the Next Decade [J]. Front Oncol, 2020, 10(1727. Blot W J. Invited commentary: more evidence of increased risks of cancer among alcohol drinkers [J]. Am J Epidemiol, 1999, 150(11): van Rossum P S N, Mohammad N H, Vleggaar F P, et al. Treatment for unresectable or metastatic oesophageal cancer: current evidence and trends [J]. Nat Rev Gastroenterol Hepatol, 2018, 15(4): 235–49. Bleiberg H, Conroy T, Paillot B, et al. Randomised phase II study of cisplatin and 5-fluorouracil (5-FU) versus cisplatin alone in advanced squamous cell oesophageal cancer [J]. Eur J Cancer, 1997, 33(8): 1216–20. Guttmann D M, Mitra N, Bekelman J, et al. Improved Overall Survival with Aggressive Primary Tumor Radiotherapy for Patients with Metastatic Esophageal Cancer [J]. J Thorac Oncol, 2017, 12(7): 1131–42. Rusthoven C G, Jones B L, Flaig T W, et al. Improved Survival With Prostate Radiation in Addition to Androgen Deprivation Therapy for Men With Newly Diagnosed Metastatic Prostate Cancer [J]. J Clin Oncol, 2016, 34(24): 2835–42. Dutton S J, Ferry D R, Blazeby J M, et al. Gefitinib for oesophageal cancer progressing after chemotherapy (COG): a phase 3, multicentre, double-blind, placebo-controlled randomised trial [J]. Lancet Oncol, 2014, 15(8): 894–904. Lee S, Cohen D J. Pharmacotherapy for metastatic esophageal cancer: where do we need to improve? [J]. Expert Opin Pharmacother, 2019, 20(3): 357–66. Mao W-M, Zheng W-H, Ling Z-Q. Epidemiologic risk factors for esophageal cancer development [J]. Asian Pac J Cancer Prev, 2011, 12(10): 2461–6. Quint L E, Hepburn L M, Francis I R, et al. Incidence and distribution of distant metastases from newly diagnosed esophageal carcinoma [J]. Cancer, 1995, 76(7): 1120–5. Chao Y-K, Wu Y-C, Liu Y-H, et al. Distant nodal metastases from intrathoracic esophageal squamous cell carcinoma: characteristics of long-term survivors after chemoradiotherapy [J]. J Surg Oncol, 2010, 102(2): 158–62. Van Daele E, Scuderi V, Pape E, et al. Long-term survival after multimodality therapy including surgery for metastatic esophageal cancer [J]. Acta Chir Belg, 2018, 118(4): 227–32. Tokairin Y, Kumagai Y, Yamazaki S. [A case of postoperative liver metastasis of esophageal cancer remains in progression free after successfully resected] [J]. Gan To Kagaku Ryoho, 2009, 36(12): 2462–4. Ikebe M, Kitamura M, Saitoh G, et al. [Multimodality therapy containing hepatic arterial infusion chemotherapy for liver metastasis of esophageal cancer-a case report] [J]. Gan To Kagaku Ryoho, 2012, 39(10): 1555–7. Ida S, Baba Y, Nagai Y, et al. [A case of recurrent esophageal cancer with lymph node and lung metastases, successfully treated with systemic chemotherapy and radiofrequency-ablation] [J]. Gan To Kagaku Ryoho, 2012, 39(1): 107–10. Iitaka D, Shiozaki A, Fujiwara H, et al. Case involving long-term survival after esophageal cancer with liver and lung metastases treated by multidisciplinary therapy: report of a case [J]. Surg Today, 2013, 43(5): 556–61. St-Amour P, Winiker M, Sempoux C, et al. Correction to: The ''Real R0'': A Resection Margin Smaller Than 0.1 cm is Associated with a Poor Prognosis After Oncologic Esophagectomy [J]. Ann Surg Oncol, 2021, 28(Suppl 3): 882. Hironaka S, Komori A, Machida R, et al. The association of primary tumor site with acute adverse event and efficacy of definitive chemoradiotherapy for cStage II/III esophageal cancer: an exploratory analysis of JCOG0909 [J]. Esophagus, 2020, 17(4): 417–24. Tang H-R, Ma H-F, An S-M, et al. A Phase II Study of Concurrent Chemoradiotherapy With Paclitaxel and Cisplatin for Inoperable Esophageal Squamous Cell Carcinoma [J]. Am J Clin Oncol, 2016, 39(4): 350–4. Seyedin S N, Parekh K R, Ginader T, et al. The Role of Definitive Radiation and Surgery in Metastatic Esophageal Cancer: An NCDB Investigation [J]. Ann Thorac Surg, 2021, 112(2): 459–66. Kato K, Shah M A, Enzinger P, et al. KEYNOTE-590: Phase III study of first-line chemotherapy with or without pembrolizumab for advanced esophageal cancer [J]. Future Oncol, 2019, 15(10): 1057–66. Lu Z, Wang J, Shu Y, et al. Sintilimab versus placebo in combination with chemotherapy as first line treatment for locally advanced or metastatic oesophageal squamous cell carcinoma (ORIENT-15): multicentre, randomised, double blind, phase 3 trial [J]. BMJ, 2022, 377(e068714. Luo H, Lu J, Bai Y, et al. Effect of Camrelizumab vs Placebo Added to Chemotherapy on Survival and Progression-Free Survival in Patients With Advanced or Metastatic Esophageal Squamous Cell Carcinoma: The ESCORT-1st Randomized Clinical Trial [J]. JAMA, 2021, 326(10): 916–25. Wang Z-X, Cui C, Yao J, et al. Toripalimab plus chemotherapy in treatment-naïve, advanced esophageal squamous cell carcinoma (JUPITER-06): A multi-center phase 3 trial [J]. Cancer Cell, 2022, 40(3): Ruhstaller T, Thuss-Patience P, Hayoz S, et al. Neoadjuvant chemotherapy followed by chemoradiation and surgery with and without cetuximab in patients with resectable esophageal cancer: a randomized, open-label, phase III trial (SAKK 75/08) [J]. Ann Oncol, 2018, 29(6): 1386–93. Cunningham D, Stenning S P, Smyth E C, et al. Peri-operative chemotherapy with or without bevacizumab in operable oesophagogastric adenocarcinoma (UK Medical Research Council ST03): primary analysis results of a multicentre, open-label, randomised phase 2–3 trial [J]. Lancet Oncol, 2017, 18(3): 357–70. Shah M A, Kojima T, Hochhauser D, et al. Efficacy and Safety of Pembrolizumab for Heavily Pretreated Patients With Advanced, Metastatic Adenocarcinoma or Squamous Cell Carcinoma of the Esophagus: The Phase 2 KEYNOTE-180 Study [J]. JAMA Oncol, 2019, 5(4): 546–50. Yang Y. Cancer immunotherapy: harnessing the immune system to battle cancer [J]. J Clin Invest, 2015, 125(9): 3335–7. Doi T, Piha-Paul S A, Jalal S I, et al. Safety and Antitumor Activity of the Anti-Programmed Death-1 Antibody Pembrolizumab in Patients With Advanced Esophageal Carcinoma [J]. J Clin Oncol, 2018, 36(1): 61–7. Doki Y, Ajani J A, Kato K, et al. Nivolumab Combination Therapy in Advanced Esophageal Squamous-Cell Carcinoma [J]. N Engl J Med, 2022, 386(5): 449–62. Kato K, Cho B C, Takahashi M, et al. Nivolumab versus chemotherapy in patients with advanced oesophageal squamous cell carcinoma refractory or intolerant to previous chemotherapy (ATTRACTION-3): a multicentre, randomised, open-label, phase 3 trial [J]. Lancet Oncol, 2019, 20(11): 1506–17. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Feb, 2025 Reviews received at journal 09 Jan, 2025 Reviewers agreed at journal 08 Jan, 2025 Reviews received at journal 29 Oct, 2024 Reviewers agreed at journal 18 Oct, 2024 Reviewers invited by journal 26 Jul, 2024 Editor assigned by journal 26 Jul, 2024 Editor invited by journal 12 Jul, 2024 Submission checks completed at journal 11 Jul, 2024 First submitted to journal 01 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4666614","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":334211863,"identity":"fd265f3b-1143-4a2d-8450-df6df2474de5","order_by":0,"name":"Shuang Li","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital(Affiliated People's Hospital, Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Li","suffix":""},{"id":334211864,"identity":"76e479de-dd25-40e4-a2d5-ef765a771058","order_by":1,"name":"Yanwei Lu","email":"","orcid":"","institution":"Zhejiang Provincial People’s Hospital, Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yanwei","middleName":"","lastName":"Lu","suffix":""},{"id":334211865,"identity":"3414c2a2-547c-4571-bc2f-fecd3aa96866","order_by":2,"name":"Ruiqi Liu","email":"","orcid":"","institution":"Zhejiang Provincial People’s Hospital, Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Ruiqi","middleName":"","lastName":"Liu","suffix":""},{"id":334211866,"identity":"3a56795f-7daf-43b8-bc4d-ba759b967a86","order_by":3,"name":"Luanluan Huang","email":"","orcid":"","institution":"Zhejiang Provincial People’s Hospital, Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Luanluan","middleName":"","lastName":"Huang","suffix":""},{"id":334211867,"identity":"501b0982-3ba5-49d1-994f-b6a833697754","order_by":4,"name":"Ding Nan","email":"","orcid":"","institution":"Zhejiang Provincial People’s Hospital, Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Ding","middleName":"","lastName":"Nan","suffix":""},{"id":334211868,"identity":"791f8b3e-b98c-4cf5-a71b-44c9fd5340c6","order_by":5,"name":"Xiaoyan Chen","email":"","orcid":"","institution":"Zhejiang Provincial People’s Hospital, Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Chen","suffix":""},{"id":334211869,"identity":"491be490-e0aa-4d2c-b5ad-113add59b9c5","order_by":6,"name":"wenjie xia","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital, Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"wenjie","middleName":"","lastName":"xia","suffix":""},{"id":334211870,"identity":"9a256e49-0759-4cb9-894f-e01a639cebcb","order_by":7,"name":"Haibo Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYDACCR4GCTCDvbHx4QcStBgwMPAcbjaWIE2LRHqbAA8xOuRn9x688aHmj5z5zIdtQM12croNBLQwzjmXbDnjmIGxzO3EtgcFDMnGZgcIaGGWyDGT5m0wSJwhndhuIMFwIHEbIS1scC2SB9uA/iJCCw9ciwQjkVokJPJAfjE2luBJBAayARF+kZ+RCwoxOTkJ9uMPH36osJMjqAUNGJCmfBSMglEwCkYBDgAA5DE6/stR/eEAAAAASUVORK5CYII=","orcid":"","institution":"Zhejiang Provincial People’s Hospital, Hangzhou Medical College","correspondingAuthor":true,"prefix":"","firstName":"Haibo","middleName":"","lastName":"Zhang","suffix":""},{"id":334211871,"identity":"211cc992-d189-44ab-9e21-62d54eab65a9","order_by":8,"name":"Xiaodong Liang","email":"","orcid":"","institution":"Zhejiang Provincial People’s Hospital, Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xiaodong","middleName":"","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2024-07-01 08:51:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4666614/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4666614/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-16890-w","type":"published","date":"2025-08-22T16:29:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62216736,"identity":"9225ee69-04d3-44fb-8105-f2e89473638c","added_by":"auto","created_at":"2024-08-11 11:46:58","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3961059,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of patients’ selection.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4666614/v1/ba467db78de381c07f83c4fb.jpg"},{"id":62215653,"identity":"8d7a026f-381a-4600-bab5-6fb98c00777d","added_by":"auto","created_at":"2024-08-11 11:38:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3440827,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival curve of metastatic esophageal cancer before and after PSM. (A) Pre-PSM chemotherapy overall survival. (B) Post PSM chemotherapy overall survival. (C) Pre-PSM radiotherapy overall survival.(D) Post-PSM radiotherapy overall survival. (E) Pre-PSM surgery overall survival. (F) Post PSM surgery overall survival\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4666614/v1/14cff966b3b42f27a4602d59.jpg"},{"id":62215648,"identity":"84fe119d-0ef7-4311-9013-e0f96d329483","added_by":"auto","created_at":"2024-08-11 11:38:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1239030,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curve of comparison between training cohort and validation group.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4666614/v1/927a344aee4cb3bd50cf333b.jpg"},{"id":62215650,"identity":"2acdb372-8a96-4b07-bbbb-f39169f0c303","added_by":"auto","created_at":"2024-08-11 11:38:58","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1105967,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram predicting survival in patients with distant metastatic esophageal cancer.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4666614/v1/cd46ad46cd8e8eeb027a4b81.jpg"},{"id":62216738,"identity":"4d9e5b5f-460b-4526-97ac-061bfe8e9a1c","added_by":"auto","created_at":"2024-08-11 11:46:58","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3050460,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curves and AUCs at 1, 2, and 3 years in the training cohort and validation cohort. ROC curves for (A) 1 year, (B) 2 year, and (C) 3 years in the training cohort; and (D) 1 year, (E) 2 year, and (F) 3 years in the validation cohort. OS, overall survival\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4666614/v1/228e50d794f7bfdb79c7539f.jpg"},{"id":62216737,"identity":"681f0c63-db0f-44df-98d1-712efc45a1fa","added_by":"auto","created_at":"2024-08-11 11:46:58","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3123680,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves predicting the 1-, 2-, and 3-year OS of patients in the training cohort and validation cohort. (A), (B) and (C): the training period is 1 year, 2 years and 3 years; (D), (E) and (F): the validation cohort is 1 year, 2 years and 3 years;\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4666614/v1/6c9a59017e464e6600781f1c.jpg"},{"id":89847261,"identity":"dd227931-e715-4b21-aa3f-bb61faeca4c0","added_by":"auto","created_at":"2025-08-25 16:42:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":17341416,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4666614/v1/14767686-f76f-4662-bd9c-d0e9f0fd1d5a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical features, treatment and prognosis analysis of distant metastatic esophageal cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEsophageal cancer is the seventh most common malignant tumor in the world, ranking sixth among all cancer-related causes of death\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Esophageal cancer was found to be the fourth leading cause of cancer deaths in Chinese men over the age of 45 in the National Cancer Center Registry in 2016\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. China is a new case of esophageal cancer, accounting for more than half of the global esophageal cancer cases\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.The main histological subtype of esophageal cancer is squamous cell carcinoma\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In 2020, the number of new cases of esophageal cancer will reach 604,000 and the number of deaths will reach 544,000\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The prognosis of esophageal cancer is extremely poor, and the 5-year survival rate is about 20%. Esophageal cancer is usually diagnosed in the late stage, mainly due to the lack of early clinical symptoms. With the continuous progress of comprehensive treatment of esophageal cancer, the prognosis of esophageal cancer has improved by means of endoscopic treatment, surgical techniques, radiotherapy, chemotherapy, immunotherapy and targeted therapy. Identifying, exploring and intervening all potential risk factors may reduce the incidence of esophageal cancer. The risk factors of esophageal cancer include gastroesophageal reflux disease, obesity, smoking, drinking hot tea and alcohol, eating pickled vegetables, low fruit intake, hot food, drinks, malnutrition and low social and economic status\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Studies have shown that Barrett's esophagus is also an important risk factor for esophageal cancer. The risk factors of esophageal squamous cell carcinoma and adenocarcinoma are still different\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Therefore, identifying risk factors and establishing a prediction models can benefit patients. It can also help clinicians to make more appropriate medical decisions for patients.\u003c/p\u003e \u003cp\u003eStudies have shown that concurrent chemoradiotherapy(CCRT) is the first choice for unresectable locally advanced esophageal cancer\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. At present, 5- fluorouracil or capecitabine combined with oxaliplatin or cisplatin is commonly used to treat locally advanced or metastatic esophageal squamous cell carcinoma\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. One study found that definitive radiotherapy improved overall survival (OS) in newly diagnosed metastatic esophageal cancer\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Short-term radiotherapy is used to relieve dysphagia, malnutrition and chronic bleeding in esophageal cancer\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Comprehensive treatment is very important for the relief of esophageal cancer. Endoscopic therapy and surgical intervention for early tumors. Chemotherapy, targeted drug therapy and immunotherapy are used for the treatment of unresectable locally advanced or advanced patients. Currently there are fewer drugs for targeted therapy of esophageal cancer, mostly targeting human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR), and inositol 3-kinase/mammalian target of rapamycin (PI3K/mTOR)\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.It is reported that adoptive T cell transfer technology is also used for distant metastatic esophageal cancer\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Of course, it also needs a lot of clinical trial data to verify. Clinical trials such as KEYNOTE-590, CheckMate648, KEYNOTE-811 and ATTRACTION-3 all show that immunotherapy can improve the prognosis of advanced esophageal cancer patients.\u003c/p\u003e \u003cp\u003ePreference score matching (PSM) is a statistical method to reduce data bias and the influence of confounding variables in observation and research. PSM is often used to balance baseline characteristics and eliminate the bias between the treatment population and the control population. The SEER database is a large tumor database established by the National Cancer Institute. It is a public database commonly used clinically for surveillance and research in the field of cancer. Nomogram are widely used for cancer prognosis.\u003c/p\u003e \u003cp\u003eIn recent years, there are few prognostic models for patients with metastatic esophageal cancer. Metastatic esophageal cancer is not clear in this field at present. The prognosis model has been constructed, which has a good prediction effect on patients with metastatic esophageal cancer and also has certain clinical references.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and selection criteria\u003c/h2\u003e \u003cp\u003ePatients with distant metastatic esophageal cancer from January 2004 to December 2015 were selected from SEER database. The database (Incidence-SEER Research Plus Data,18 Registries, Nov 2020 Sub2000-2018 varying)includes radiotherapy and chemotherapy information.\u003c/p\u003e \u003cp\u003ePatients with metastatic esophageal cancer were identified based on the following inclusion criteria: From January, 2004 to December, 2015, patients with esophageal cancer, AJCC 6th edition, M1 stage,; Patients must have complete survival time data; The effectiveness of follow-up must be guaranteed; Esophageal cancer was diagnosed by pathology.The exclusion criteria: Patients with missing or insufficient information such as staging, survival rate, follow-up time or cause of death; The treatment is not clear; The patient has not only one primary malignant tumor; The patients diagnosed only by autopsy results or death certificates.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003eis not required as all data in the SEER database is available through a public method.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy variables\u003c/h2\u003e \u003cp\u003eCollect the following patient information includes: age, race, sex, primary site, histological type, degree of differentiation, TNM stage, radiotherapy, chemotherapy, surgery, survival time, survival status and so on. The end point of our research is OS. OS was defined as the time from the start of diagnosis to the time of death from any cause or the time of last follow-up cutoff.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the nomogram\u003c/h2\u003e \u003cp\u003eAfter screening the data from the SEER database to meet the inclusion and exclusion criteria, baseline differences were eliminated by the PSM, and then the matched data were randomly divided into a training cohort and a validation cohort in a 7:3 ratio using the R software, and plotted a baseline table for each variable. Kaplan-Meier method was used to draw the survival curves of radiotherapy group and chemotherapy group. The log rank test was used to compare the differences between OS. Univariate cox regression model was used to analyze the prognostic factors of metastatic esophageal cancer (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), The variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were analyzed by multivariate cox regression. The risk ratio (HR) and the corresponding 95% confidence interval (95%CI) are calculated. The index of independent risk factors for metastatic esophageal cancer was obtained(P\u0026lt;0.05).Based on these independent prognostic factors, we use R software to construct nomogram, which can predict the probability of OS in patients with metastatic esophageal cancer for 1 year, 2 years and 3 years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDiscrimination and calibration of the nomogram\u003c/h2\u003e \u003cp\u003eC index and calibration curve are often used to evaluate the performance and accuracy of nomograms. The discrimination degree of prediction model is the degree of evaluating the ability of model to predict events. The degree of discrimination can reflect the prediction ability of constructing this prediction model. It can be evaluated by C index, ROC curve and AUC. In the ROC curve, the abscissa is specificity and the ordinate is sensitivity. C index and AUC of 0.5 indicate that the model has no predictive ability. The range of 0.50\u0026thinsp;~\u0026thinsp;0.70 indicates that the discrimination of the prediction model is low. A range of 0.70\u0026thinsp;~\u0026thinsp;0.90 indicates that the prediction discrimination is moderate. More than 0.90 indicates that the prediction model has high discrimination.\u003c/p\u003e \u003cp\u003eCalibration degree, also called fitting degree, which can be evaluated by calibration curve to evaluate the degree of accurate risk estimation by the model. The higher the overlap of the model prediction curve with the diagonal standard line, this indicates a better calibration of the model.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCase inclusion and exclusion process\u003c/h2\u003e \u003cp\u003eThe inclusion and exclusion criteria of the study cases are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We selected patient cases from the SEER database between January 2004 and December 2015, and these patients were diagnosed with esophageal cancer in a total of 10,792 cases. Patients with esophageal cancer with M1 distant metastases were subsequently included. After that, we excluded patients with missing race (3 cases); patients with missing marital status (77 cases); patient cases with missing grading (388 cases); patient cases with missing tumor size (2,165 cases); patients with incomplete information on TNM staging (6,002 cases); and patients diagnosed only by autopsy results or death certificates (89 cases), which left 2,068 patients. These patients were treated with radiotherapy in 1087 cases, and there were\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e981 patients who did not receive radiotherapy.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the clinical baseline characteristics of all patients with metastatic esophageal cancer. From the analysis of the table, it can be seen that the number of patients in the radiotherapy group was 1,087 and the number of patients in the non-radiotherapy group was 981, and the number of patients receiving radiotherapy in metastatic esophageal cancer accounted for 52.6% of the total number of patients. Prior to the use of PSM, it was possible to observe some differences in the distribution of some variables between the two groups, which included marital status, chemotherapy treatment, tumor location, surgical treatment, and degree of differentiation. The proportion of unmarried patients who received radiotherapy increased relative to those who did not receive radiotherapy; patients who received radiotherapy were more likely to receive chemotherapy and surgery; patients with upper and mid-stage esophageal cancer were more likely to receive radiotherapy; and receipt of radiotherapy was increased in patients with lower levels of differentiation; of these, there were no statistically significant differences in the variables of gender, age, ethnicity, and histology on whether or not they received radiotherapy. By matching using the PSM method, we categorized patients into a group that did not receive radiotherapy and a group that received radiotherapy, each including 771 patients. By analyzing the baseline characteristics of the two groups, we can observe that there is no statistically significant difference in the data of patients who received radiotherapy or not after matching by PSM method, and the data after balancing for the same variable satisfy P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, which indicates that PSM has well balanced the baseline characteristics between the group that received radiotherapy and the group that did not receive radiotherapy. With this method, we eliminated the interference of confounding factors on the study results during the research process and improved the reliability of the study results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics before and after PSM according to radiotherapy or not\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBefore matching\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003csup\u003ea\u003c/sup\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eAfter matching\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-radiotherapy\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;981(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1087 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon- radiotherapy\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;771(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;771(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e811(82.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e907(83.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e644(83.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e644(83.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170(17.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180(16.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e127(16.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e127(16.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e539(54.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e618(56.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e435(56.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e442(57.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e442(45.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e469(43.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e336(43.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e329(42.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e810(82.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e868(79.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e642(83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e632(82.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnwhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e171(17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e219(20.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e129(16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e139(18.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarriage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e458(46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e440(40.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e429(55.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e432(56.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e523(53.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e647(59.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e342(44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e339(44.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e429(43.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e229(21.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e252(32.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e226(29.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e552(56.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e858(78.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e519(67.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e545(70.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervical/upper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36(3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65(6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31(4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30(3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThoracic/middle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170(17.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e207(19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e142(18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e137(17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal/lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e699(71.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e768(70.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e558(72.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e561(72.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverlapping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76(7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47(4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40(5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43(5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e652(66.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e684(62.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e516(66.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e516(66.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e329(33.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e403(37.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e255(33.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e255(33.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e928(94.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e977(89.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e726(94.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e733(95.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53(5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110(10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45(5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38(4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u0026thinsp;+\u0026thinsp;II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e336(34.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e447(41.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e280(36.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e272(35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u0026thinsp;+\u0026thinsp;IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e645(65.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e640(58.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e491(63.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e499(64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e284(29.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e244(22.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e217(28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e197(25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50(5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70(6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37(4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47(6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e208(21.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e361(33.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e176(22.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e214(27.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e253(25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e254(23.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e206(26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e193(25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e186(19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e158(14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e135(17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e120(15.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e268(27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e288(26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e211(27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e206(26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e600(61.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e731(67.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e489(63.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e509(66.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113(11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68(6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71(9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56(7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44(4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150(13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43(5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31(4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e937(95.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e937(86.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e728(94.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e740(96.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20(2.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32(2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15(1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19(2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e961(98.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1055(97.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e756(98.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e752(97.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analyses\u003c/h2\u003e \u003cp\u003eIn order to further demonstrate the therapeutic effects of radiotherapy, chemotherapy and surgery in patients with distant metastatic esophageal cancer, we grouped the patients according to whether they received radiotherapy, chemotherapy or not, or surgery or not, and then we performed Kaplan-Meier survival analysis of metastatic esophageal cancer patients before and after PSM, and the specific results are detailed in Figure.2. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA showed that the survival graph of metastatic esophageal cancer patients before PSM, and there was a significant improvement in the chemotherapy group OS compared with no chemotherapy ([HR] 0.324, 95% CI: 0.294\u0026thinsp;~\u0026thinsp;0.358, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB shows the survival graph after PSM in patients with metastatic esophageal cancer, and there was a significant improvement in OS in the chemotherapy group compared with no chemotherapy ([HR]0.369,95%CI:0.330\u0026thinsp;~\u0026thinsp;0.413, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC shows the survival graph of metastatic esophageal cancer patients before PSM, and there was a significant improvement in OS in the radiotherapy group compared with the no-radiotherapy group ([HR]0.681,95%CI:0.624\u0026thinsp;~\u0026thinsp;0.744, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD shows the survival graph of metastatic esophageal cancer patients before PSM, and there was a significant improvement in OS in the radiotherapy group compared with no radiotherapy ([HR] 0.879, 95% CI: 0.795\u0026thinsp;~\u0026thinsp;0.973, p\u0026thinsp;=\u0026thinsp;0.0084). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE shows the survival graph of metastatic esophageal cancer patients before PSM, and there was a significant improvement in OS in the surgical group compared with the non-surgical group ([HR] 0.455, 95% CI: 0.383\u0026thinsp;~\u0026thinsp;0.540, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF shows the survival graph after PSM in patients with metastatic esophageal cancer, and there was a significant improvement in OS in the surgical group compared with the non-surgical group ([HR]0.482,95%CI:0.379\u0026thinsp;~\u0026thinsp;0.612, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). According to the results of our study, patients who had been treated with radiotherapy, chemotherapy, and surgery demonstrated longer survival than those who did not receive these treatment modalities, regardless of whether or not a propensity score matching process was performed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparison of clinical case data between training and validation cohort\u003c/h2\u003e \u003cp\u003e After eliminating baseline differences by the PSM method described above, a total of 1542 eligible patients were included, and we randomly divided the 1542 patients into training cohort and a validation cohort according to the ratio of 7:3, in which 1116 patients were in the training cohort and 426 patients were in the validation cohort. Among them, the case data of patients in the overall, training cohort, and validation cohort are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics and clinicopathological characteristics of patients with esophageal carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAll patients(n\u0026thinsp;=\u0026thinsp;1542)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eTraining cohort (N\u0026thinsp;=\u0026thinsp;1116)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eValidation cohort\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;426)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnwhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarriage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon- adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervical/upper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThoracic/middle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal/lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverlapping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u0026thinsp;+\u0026thinsp;II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u0026thinsp;+\u0026thinsp;IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOf the 1542 patients with distant metastatic esophageal cancer, gender included 1288 male patients (83.5%) and 254 female patients (16.5%). Among races, there were 1274 cases of Caucasians, accounting for up to 82.6%, and 268 cases of non-Caucasians, accounting for 17.4%. Age\u0026thinsp;\u0026lt;\u0026thinsp;65 years had 877 patients (56.9%) and age\u0026thinsp;\u0026ge;\u0026thinsp;65 years had 665 patients (43.1%). In marriage there were 861 patients (55.8%) who were married and 681 patients (44.2%) who were other (single, divorced, separated, widowed). Of chemotherapy there were 1064 patients (69.0%) who received chemotherapy and 478 patients (31.0%) who did not receive chemotherapy. The number of patients who received radiotherapy and those who did not was 771 cases, accounting for 50%, and the histologic types were adenocarcinoma in 1032 cases, accounting for 66.9%, and non-adenocarcinoma in 510 cases, accounting for 33.1%. The tumor location was located in the upper esophageal cancer in 61 cases, accounting for 4.0%, in the middle esophageal cancer in 279 cases, accounting for 18.1%, in the lower esophageal cancer in 1,119 patients, accounting for 72.6%, and in the other patients in 83 cases, accounting for 5.4%. In surgical treatment, there were 1459 patients without surgery, accounting for 94.6%, and 83 patients with surgery, accounting for 5.4%. There were a total of 990 patients in grade III\u0026thinsp;+\u0026thinsp;IV tumor differentiation, accounting for 64.2%, and a total of 552 patients in grade I\u0026thinsp;+\u0026thinsp;II, accounting for 35.8%.There were 414 patients in T1 stage in T staging, accounting for 26.8%, 84 patients in T2 stage, accounting for 5.4%, 390 patients in T3 stage, accounting for 25.3%, 399 patients in T4 stage, accounting for 25.9%, 255 patients in Tx stage, accounting for 16.5%, and 417 patients in lymph node stage N0, accounting for 27.0%, 998 patients in N1, accounting for 64.7%, and 127 patients in Nx, accounting for 8.2%. Among distant metastases, there were 74 patients with M1a (4.8%) and 1,468 patients with M1b (95.2%). A total of 1,508 (97.8%) of the 1,542 patients died and 34 (2.2%) survived.\u003c/p\u003e \u003cp\u003eAfter statistical tests, it was found that there was no statistically significant difference between the training and validation cohort on the baseline balance (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and the differences in the various study indicators were not statistically significant, see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. After plotting the OS survival curves for the training and validation cohort, it was found that there was also no statistically significant difference between the two groups (p\u0026thinsp;=\u0026thinsp;0.77), and the detailed data are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIndependent prognostic factors in the training cohort\u003c/h2\u003e \u003cp\u003eThe results of univariate and multivariate Cox regression analysis are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Univariate Cox regression analysis was performed on all variables to explore the effects of different factors on the prognosis of metastatic esophageal cancer, and the results showed that \"race, age, marriage, chemotherapy, radiotherapy, histological grading, tumor site, surgery, T stage, N stage, and M stage\" were the prognostic factors affecting the OS of metastatic esophageal cancer (P\u0026lt;0.05). and in the multifactorial Cox risk proportional regression model, chemotherapy, radiotherapy, tumor site, surgery, T stage, and M stage were independent prognostic factors affecting OS in metastatic esophageal cancer (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate Cox proportional hazards regression analysis in esophageal cancer patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR Comparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariable analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.091(0.931\u0026ndash;1.279)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnwhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.332(1.138\u0026ndash;1.559)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.170(1.038\u0026ndash;1.318)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarriage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.817(0.725\u0026ndash;0.921)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo vs Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.338(0.295\u0026ndash;0.386)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.308(0.266\u0026ndash;0.357)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo vs Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.874(0.776\u0026ndash;0.985)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.835(0.740\u0026ndash;0.942)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.274(1.121\u0026ndash;1.447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverlapping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCervical/upper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.884(0.596\u0026ndash;1.312)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.611(0.407\u0026ndash;0.918)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThoracic/middle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.997(0.757\u0026ndash;1.313)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.814 (0.612\u0026ndash;1.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbdominal/lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.739(0.576\u0026ndash;0.947)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.788 (0.612\u0026ndash;1.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo vs Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.465(0.353\u0026ndash;0.614)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.446 (0.333\u0026ndash;0.596)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u0026thinsp;+\u0026thinsp;II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIII\u0026thinsp;+\u0026thinsp;IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.124(0.993\u0026ndash;1.272)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.770(0.587\u0026ndash;1.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.879(0.668\u0026ndash;1.158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.814(0.690\u0026ndash;0.960)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.956(0.805\u0026ndash;1.135)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.183(1.004\u0026ndash;1.394)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.227 (1.038\u0026ndash;1.451)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.216(1.008\u0026ndash;1.467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.091(0.894\u0026ndash;1.333)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.959(0.837\u0026ndash;1.099)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.490(1.182\u0026ndash;1.879)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM1a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM1b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.749(1.303\u0026ndash;2.349)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.799(1.325\u0026ndash;2.444)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom the results of multifactorial Cox regression, we can learn that receiving chemotherapy was an independent prognostic factor for improving overall survival of metastatic esophageal cancer compared with not receiving chemotherapy (HR\u0026thinsp;=\u0026thinsp;0.038,95% CI: 0.266\u0026ndash;0.357, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); receiving radiotherapy was also an independent prognostic factor for improving overall survival of metastatic esophageal cancer compared with not receiving radiotherapy (HR\u0026thinsp;=\u0026thinsp;0.835,95% CI: 0.740\u0026ndash;0.942, p\u0026thinsp;=\u0026thinsp;0.003); receiving surgery was also an independent prognostic factor for improving OS in distant metastatic esophageal cancer compared to not receiving surgery (HR\u0026thinsp;=\u0026thinsp;0.446,95% CI: 0.333\u0026ndash;0.596, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); and in the upper esophagus (HR\u0026thinsp;=\u0026thinsp;0.611,95% CI: 0.407\u0026ndash;0.918, p\u0026thinsp;=\u0026thinsp;0.018) and lower esophagus (HR\u0026thinsp;=\u0026thinsp;0.788,95%CI: 0.612\u0026ndash;1.015, p\u0026thinsp;=\u0026thinsp;0.065) had a poorer prognosis than patients with other tumor sites; patients with stage T4 (HR\u0026thinsp;=\u0026thinsp;1.227,95%CI: 1.038\u0026ndash;1.451, p\u0026thinsp;=\u0026thinsp;0.017) had a poorer prognosis than patients with T1; patients with stage Nx (HR\u0026thinsp;=\u0026thinsp;1.226,95% CI: 0.958\u0026ndash;1.568, p\u0026thinsp;=\u0026thinsp;0.105) patients had a worse prognosis than N0 patients; M1b stage (HR\u0026thinsp;=\u0026thinsp;1.799,95% CI: 1.325\u0026ndash;2.444, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) patients had a worse prognosis than M1a stage patients. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic nomogram for OS\u003c/h2\u003e \u003cp\u003eThe statistically significant study variables obtained from the multifactorial Cox regression analysis above, such as the six independent risk factors of radiotherapy, T-stage, M-stage, surgery, chemotherapy, and tumor site, were plotted on a column-line graph (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).The OS at 1 year, 2 years, and 3 years can be easily calculated, and the score value at the very top is the score obtained from the upwardly-directed vertical line made by each of the study variables below, and an individual risk score can be calculated by summing the scores obtained by all of them. The corresponding points were then found on the survival scale. The scores obtained can be summed to give a total score, an individual risk score is calculated, and the corresponding points are then found on the survival scale.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCalibration and validation of the nomogram\u003c/h2\u003e \u003cp\u003eThe accuracy and discriminative power of the column line drawings of distant metastatic esophageal cancer could be evaluated based on C-index and AUC. In the prediction model, the C-index of OS in the training cohort was 0.690 (95% CI:0.672\u0026ndash;0.709), and that of OS in the validation cohort was 0.659 (95% CI:0.627\u0026ndash;0.693), and the AUCs of OS for metastatic esophageal cancer in the training cohort for 1 year, 2 years, and 3 years were 69%, 73%, and 77.3%, respectively. The AUCs of 1, 2, and 3 years for OS of metastatic esophageal cancer in the validation cohort were 69.4%, 73.4%, and 78.1%, respectively. In the constructed model, the AUC values of the area under the curve for 1-year OS in the training cohort and validation cohort were between 0.50 and 0.70, and the AUCs of the rest of the training and validation cohorts were between 0.70 and 0.90, which indicated that the model had a medium degree of differentiation, and, it could be observed that the AUC of the area under the curve might get bigger and bigger with the prolongation of the time, and that the predictive model was getting more and more reliable, and the prediction model was getting differentiation is getting stronger. The ROC curves for the 1-year, 2-year, and 3-year OS for the training and validation cohort are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The calibration of the prediction model is assessed by the calibration curve, and the calibration curve is similar to the 45\u0026deg; diagonal line, which indicates that the calibration of the prediction model is good, and the calibration curves of the OS for 1, 2, and 3 years for the training and validation cohort are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eEsophageal cancer remains an aggressive disease with a low survival rate\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, with a low 5-year survival rate and distant metastases in 20\u0026ndash;30% of patients with esophageal cancer at initial diagnosis\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. It is necessary to construct a prognostic model for metastatic esophageal cancer. Through univariate and multivariate Cox regression analysis, we found that radiotherapy, chemotherapy, surgery, tumor site, T-stage, and M-stage were independent risk factors affecting OS.\u003c/p\u003e \u003cp\u003eSome studies have indicated that metastatic esophageal cancer treated with surgery or radiotherapy can provide survival benefit for patients. Zhao et al. showed that the median survival of patients with M1a stage metastatic squamous esophageal cancer undergoing R0 esophagectomy after simultaneous radiotherapy could reach 36.9 months\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Elk et al. found that after the use of a multimodal treatment approach including surgery in patients with extraintestinal metastases or liver metastases from esophageal cancer, recurrence occurred in about 64% of R0 resection patients, and 50% of the operated patients were still alive after a median follow-up of 22 months\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. This suggests that palliative resection or radiotherapy may be beneficial for survival. There are also some case reports suggesting that a combination of treatments such as surgery or radiotherapy treatment improves the overall survival of patients with metastatic esophageal cancer\u003csup\u003e[\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. The above studies are consistent with the findings of the present study.\u003c/p\u003e \u003cp\u003eSurgery is an option for earlier staging of esophageal cancer, and simultaneous radiochemotherapy is considered to be the preferred treatment option for locally advanced patients who are inoperable or unresectable. The results of a prospective phase II study showed that among patients with inoperable squamous esophageal cancer, those who received simultaneous radiotherapy with a chemotherapy regimen of cisplatin and doxorubicin had a more prolonged overall survival\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.Seyedin et al. found that patients with non-visceral and non-osteosynovial metastatic esophageal cancer who underwent radical radiotherapy in combination with surgery had a higher median overall survival than those who were treated with radiotherapy alone\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. The results of the study conducted by David et al. showed that chemotherapy combined with radical radiotherapy improved the overall survival of patients even more\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The results of the study in our SEER database also showed that radiotherapy improved the overall survival of patients with metastatic esophageal cancer.\u003c/p\u003e \u003cp\u003eThis study also has some limitations; the phase III study KEYNOTE-590 evaluated the efficacy of pembrolizumab in combination with chemotherapy as a first-line treatment for patients with advanced esophageal cancer\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.ORIENT-15, also a phase III trial, compared to placebo in the first-line treatment of patients with advanced or metastatic esophageal squamous cell carcinoma, sindelizumab in combination with cisplatin in combination with paclitaxel showed significant benefits in terms of overall survival and progression-free survival\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. In addition, similar benefits of sindilizumab in combination with cisplatin plus 5-fluorouracil showed significant potential\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.The ESCORT-1st randomized clinical trial, a phase III trial designed to investigate the efficacy of chemotherapy combined with karelizumab treatment compared to placebo-combined chemotherapy in patients with advanced or metastatic esophageal squamous cell carcinoma, demonstrated that the chemotherapy-combined karelizumab treatment group significantly improved patients' overall survival and progression-free survival\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. A multicenter phase III trial, the JUPITER-06 study, demonstrated that teraplizumab in combination with paclitaxel plus cisplatin chemotherapy significantly improved overall and progression-free survival in patients with primary advanced esophageal squamous carcinoma\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.Cetuximab targeting epidermal growth factor receptor (EGFR) and bevacizumab targeting vascular endothelial growth factor (VEGF) have been shown to significantly improve the prognosis of patients with esophageal cancer\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Pabolizumab (PD-L1 inhibitor) has been approved for the treatment of patients with PD-L1-positive or advanced esophageal squamous cell carcinoma (ESCC), showing significant efficacy \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn contrast, the strategy of palliative chemotherapy combined with immunotherapy should be considered for those patients with distant metastatic disease \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. For example, pembrolizumab combined with chemotherapy is used as first-line treatment for patients with advanced esophageal cancer. Immunotherapy is performed by activating one's own T-lymphocytes so that they recognize and kill tumor cells\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. There are nationally renowned large clinical trials of immunotherapy for esophageal cancer, such as the study in KEYNOTE-028 which showed that pembrolizumab PD-L1-positive patients with advanced esophageal cancer demonstrated manageable toxicity and continued to exhibit antitumor activity\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.The study in CheckMate648 showed that in patients with advanced esophageal squamous cell carcinoma, either nabulizumab in combination with chemotherapy or na nabulizumab in combination with ibritumomab as first-line treatment regimens both demonstrated significant benefits and were able to significantly prolong overall survival\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e.ATTRACTION-3, a phase III clinical trial, demonstrated that nabulizumab was associated with a significantly higher overall survival in patients with advanced esophageal squamous cell carcinoma who had received prior therapy compared to chemotherapy and a favorable safety profile was associated\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThere are also some limitations in this study, First, the SEER database does not contain information such as immunotherapy, targeted therapy, clinical manifestations, basic diseases and surgical types. Second, although we consider chemotherapy in the nomogram, the specific scheme is unknown, different treatment schemes may lead to different results. In addition, we used SEER data mainly in America for validation and analysis, but did not use data from patients in other countries. Therefore, the reliability of the findings would be higher if data from domestic and foreign multicenter studies were integrated for external validation.\u003c/p\u003e \u003cp\u003eThe strength of our study lies in the selection of a large sample of metastatic esophageal cancer patients and the adequate statistical analysis of a column line graph of distant metastatic esophageal cancer patients, which has a better predictive effect on the prognosis of the patients and also has some clinical reference value. Moreover, with the continuous progress of immunotherapy and targeted therapies, the prediction of survival of cancer patients with different treatment combinations may be more challenging. Therefore, more multicenter studies and prospective data collection with other potential variables are needed to improve this column line graph.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe developed and validated a column-line diagram based on the SEER database, which can assist clinicians in predicting patient prognosis and also provide a convenient and reliable individualized survival prediction tool for patients with distant metastatic esophageal cancer. Our study demonstrated that T-stage, M-stage, primary tumor site, surgery, chemotherapy, and radiotherapy were independent prognostic risk factors affecting the survival of patients with distant metastatic esophageal cancer. Meanwhile radiotherapy, chemotherapy as well as surgical treatment can prolong the survival time of patients with distant metastatic esophageal cancer, which can bring survival benefit to patients with distant metastatic esophageal cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors have declared that no competing interest exists.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by grants from the National Natural Science Foundation of China (82203377, to Yanwei Lu; 82003236, to Haibo Zhang); Natural Science Foundation of Zhejiang Province (Grant number: LQ22H160036, to Yanwei Lu; LY24H160022 to Haibo Zhang). Zhejiang Health Science and Technology Project (2022KY537, to Yanwei Lu, 2022KY596, to Haibo Zhang)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eShuang Li and Yanwei Lu wrote the main manuscript text,and Ruiqi Liu ,Luanluan Huang and Ding Nan prepared Figures 1-3. Xiaoyan Chen and Wenjie Xia prepared Figures 4-5.Xiaodong Liang and Haibo Zhang prepared Figures 6.All the authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eThe authors thank all patients, especially the ability to have open access to the SEER database.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data included in this study are available upon request by contact with the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries [J]. CA Cancer J Clin, 2021, 71(3): 209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu H, Ma X, Ye T, et al. Esophageal cancer in China: Practice and research in the new era [J]. Int J Cancer, 2023, 152(9): 1741\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen W, Zheng R, Baade P D, et al. Cancer statistics in China, 2015 [J]. CA Cancer J Clin, 2016, 66(2): 115\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang J, Liu X, Cao S, et al. Understanding Esophageal Cancer: The Challenges and Opportunities for the Next Decade [J]. Front Oncol, 2020, 10(1727.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlot W J. Invited commentary: more evidence of increased risks of cancer among alcohol drinkers [J]. Am J Epidemiol, 1999, 150(11):\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Rossum P S N, Mohammad N H, Vleggaar F P, et al. Treatment for unresectable or metastatic oesophageal cancer: current evidence and trends [J]. Nat Rev Gastroenterol Hepatol, 2018, 15(4): 235\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBleiberg H, Conroy T, Paillot B, et al. Randomised phase II study of cisplatin and 5-fluorouracil (5-FU) versus cisplatin alone in advanced squamous cell oesophageal cancer [J]. Eur J Cancer, 1997, 33(8): 1216\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuttmann D M, Mitra N, Bekelman J, et al. Improved Overall Survival with Aggressive Primary Tumor Radiotherapy for Patients with Metastatic Esophageal Cancer [J]. J Thorac Oncol, 2017, 12(7): 1131\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRusthoven C G, Jones B L, Flaig T W, et al. Improved Survival With Prostate Radiation in Addition to Androgen Deprivation Therapy for Men With Newly Diagnosed Metastatic Prostate Cancer [J]. J Clin Oncol, 2016, 34(24): 2835\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDutton S J, Ferry D R, Blazeby J M, et al. Gefitinib for oesophageal cancer progressing after chemotherapy (COG): a phase 3, multicentre, double-blind, placebo-controlled randomised trial [J]. Lancet Oncol, 2014, 15(8): 894\u0026ndash;904.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee S, Cohen D J. Pharmacotherapy for metastatic esophageal cancer: where do we need to improve? [J]. Expert Opin Pharmacother, 2019, 20(3): 357\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMao W-M, Zheng W-H, Ling Z-Q. Epidemiologic risk factors for esophageal cancer development [J]. Asian Pac J Cancer Prev, 2011, 12(10): 2461\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuint L E, Hepburn L M, Francis I R, et al. Incidence and distribution of distant metastases from newly diagnosed esophageal carcinoma [J]. Cancer, 1995, 76(7): 1120\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChao Y-K, Wu Y-C, Liu Y-H, et al. Distant nodal metastases from intrathoracic esophageal squamous cell carcinoma: characteristics of long-term survivors after chemoradiotherapy [J]. J Surg Oncol, 2010, 102(2): 158\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Daele E, Scuderi V, Pape E, et al. Long-term survival after multimodality therapy including surgery for metastatic esophageal cancer [J]. Acta Chir Belg, 2018, 118(4): 227\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTokairin Y, Kumagai Y, Yamazaki S. [A case of postoperative liver metastasis of esophageal cancer remains in progression free after successfully resected] [J]. Gan To Kagaku Ryoho, 2009, 36(12): 2462\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIkebe M, Kitamura M, Saitoh G, et al. [Multimodality therapy containing hepatic arterial infusion chemotherapy for liver metastasis of esophageal cancer-a case report] [J]. Gan To Kagaku Ryoho, 2012, 39(10): 1555\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIda S, Baba Y, Nagai Y, et al. [A case of recurrent esophageal cancer with lymph node and lung metastases, successfully treated with systemic chemotherapy and radiofrequency-ablation] [J]. Gan To Kagaku Ryoho, 2012, 39(1): 107\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIitaka D, Shiozaki A, Fujiwara H, et al. Case involving long-term survival after esophageal cancer with liver and lung metastases treated by multidisciplinary therapy: report of a case [J]. Surg Today, 2013, 43(5): 556\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSt-Amour P, Winiker M, Sempoux C, et al. Correction to: The ''Real R0'': A Resection Margin Smaller Than 0.1 cm is Associated with a Poor Prognosis After Oncologic Esophagectomy [J]. Ann Surg Oncol, 2021, 28(Suppl 3): 882.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHironaka S, Komori A, Machida R, et al. The association of primary tumor site with acute adverse event and efficacy of definitive chemoradiotherapy for cStage II/III esophageal cancer: an exploratory analysis of JCOG0909 [J]. Esophagus, 2020, 17(4): 417\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang H-R, Ma H-F, An S-M, et al. A Phase II Study of Concurrent Chemoradiotherapy With Paclitaxel and Cisplatin for Inoperable Esophageal Squamous Cell Carcinoma [J]. Am J Clin Oncol, 2016, 39(4): 350\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeyedin S N, Parekh K R, Ginader T, et al. The Role of Definitive Radiation and Surgery in Metastatic Esophageal Cancer: An NCDB Investigation [J]. Ann Thorac Surg, 2021, 112(2): 459\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKato K, Shah M A, Enzinger P, et al. KEYNOTE-590: Phase III study of first-line chemotherapy with or without pembrolizumab for advanced esophageal cancer [J]. Future Oncol, 2019, 15(10): 1057\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu Z, Wang J, Shu Y, et al. Sintilimab versus placebo in combination with chemotherapy as first line treatment for locally advanced or metastatic oesophageal squamous cell carcinoma (ORIENT-15): multicentre, randomised, double blind, phase 3 trial [J]. BMJ, 2022, 377(e068714.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo H, Lu J, Bai Y, et al. Effect of Camrelizumab vs Placebo Added to Chemotherapy on Survival and Progression-Free Survival in Patients With Advanced or Metastatic Esophageal Squamous Cell Carcinoma: The ESCORT-1st Randomized Clinical Trial [J]. JAMA, 2021, 326(10): 916\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z-X, Cui C, Yao J, et al. Toripalimab plus chemotherapy in treatment-na\u0026iuml;ve, advanced esophageal squamous cell carcinoma (JUPITER-06): A multi-center phase 3 trial [J]. Cancer Cell, 2022, 40(3):\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuhstaller T, Thuss-Patience P, Hayoz S, et al. Neoadjuvant chemotherapy followed by chemoradiation and surgery with and without cetuximab in patients with resectable esophageal cancer: a randomized, open-label, phase III trial (SAKK 75/08) [J]. Ann Oncol, 2018, 29(6): 1386\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCunningham D, Stenning S P, Smyth E C, et al. Peri-operative chemotherapy with or without bevacizumab in operable oesophagogastric adenocarcinoma (UK Medical Research Council ST03): primary analysis results of a multicentre, open-label, randomised phase 2\u0026ndash;3 trial [J]. Lancet Oncol, 2017, 18(3): 357\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah M A, Kojima T, Hochhauser D, et al. Efficacy and Safety of Pembrolizumab for Heavily Pretreated Patients With Advanced, Metastatic Adenocarcinoma or Squamous Cell Carcinoma of the Esophagus: The Phase 2 KEYNOTE-180 Study [J]. JAMA Oncol, 2019, 5(4): 546\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Y. Cancer immunotherapy: harnessing the immune system to battle cancer [J]. J Clin Invest, 2015, 125(9): 3335\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoi T, Piha-Paul S A, Jalal S I, et al. Safety and Antitumor Activity of the Anti-Programmed Death-1 Antibody Pembrolizumab in Patients With Advanced Esophageal Carcinoma [J]. J Clin Oncol, 2018, 36(1): 61\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoki Y, Ajani J A, Kato K, et al. Nivolumab Combination Therapy in Advanced Esophageal Squamous-Cell Carcinoma [J]. N Engl J Med, 2022, 386(5): 449\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKato K, Cho B C, Takahashi M, et al. Nivolumab versus chemotherapy in patients with advanced oesophageal squamous cell carcinoma refractory or intolerant to previous chemotherapy (ATTRACTION-3): a multicentre, randomised, open-label, phase 3 trial [J]. Lancet Oncol, 2019, 20(11): 1506\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Distant metastatic oesophageal cancer, PSM, SEER database, Prediction model","lastPublishedDoi":"10.21203/rs.3.rs-4666614/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4666614/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackgrounds\u003c/h2\u003e \u003cp\u003e: Esophageal cancer (EC) is one of the most common malignant tumors in China. EC is characterized by poor clinical prognosis, with many patients being diagnosed at advanced stages. This study utilized data from the the Surveillance, Epidemiology, and End Results (SEER) database. The clinical features, treatment and prognostic factors of patients with distant metastatic esophageal cancer were screened and analyzed, a nomogram was drawn to construct a prognostic model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eEligible patients with distant metastatic esophageal cancer diagnosed from January 2004 to December 2015 were extracted from SEER database. Propensity score matching(PSM)was used to eliminate baseline differences between groups. The data were divided into training cohort (1116 cases) and validation cohort (426 cases) by using R software and random sampling function at the ratio of 7: 3. The baseline table was plotted using x2 or Fisher's exact test. Kaplan-Meier curve, log rank test and Cox regression were used for survival analysis. C index and AUC were used to evaluate the performance of prognosis model. Calibration curve was used to evaluate the calibration of the model. Using the data of the validation cohort, external validation is used to create prediction model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAfter applying the inclusion and exclusion criteria and PSM, a total of 1,542 cases diagnosed between 2004 and 2015 were included in the study. Before and after PSM, we analyzed Kaplan-Meier survival of patients with metastatic esophageal cancer with different treatment methods. The results showed that radiotherapy, chemotherapy or surgical treatment brought significant survival benefits to patients with metastatic esophageal cancer(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Univariate and Multivariate regression analysis showed that T-stage, M-stage, primary site, surgery, chemotherapy and radiotherapy were independent prognostic factors affecting the prognosis of distant metastatic oesophageal cancer (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Evaluating the predictive ability of nomogram, the C index of the training cohort was 0.69(95%CI:0.67\u0026ndash;0.71), and the C index of the validation cohort was 0.659 (95% CI:0.627\u0026ndash;0.693). The AUC values for the training and validation cohort for the 1-year OS ranged from 0.50 to 0.70, and the AUC for the rest of the training and validation cohort ranged from 0.70 to 0.90, which suggests that the model is moderately discriminating. The calibration curves of 1 year, 2 years and 3 years in the two groups are very close to the 45\u0026deg;reference line, suggesting that the models exhibit a good degree of calibration. The C-index, AUC and calibration curves suggest that the models have good discriminating and calibration.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe results reveal that T stage, M stage, primary tumor site, surgery, chemotherapy and radiotherapy play an important role in influencing the treatment effect and prognosis of patients. The nomogram prediction model based on the above independent risk factors shows good discriminating and calibration.\u003c/p\u003e","manuscriptTitle":"Clinical features, treatment and prognosis analysis of distant metastatic esophageal cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-11 11:38:54","doi":"10.21203/rs.3.rs-4666614/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-11T04:56:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-09T17:36:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86473746300725938313541676480031001815","date":"2025-01-09T01:01:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-29T16:20:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"101306446161470986510375170300060908839","date":"2024-10-18T16:29:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-26T11:20:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-26T11:16:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-12T06:02:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-11T06:00:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-01T08:50:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8fdc7b21-c7f2-44a1-aa3b-7db6ee7301e3","owner":[],"postedDate":"August 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":35392428,"name":"Biological sciences/Cancer/Cancer models"},{"id":35392429,"name":"Health sciences/Oncology/Cancer"},{"id":35392430,"name":"Biological sciences/Cancer"},{"id":35392431,"name":"Health sciences/Diseases"},{"id":35392432,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-08-25T16:35:26+00:00","versionOfRecord":{"articleIdentity":"rs-4666614","link":"https://doi.org/10.1038/s41598-025-16890-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-08-22 16:29:26","publishedOnDateReadable":"August 22nd, 2025"},"versionCreatedAt":"2024-08-11 11:38:54","video":"","vorDoi":"10.1038/s41598-025-16890-w","vorDoiUrl":"https://doi.org/10.1038/s41598-025-16890-w","workflowStages":[]},"version":"v1","identity":"rs-4666614","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4666614","identity":"rs-4666614","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}