Development and External Validation of a Nomogram to Predict Prognosis of Patients With Urothelial Carcinoma of Bladder

Development and External Validation of a Nomogram to Predict Prognosis of Patients With Urothelial Carcinoma of Bladder | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and External Validation of a Nomogram to Predict Prognosis of Patients With Urothelial Carcinoma of Bladder Jufang Wei, Chunmeng Wei, Juan Chen, Wenpiao Zhao, Xianhui Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4076346/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This research aimed to create and validate nomogram predicting overall survival (OS) for urothelial carcinoma of the bladder (UCB) patients. Methods We sourced 15,606 UCB patients diagnosed between 2004 and 2015 from the Surveillance, Epidemiology, and End Results database. The patients were randomized into training (70%) and internal validation (30%) cohorts. In addition, 122 patients from Minzu Hospital of Guangxi Zhuang Autonomous Region between 2012 and 2022 were selected as the external validation cohort. Utilizing univariate and multivariate Cox regression analyses, we devised nomograms forecasting 1-, 3-, and 5-year OS. Several metrics, including the consistency index (C-index), calibration plots, area under the receiver operator characteristics (ROC) curve, and decision curve analysis (DCA) were used to validate the validity and clinical utility of the model. Patients were categorized into high- and low-risk profiles, and their survival outcomes were contrasted using the Kaplan-Meier method and the log-rank test. Results Age, marriage, AJCC stage, tumor size, surgery, and chemotherapy were identified as predictors of OS. In the training cohort, internal validation cohort and external validation cohort, the nomogram for predicting OS achieved C-index values of 0.718 (95% CI: 0.712–0.724), 0.714 (95% CI: 0.704–0.724), and 0.725 (95% CI: 0.641–0.809), respectively. In all cohorts, the calibration plots revealed high consistency between actual and predicted values. The nomogram depicted by ROC and DCA showcased superior stability, predictive value, and clinical applicability for 1, 3-, and 5-year OS. The risk stratification delineated patients into low- and high-risk brackets, revealing significant prognostic distinctions ( P < 0.05). Conclusions Based on the SEER database and Chinese data, we developed a reliable nomogram forecasting 1-, 3-, and 5-year OS for UCB patients. The model can identifie high-risk patients, aiding clinicians in personalised treatment and prognostic evaluations. Urothelial carcinoma of the bladder nomogram prognosis overall survival SEER Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Bladder cancer ranks as the tenth most prevalent cancer globally. Urothelial carcinoma of the bladder (UBC) represents about 90% of all bladder cancer cases, with a notably high occurrence in Western countries ( 1 , 2 ). About 25% of UCB are muscle invasive bladder cancer (MIBC) and 75% are non-muscle invasive bladder cancer (NMIBC)( 3 ). While current therapy strategies include surgery, radiotherapy, chemotherapy, and molecularly targeted therapy ( 4 , 5 ), they offer limited improvement in patient prognosis. MIBC is very aggressive and has a poor prognosis. Even after radical cystectomy and lymphadenectomy, the 5-year survival rate for MIBC patients is only 38% and only 6% for those with distant metastases( 6 ). Despite the high survival rate of NMIBC, the 5-year postoperative recurrence rate of NMIBC is still as high as 80%, and 15% of NMIBC progress to MIBC after five years( 7 , 8 ). Therefore, the prognosis of patients with MIBC and NMIBC must be considered. The AJCC system, commonly used for UCB as with other cancers, primarily focuses on the tumor's status, regional lymph nodes, and distant metastases. Yet, it overlooks crucial demographic details and treatment approaches, which can be pivotal in determining prognosis. For instance, advanced age post-radical cystectomy has been linked to less favorable outcomes ( 9 ). This underscores the need for advanced tools in precision medicine to better predict UCB outcomes Nomograms, graphical representations for statistical predictions, can enhance the precision of forecasting outcomes in cancer patients ( 10 ). Their application spans various tumor prognosis studies ( 11 – 15 ). Given that nomograms consider a range of vital clinicopathological aspects and offer individualized survival estimates, they've sometimes outperformed the AJCC in accuracy ( 16 ). Interestingly, future AJCC staging manuals hint at leveraging nomograms to advance personalized healthcare ( 17 ). Thus, our research focuses on devising reasonable nomograms to forecast OS in UCB patients, aiding in informed clinical decisions. In this study, we utilized clinical data on UCB patients from the SEER database, a prominent source for US cancer statistics, as well as collecting information on UCB patient data from a medical institution in China. Employing both univariate and multivariate Cox regression analyses, we formulated advanced nomograms to forecast 1-, 3-, and 5-year probabilities of OS for UCB patients. The nomogram was subsequently assessed both internally and externally using various metrics, including the consistency index (C-index), calibration plots, area under the receiver operator characteristics (ROC) curve, and decision curve analysis (DCA). We also grouped patients by risk level and compared their survival outcomes using the Kaplan-Meier method and the log-rank test. Materials We sourced patient data from the Surveillance, Epidemiology, and End Results (SEER) database, employing the SEER*Stat 8.4.0.1 tool (2000–2017 dataset). In addition, we collected information on patients with a pathological diagnosis of UCB from Guangxi Zhuang Autonomous Region Ethnic Hospital from 2012 to 2022. Inclusion criteria included: ( 1 ) the tumor-site ICD-9 codes were C67.0–C67.9, and the ICD-O-3 code was 8130/3. Exclusion criteria included: ( 1 ) survival time unknown; ( 2 ) race unknown; ( 3 ) marital status unknown; ( 4 ) age unknown; ( 5 ) AJCC stage unknown; ( 6 ) tumor size unknown. Following these criteria, eligible UCB patients from the SEER database were randomized into the training and internal validation cohorts at a ratio of 7:3. Data from the Guangxi Zhuang Autonomous Region Ethnic Hospital were used as external validation cohort. The training cohort facilitated variable filtration and nomogram construction, while the internal validation cohort and external validation cohort served to verify outcomes derived from the training cohort. The data selection process is detailed in Fig. 1. The variables in the SEER database consist of demographic characteristics (age, sex, race, and marital status), tumor characteristics (AJCC stage and tumor size), treatment information (surgery, radiotherapy, and chemotherapy), and survival messages (survival month and survival status). We made certain adjustments to some variables: ( 1 ) age (< 60 years, 60–69 years, 70–79 years, and ≥ 80 years); ( 2 ) sex (female and male); ( 3 ) race (white, black, and other); ( 4 ) Marital statuses were grouped into single, married, or separated/divorced/widowed (SDW); ( 5 ) AJCC stage (Stage Ⅰ and Ⅱ-Ⅳ); ( 6 ) surgery (no/unknown and yes); ( 7 ) radiotherapy (no/unknown and yes); ( 8 ) chemotherapy (no/unknown and yes); ( 9 ) tumor size (< 30mm and ≥ 30mm). Our analysis primarily focused on OS. OS represented the duration from pathological confirmation to any cause of death. Baseline characteristics of the UCB patient groups were assessed using the Chi-square test. We initially conducted a univariate Cox regression analysis to pinpoint variables significantly influencing OS or CSS ( P < 0.05). Following this, we included these variables in a multivariate Cox analysis to discern the independent impact of each on survival. Results were presented as hazard ratios (HR) with their associated 95% confidence intervals (95%CI). The nomogram were developed based on multivariate Cox analysis to predict OS at 1-, 3-, and 5-year. The accuracy and feasibility of the model were tested utilizing the C-index. A C-index value larger than 0.7 indicated a superior prediction model ( 18 ). Calibration plots were employed to validate the nomograms; a near 45° alignment of predicted values with the calibration sample confirmed the model's accuracy. Model performance was evaluated through the ROC curve ( 19 ). The DCA was utilized to evaluate the clinical benefit. Additionally, X-tile software was utilized to identify an optimal risk score cutoff point, resulting in the stratification of patients into high- and low-risk groups. Survival outcomes for these risk clusters were plotted utilizing the Kaplan-Meier method and contrasted via log-rank tests. The data analysis was completed utilizing SPSS version 23.0 (IBM Corp, Armonk, NY), X-tile software version 3.6.1 (Yale University School of Medicine, New Haven, CT, USA), and R statistical software (version 4.2.1, http://www.r-project.org ). P -value < 0.05 (2-sided) was deemed statistically significant. Results Baseline Characteristics In this study, we identified 15,606 UCB patients who fit our study criteria between 2004 and 2015 for the SEER database, and collected 122 patients (101 males and 21 females) from Minzu Hospital of Guangxi Zhuang Autonomous Region between 2012 and 2022. These patients from the SEER database were randomized into a training cohort (n = 10,926) and a internal validation cohort (n = 4,680) in a 7:3 ratio. The median OS is 32 months in the SEER database and 47.5 months in the external validation cohort. In the SEER database, the overall median age was 67 years, with an interquartile range of 62–77 years. A significant proportion were male (78.4%), identified as white (89.6%), and were married (58.2%). Tumors were primarily larger than 30 mm (76.1%). Based on the pathologic tissues, we combined AJCC stage II, AJCC stage III, and AJCC stage IV into AJCC stages II-IV for further analysis. AJCC stage I and AJCC stages II-IV accounted for 58.4% and 41.6%, respectively. Most underwent surgical treatment (94.1%) but declined both radiotherapy (87.1%) and chemotherapy (70.5%). Both cohorts displayed similar demographic and clinical profiles ( P > 0.05), as detailed in Table 1 . Clinical data from the external validation cohort were also presented in Table 1 . Construction of Nomogram Within the training cohort, the univariate analysis and multivariate analysis identified age, marital status, AJCC stage, tumor size, surgery, and chemotherapy as factors influencing OS (Table 2 ). The nomogram were developed to project 1-, 3-, and 5-year probabilities for OS. The most influential variables for OS prognosis were AJCC stage, surgery, and marital status. In the nomogram, each factor was assigned a specific score. By aggregating these scores, we obtained a comprehensive score that could be used to determine the 1-, 3-, and 5-year likelihood of OS (Fig. 2). Additionally, to simplify individual prognosis predictions for UCB patients, we've tabulated the nomogram scores for all variables in Table 3 . Evaluation and Validation of the OS Nomogram We used C index, calibration curve, ROC values and DCA to assess the performance and clinical utility of the nomogram. In the training cohort, internal validation cohort, and the external validation cohort, the C-index of OS nomogram was 0.718 (95% CI: 0.712–0.724), 0.714 (95%CI: 0.704–0.724), and 0.725 (95%CI: 0.641–0.809), respectively. Meanwhile, the 1-, 3-, and 5-year ROC values of the nomogram were 0.801, 0.775, and 0.749 for the training cohort, 0.791, 0.769, and 0.757 for the internal validation cohort, and 0.827, 0.717, and 0.730 for the external validation cohort, respectively (Fig. 3). The calibration curves for three cohorts exhibited a strong concordance between the estimates provided by the nomogram and the observed survival probabilities at 1-, 3-, and 5-years, signifying reliable discrimination and calibration capabilities of the models (Fig. 4). Furthermore, we assessed the clinical efficacy of our nomogram against the AJCC system. The DCA curves indicated superior predictions for OS using our nomogram, providing enhanced guidance for treatment decisions across a wide range of probability thresholds (Figs. 5). Collectively, these findings emphasize that our nomogram can offer precise clinical prognosis. Risk Rating Based on Nomograms To define risk categories for each patient, we determined the optimal risk score cut-off points by means of the X-tile software, leading to the identification of two distinct risk groups: low and high risk. Specifically, the optimal risk score thresholds were 122.02 for the training cohort, 128.2 for the internal validation cohort, and 109.9 for the external validation cohort. Based on these thresholds, UCB patients were stratified as: 0 to 122.02, 123.69 to 269.57 for the training cohort, 0-128.15, 129.50-272.88 for the internal validation cohort, and 1.64-109.89, 111.53-141.69 for the external validation cohort. Using the Kaplan-Meier method, we plotted survival curves for these risk groups, with the log-rank tests facilitating statistical comparisons. All survival curves revealed significant disparities between the two groups ( P < 0.001) (Fig. 6). Discussion In our research, we employed a widely accepted random splitting approach ( 20 , 21 ) to divide our sample of 15,606 patients into training and internal validation cohorts, and also collected clinical data from 122 Chinese UCB patients as an external validation cohort. The multifactorial Cox analysis highlighted factors such as age, marital status, AJCC stage, tumor dimensions, surgery, and chemotherapy as key predictors of OS. Importantly, by stratifying patients into high- and low-risk categories, we enhanced the prognostic precision for long-term outcomes, aiding in informed decision-making. We also found that our model exhibited superior discrimination and accuracy in forecasting the probability of 1-, 3-, and 5-year survival, as evidenced by various performance metrics such as the C-index, calibration plots, and ROC curves. Additionally, our model demonstrated better clinical applicability than the AJCC stage. In essence, the validated UCB predictive model serve as a valuable tool for understanding patient characteristics and guiding clinical interventions. In our study, we found that AJCC staging emerged as the most robust predictor for OS, consistent with the findings reported by Drakaki, A. et al. ( 22 ). Enhancing the precision of prognostic models is achievable by combining AJCC staging with other clinical prognostic markers, a sentiment supported in studies across various cancer types ( 23 – 25 ). Not surprisingly, the AJCC staging primarily reflects the severity of the tumor. Our nomogram incorporate two therapeutic components: surgery and chemotherapy. Historically, surgery was the primary intervention for UCB patients. In our analysis, those who underwent surgical procedures exhibited notably superior survival rates. A significant proportion of UCB patients show favorable 10-year metastasis-free after cystectomy ( 26 ). Moreover, individuals undergoing transurethral resection of bladder tumors experience better OS than their counterparts receiving alternate or no surgical treatments ( 27 ). Chemotherapy also plays a pivotal role in enhancing OS for UCB patients. A body of research indicates that patients undergoing chemotherapy typically outlive those who don't ( 28 , 29 ). However, our study fail to recognize the independent prognostic relevance of radiotherapy, which is similar to the findings of Zhiqiang Yang ( 30 ). Some studies have even found that radiotherapy was revealed to have detrimental effects ( 31 , 32 ). Consequently, further research is warranted to better comprehend the impact of radiotherapy on UCB. In light of these results, we caution against the excessive use of radiotherapy in UCB patients. Furthermore, our nomogram underscored the detrimental prognosis linked to larger tumor sizes in UCB cases, a finding echoed in various studies ( 33 ). At the same time, Our analysis found marital status to be a decisive factor in OS among UCB patients. Specifically, married individuals exhibited more favorable outcomes than their single or other patients. Multiple studies have confirmed that married bladder cancer patients have a lower mortality rate than unmarried and SDW patients ( 31 , 34 ). This disparity can be attributed to the better financial capabilities of married patients, facilitating access to superior treatments and care. Furthermore, single bladder cancer patients demonstrate a higher propensity for post-treatment psychiatric diagnoses than married ones. Notably, a psychiatric diagnosis is often linked to a less favorable bladder cancer prognosis ( 35 ), highlighting the interplay between mental health and survival outcomes in UCB patients. Age plays a pivotal role in predicting the prognosis of various tumors. As individuals age, a waning immune system can hasten tumor growth and decrease overall survival time. He H et al. have found that in older patients, decreased physical abilities and increased comorbidities significantly increase perioperative mortality and postoperative complications, resulting in a greater risk of bladder cancer-related mortality ( 31 , 36 , 37 ). In our study, age had a smaller influence on UCB patients' prognosis. The research by Huang C et al. ( 38 ) has revealed that age may not be an independent predictor. It's worth noting that selection biases, such as patient exclusion, can skew age-related conclusions. Enhancing sample sizes might offer more refined insights into the role of age. For effective UCB patient management, we classified patients into low- and high-risk categories using the nomogram. The Kaplan-Meier curves and log-rank tests revealed pronounced disparities between these risk groups. Risk stratification is vital in identifying high-risk individuals, enabling precise surgical interventions and enhanced monitoring. For example, a 71-year-old single white male with UCB, diagnosed at AJCC stage Ⅳ, having a tumor size exceeding 30 mm, who underwent surgery and chemotherapy but not radiotherapy. The cumulative scores from each predictor yielded nomogram scores of 139.68 for OS. According to the nomogram, the expected 1-, 3-, and 5-year survival rates for OS were 61.10%, 35.71%, and 23.93%, respectively. Given these values, this patient would be categorized as high risk, warranting enhanced care due to his unfavorable prognosis. The AJCC system has long been the primary tool for prognosticating UCB outcomes. Yet, its limitations are evident. The AJCC often groups UCB patients with varied survival outcomes under a single stage, introducing heterogeneity. This disparity arises as the AJCC system overlooks factors such as age, gender, race, marital status, and treatment type. Our nomogram, which integrated demographic and clinicopathologic features, offered a more comprehensive prognostic tool than the AJCC system, enhancing predictive accuracy and clinical decision support. The DCA underscored the increased clinical utility of our nomogram in predicting survival. Our study does present certain limitations. Firstly, most of our cases were sourced from the SEER database, which primarily encompasses data on American patients, thus limiting the generalizability of the nomograms to Asian or European populations. Secondly, the external validation data for this study came from a single center with a small sample size. Thus reducing the representativeness of our patients. Thirdly, important prognostic factors like underlying disease status, comorbidities, treatment-related complications, and details regarding the quality of care were not collected ( 38 , 39 ). Lastly, given the retrospective nature of our study, inherent biases associated with such an approach exist. We recommend further studies in the future using randomized, multicenter, large sample size prospective clinical data for enhanced credibility. Conclusion In conclusion, our study developed novel predictive a model to assess the risk in patients with UCB. The nomogram efficiently predicted OS over 1-, 3-, and 5-year periods, offering clinicians a valuable tool for personalized treatment planning. Declarations Acknowledgments We’d like to express our gratitude to the SEER database for allowing us to access free and open data. We also thank the Minzu Hospital of Guangxi Zhuang Autonomous Region for providing us with external validation data. Authors’ contributions Jufang Wei: conceived and designed the study, also collected and processed the data, and wrote the manuscript. Chunmeng Wei and Juan Chen: critically reviewed the manuscript. Wenpiao Zhao: revised the format of the manuscript. Xianhui Zhang: collected the data. All authors contributed to the article and approved the submitted version. Funding This work received no specific funding. Conflict of interest The authors declared that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Availability of data and materials The data of training cohort and internal validation cohort were obtained from the official website of SEER database (http://seer.cancer.gov/data/). The data of external validation cohort were obtained from the medical record information system of Minzu Hospital of Guangxi Zhuang Autonomous Region. Ethics approval and consent to participate This retrospective study was approved by the Ethics Committee of Minzu Hospital of Guangxi Zhuang Autonomous Region (No. [2024] 41). Competing interests The author declares no competing interests. 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Tables Table 1 Characteristics of patients with UCB enrolled in training cohort, internal validation cohort, and external validation cohort. Characteristics Training cohort (n = 10926) Internal validation cohort (n = 4680) Total (n = 15606) P -value External validation cohort (n = 122) Age 0.744 <60 2031(18.6) 894(19.1) 2925(18.7) 39(32.0) 60–69 3224(29.5) 1344(28.7) 4568(29.3) 28(23.0) 70–79 3959(36.2) 1700(36.3) 5659(36.3) 41(33.6) ≥ 80 1712(15.7) 742(15.9) 2454(15.7) 14(11.5) Sex 0.993 Female 2361(21.6) 1011(21.6) 3372(21.6) 21(17.2) Male 8565(78.4) 3669(78.4) 12234(78.4) 101(82.8) Race 0.724 White 9772(89.4) 4206(89.9) 13978(89.6) - Black 692(6.3) 281(6.0) 973(6.2) - Other a 462(4.2) 193(4.1) 655(4.2) 122(100.0) Marital status 0.124 Single 1291(11.8) 593(12.7) 1884(12.1) 4(3.3) Married 6339(58.0) 2739(58.5) 9078(58.2) 110(90.2) SDW b 3296(30.2) 1348(28.8) 4644(29.8) 8(6.6) AJCC stage 0.088 Ⅰ 6429(58.8) 2685(57.4) 9114(58.4) 68(55.7) Ⅱ-Ⅳ 4497(41.2) 1995(42.6) 6492(41.6) 54(44.3) Surgery 0.807 No/unknown 645(5.9) 281(6.0) 926(5.9) 7(5.7) Yes 10281(94.1) 4399(94.0) 14680(94.1) 115(94.3) Radiotherapy 0.097 No/unknown 9552(87.4) 4046(86.5) 13598(87.1) 115(94.3) Yes 1374(12.6) 634(13.5) 2008(12.9) 7(5.7) Chemotherapy 0.560 No/unknown 7720(70.7) 3285(70.2) 11005(70.5) 46(37.7) Yes 3206(29.3) 1395(29.8) 4601(29.5) 76(62.3) Tumor size(mm) 0.445 <30 2590(23.7) 1136(24.3) 3726(23.9) 66(54.1) ≥ 30 8336(76.3) 3544(75.7) 11880(76.1) 56(45.9) a other, American Indian/AK Native, and Asian/Pacific Islander. b SDW, separated, divorced, and widowed. Table 2 Univariate and multivariate Cox analysis of risk factors for OS in UCB patients. Characteristic Univariate analyses Multivariate analyses HR (95% CI) P -value HR (95% CI) P -value Age <60 Reference Reference 60–69 (0.982–1.070) 0.922 1.027(0.957–1.103) 0.448 70–79 (1.002–1.148) 0.043 1.101(1.029–1.179) 0.005 ≥ 80 (0.925–1.090) 0.917 1.030(0.948–1.118) 0.488 Sex Female Reference Male 1.048(0.988–1.110) 0.118 Race White Reference Black 0.992(0.900-1.094) 0.880 Other a 0.971(0.863–1.093) 0.626 Marital status Single Reference Reference Married 0.815(0.754–0.881) < 0.001 0.909(0.841–0.983) 0.016 SDW b 1.400(1.292–1.518) < 0.001 1.390(1.282–1.507) < 0.001 AJCC stage Ⅰ Reference Reference Ⅱ-Ⅳ 3.393(3.232–3.563) < 0.001 3.291(3.118–3.474) < 0.001 Surgery No/unknown Reference Reference Yes 0.333(0.305–0.363) < 0.001 0.391(0.358–0.427) < 0.001 Radiotherapy No/unknown Reference Reference Yes 1.970(1.847–2.101) < 0.001 Chemotherapy No/unknown Reference Reference Yes 1.085(1.030–1.144) < 0.001 0.746(0.705–0.790) < 0.001 Tumor size(mm) <30 Reference Reference ≥ 30 1.575(1.484–1.672) < 0.001 1.327(1.249–1.410) < 0.001 a other, American Indian/AK Native, and Asian/Pacific Islander. b SDW, separated, divorced, and widowed. Table 3 Nomogram-based scores of all variables of UCB patients. Variables Classifications Scores of OS age <60 0 60–69 2 70–79 8 ≥ 80 2 race White 4 Black 0 Other 2 marital_status Single 8 Married 0 SDW a 36 tumor_size <30 0 ≥ 30 24 AJCC stage Ⅰ 0 Ⅱ-Ⅳ 100 surgery No/unknown 79 Yes 0 radiotherapy No/unknown 0 Yes 3 chemotherapy No/unknown 25 Yes 0 a SDW, separated, divorced, and widowed. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4076346","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":279361622,"identity":"c10c92e1-ab38-4d5b-a2f3-af0ca7b6d773","order_by":0,"name":"Jufang Wei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIie3RoQvCQBTH8TcGZ3manwgzmIWTwUAUDf4jG8Is/gFrKsKSaFUQ/BcEwXxwcOnWrUMwLWg3WDXtbIL37R94Px6AzfaDscpSiDvvzzYghRmpoYrybRI7u4UKzYhH05aPWjpHqbnhYTBlVE2lC0o/LgUMvPa8lGSK6umEOavs1N3D2A9EGXHWMXXSHrqUnRsIIjqXEhcDilKXWLO4GRKGPhd6xBE0MyTIonyexCGB8rt7brClebgK+eT9cChkfimSgVdKPiI0fM07+VbYbDbbX/QCbgBEKmbHv4QAAAAASUVORK5CYII=","orcid":"","institution":"Minzu Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":true,"prefix":"","firstName":"Jufang","middleName":"","lastName":"Wei","suffix":""},{"id":279361623,"identity":"1a30739f-101c-4f56-9ad3-99564bb50b8d","order_by":1,"name":"Chunmeng Wei","email":"","orcid":"","institution":"First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chunmeng","middleName":"","lastName":"Wei","suffix":""},{"id":279361624,"identity":"2032c0b8-c3e1-4e53-af0d-fed56df0563a","order_by":2,"name":"Juan Chen","email":"","orcid":"","institution":"Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Chen","suffix":""},{"id":279361625,"identity":"64ab9c14-9ef0-4cdc-a79c-a907e0da4b56","order_by":3,"name":"Wenpiao Zhao","email":"","orcid":"","institution":"The Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Wenpiao","middleName":"","lastName":"Zhao","suffix":""},{"id":279361627,"identity":"3ccc854a-12ea-4de1-8f48-14137d397f99","order_by":4,"name":"Xianhui Zhang","email":"","orcid":"","institution":"Minzu Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Xianhui","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-03-11 16:01:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4076346/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4076346/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52790330,"identity":"14824021-989d-47bd-ad25-bee1c00b5d53","added_by":"auto","created_at":"2024-03-15 19:49:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":848689,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4076346/v1/9e2c129ee71d41643644ed73.png"},{"id":52790336,"identity":"dfb5d6fd-a115-4234-b24a-459879e767ec","added_by":"auto","created_at":"2024-03-15 19:49:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":583269,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4076346/v1/220ec6e386a6bc05d4e545a1.png"},{"id":52790324,"identity":"3e078cde-9cfe-4a30-b98b-912f1f289c1f","added_by":"auto","created_at":"2024-03-15 19:49:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":882315,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4076346/v1/6bdb8aa8f8d905a9cc1317f8.png"},{"id":52790332,"identity":"d6b028cc-07bd-47f3-a355-c594cb50e1d6","added_by":"auto","created_at":"2024-03-15 19:49:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":853082,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4076346/v1/8eeee82c73fbb0c31c351cd1.png"},{"id":52790325,"identity":"50125061-aa4b-4544-b945-c283e295142c","added_by":"auto","created_at":"2024-03-15 19:49:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":933353,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4076346/v1/0aba5f6c5501785c717d9f9f.png"},{"id":52790331,"identity":"21c569cc-6ab9-4ec2-8744-2d6b15c9ed8e","added_by":"auto","created_at":"2024-03-15 19:49:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1382556,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4076346/v1/c0555b435b8f6b9064ca2560.png"},{"id":53700531,"identity":"dff8550a-d228-41f3-b262-e1ac9443f371","added_by":"auto","created_at":"2024-03-29 05:06:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1291586,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4076346/v1/e619c635-4bca-4492-bf50-124d5770bbba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and External Validation of a Nomogram to Predict Prognosis of Patients With Urothelial Carcinoma of Bladder","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBladder cancer ranks as the tenth most prevalent cancer globally. Urothelial carcinoma of the bladder (UBC) represents about 90% of all bladder cancer cases, with a notably high occurrence in Western countries (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). About 25% of UCB are muscle invasive bladder cancer (MIBC) and 75% are non-muscle invasive bladder cancer (NMIBC)(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). While current therapy strategies include surgery, radiotherapy, chemotherapy, and molecularly targeted therapy (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), they offer limited improvement in patient prognosis. MIBC is very aggressive and has a poor prognosis. Even after radical cystectomy and lymphadenectomy, the 5-year survival rate for MIBC patients is only 38% and only 6% for those with distant metastases(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Despite the high survival rate of NMIBC, the 5-year postoperative recurrence rate of NMIBC is still as high as 80%, and 15% of NMIBC progress to MIBC after five years(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Therefore, the prognosis of patients with MIBC and NMIBC must be considered.\u003c/p\u003e \u003cp\u003eThe AJCC system, commonly used for UCB as with other cancers, primarily focuses on the tumor's status, regional lymph nodes, and distant metastases. Yet, it overlooks crucial demographic details and treatment approaches, which can be pivotal in determining prognosis. For instance, advanced age post-radical cystectomy has been linked to less favorable outcomes (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This underscores the need for advanced tools in precision medicine to better predict UCB outcomes\u003c/p\u003e \u003cp\u003eNomograms, graphical representations for statistical predictions, can enhance the precision of forecasting outcomes in cancer patients (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Their application spans various tumor prognosis studies (\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Given that nomograms consider a range of vital clinicopathological aspects and offer individualized survival estimates, they've sometimes outperformed the AJCC in accuracy (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Interestingly, future AJCC staging manuals hint at leveraging nomograms to advance personalized healthcare (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Thus, our research focuses on devising reasonable nomograms to forecast OS in UCB patients, aiding in informed clinical decisions.\u003c/p\u003e \u003cp\u003eIn this study, we utilized clinical data on UCB patients from the SEER database, a prominent source for US cancer statistics, as well as collecting information on UCB patient data from a medical institution in China. Employing both univariate and multivariate Cox regression analyses, we formulated advanced nomograms to forecast 1-, 3-, and 5-year probabilities of OS for UCB patients. The nomogram was subsequently assessed both internally and externally using various metrics, including the consistency index (C-index), calibration plots, area under the receiver operator characteristics (ROC) curve, and decision curve analysis (DCA). We also grouped patients by risk level and compared their survival outcomes using the Kaplan-Meier method and the log-rank test.\u003c/p\u003e"},{"header":"Materials","content":"\u003cp\u003eWe sourced patient data from the Surveillance, Epidemiology, and End Results (SEER) database, employing the SEER*Stat 8.4.0.1 tool (2000\u0026ndash;2017 dataset). In addition, we collected information on patients with a pathological diagnosis of UCB from Guangxi Zhuang Autonomous Region Ethnic Hospital from 2012 to 2022. Inclusion criteria included: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the tumor-site ICD-9 codes were C67.0\u0026ndash;C67.9, and the ICD-O-3 code was 8130/3. Exclusion criteria included: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) survival time unknown; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) race unknown; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) marital status unknown; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) age unknown; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) AJCC stage unknown; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) tumor size unknown. Following these criteria, eligible UCB patients from the SEER database were randomized into the training and internal validation cohorts at a ratio of 7:3. Data from the Guangxi Zhuang Autonomous Region Ethnic Hospital were used as external validation cohort. The training cohort facilitated variable filtration and nomogram construction, while the internal validation cohort and external validation cohort served to verify outcomes derived from the training cohort. The data selection process is detailed in Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eThe variables in the SEER database consist of demographic characteristics (age, sex, race, and marital status), tumor characteristics (AJCC stage and tumor size), treatment information (surgery, radiotherapy, and chemotherapy), and survival messages (survival month and survival status).\u003c/p\u003e \u003cp\u003eWe made certain adjustments to some variables: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) age (\u0026lt;\u0026thinsp;60 years, 60\u0026ndash;69 years, 70\u0026ndash;79 years, and \u0026ge;\u0026thinsp;80 years); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) sex (female and male); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) race (white, black, and other); (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Marital statuses were grouped into single, married, or separated/divorced/widowed (SDW); (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) AJCC stage (Stage Ⅰ and Ⅱ-Ⅳ); (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) surgery (no/unknown and yes); (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) radiotherapy (no/unknown and yes); (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) chemotherapy (no/unknown and yes); (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) tumor size (\u0026lt;\u0026thinsp;30mm and \u0026ge;\u0026thinsp;30mm). Our analysis primarily focused on OS. OS represented the duration from pathological confirmation to any cause of death.\u003c/p\u003e \u003cp\u003eBaseline characteristics of the UCB patient groups were assessed using the Chi-square test. We initially conducted a univariate Cox regression analysis to pinpoint variables significantly influencing OS or CSS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Following this, we included these variables in a multivariate Cox analysis to discern the independent impact of each on survival. Results were presented as hazard ratios (HR) with their associated 95% confidence intervals (95%CI).\u003c/p\u003e \u003cp\u003eThe nomogram were developed based on multivariate Cox analysis to predict OS at 1-, 3-, and 5-year. The accuracy and feasibility of the model were tested utilizing the C-index. A C-index value larger than 0.7 indicated a superior prediction model (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Calibration plots were employed to validate the nomograms; a near 45\u0026deg; alignment of predicted values with the calibration sample confirmed the model's accuracy. Model performance was evaluated through the ROC curve (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The DCA was utilized to evaluate the clinical benefit. Additionally, X-tile software was utilized to identify an optimal risk score cutoff point, resulting in the stratification of patients into high- and low-risk groups. Survival outcomes for these risk clusters were plotted utilizing the Kaplan-Meier method and contrasted via log-rank tests.\u003c/p\u003e \u003cp\u003eThe data analysis was completed utilizing SPSS version 23.0 (IBM Corp, Armonk, NY), X-tile software version 3.6.1 (Yale University School of Medicine, New Haven, CT, USA), and R statistical software (version 4.2.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (2-sided) was deemed statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline Characteristics\u003c/p\u003e \u003cp\u003eIn this study, we identified 15,606 UCB patients who fit our study criteria between 2004 and 2015 for the SEER database, and collected 122 patients (101 males and 21 females) from Minzu Hospital of Guangxi Zhuang Autonomous Region between 2012 and 2022. These patients from the SEER database were randomized into a training cohort (n\u0026thinsp;=\u0026thinsp;10,926) and a internal validation cohort (n\u0026thinsp;=\u0026thinsp;4,680) in a 7:3 ratio. The median OS is 32 months in the SEER database and 47.5 months in the external validation cohort. In the SEER database, the overall median age was 67 years, with an interquartile range of 62\u0026ndash;77 years. A significant proportion were male (78.4%), identified as white (89.6%), and were married (58.2%). Tumors were primarily larger than 30 mm (76.1%). Based on the pathologic tissues, we combined AJCC stage II, AJCC stage III, and AJCC stage IV into AJCC stages II-IV for further analysis. AJCC stage I and AJCC stages II-IV accounted for 58.4% and 41.6%, respectively. Most underwent surgical treatment (94.1%) but declined both radiotherapy (87.1%) and chemotherapy (70.5%). Both cohorts displayed similar demographic and clinical profiles (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Clinical data from the external validation cohort were also presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eConstruction of Nomogram\u003c/p\u003e \u003cp\u003eWithin the training cohort, the univariate analysis and multivariate analysis identified age, marital status, AJCC stage, tumor size, surgery, and chemotherapy as factors influencing OS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The nomogram were developed to project 1-, 3-, and 5-year probabilities for OS. The most influential variables for OS prognosis were AJCC stage, surgery, and marital status. In the nomogram, each factor was assigned a specific score. By aggregating these scores, we obtained a comprehensive score that could be used to determine the 1-, 3-, and 5-year likelihood of OS (Fig.\u0026nbsp;2). Additionally, to simplify individual prognosis predictions for UCB patients, we've tabulated the nomogram scores for all variables in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eEvaluation and Validation of the OS Nomogram\u003c/p\u003e \u003cp\u003eWe used C index, calibration curve, ROC values and DCA to assess the performance and clinical utility of the nomogram. In the training cohort, internal validation cohort, and the external validation cohort, the C-index of OS nomogram was 0.718 (95% CI: 0.712\u0026ndash;0.724), 0.714 (95%CI: 0.704\u0026ndash;0.724), and 0.725 (95%CI: 0.641\u0026ndash;0.809), respectively. Meanwhile, the 1-, 3-, and 5-year ROC values of the nomogram were 0.801, 0.775, and 0.749 for the training cohort, 0.791, 0.769, and 0.757 for the internal validation cohort, and 0.827, 0.717, and 0.730 for the external validation cohort, respectively (Fig.\u0026nbsp;3). The calibration curves for three cohorts exhibited a strong concordance between the estimates provided by the nomogram and the observed survival probabilities at 1-, 3-, and 5-years, signifying reliable discrimination and calibration capabilities of the models (Fig.\u0026nbsp;4). Furthermore, we assessed the clinical efficacy of our nomogram against the AJCC system. The DCA curves indicated superior predictions for OS using our nomogram, providing enhanced guidance for treatment decisions across a wide range of probability thresholds (Figs.\u0026nbsp;5). Collectively, these findings emphasize that our nomogram can offer precise clinical prognosis.\u003c/p\u003e \u003cp\u003eRisk Rating Based on Nomograms\u003c/p\u003e \u003cp\u003eTo define risk categories for each patient, we determined the optimal risk score cut-off points by means of the X-tile software, leading to the identification of two distinct risk groups: low and high risk. Specifically, the optimal risk score thresholds were 122.02 for the training cohort, 128.2 for the internal validation cohort, and 109.9 for the external validation cohort. Based on these thresholds, UCB patients were stratified as: 0 to 122.02, 123.69 to 269.57 for the training cohort, 0-128.15, 129.50-272.88 for the internal validation cohort, and 1.64-109.89, 111.53-141.69 for the external validation cohort. Using the Kaplan-Meier method, we plotted survival curves for these risk groups, with the log-rank tests facilitating statistical comparisons. All survival curves revealed significant disparities between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;6).\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eIn our research, we employed a widely accepted random splitting approach (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) to divide our sample of 15,606 patients into training and internal validation cohorts, and also collected clinical data from 122 Chinese UCB patients as an external validation cohort. The multifactorial Cox analysis highlighted factors such as age, marital status, AJCC stage, tumor dimensions, surgery, and chemotherapy as key predictors of OS. Importantly, by stratifying patients into high- and low-risk categories, we enhanced the prognostic precision for long-term outcomes, aiding in informed decision-making. We also found that our model exhibited superior discrimination and accuracy in forecasting the probability of 1-, 3-, and 5-year survival, as evidenced by various performance metrics such as the C-index, calibration plots, and ROC curves. Additionally, our model demonstrated better clinical applicability than the AJCC stage. In essence, the validated UCB predictive model serve as a valuable tool for understanding patient characteristics and guiding clinical interventions.\u003c/p\u003e \u003cp\u003eIn our study, we found that AJCC staging emerged as the most robust predictor for OS, consistent with the findings reported by Drakaki, A. et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Enhancing the precision of prognostic models is achievable by combining AJCC staging with other clinical prognostic markers, a sentiment supported in studies across various cancer types (\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Not surprisingly, the AJCC staging primarily reflects the severity of the tumor.\u003c/p\u003e \u003cp\u003eOur nomogram incorporate two therapeutic components: surgery and chemotherapy. Historically, surgery was the primary intervention for UCB patients. In our analysis, those who underwent surgical procedures exhibited notably superior survival rates. A significant proportion of UCB patients show favorable 10-year metastasis-free after cystectomy (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Moreover, individuals undergoing transurethral resection of bladder tumors experience better OS than their counterparts receiving alternate or no surgical treatments (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Chemotherapy also plays a pivotal role in enhancing OS for UCB patients. A body of research indicates that patients undergoing chemotherapy typically outlive those who don't (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). However, our study fail to recognize the independent prognostic relevance of radiotherapy, which is similar to the findings of Zhiqiang Yang (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Some studies have even found that radiotherapy was revealed to have detrimental effects (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Consequently, further research is warranted to better comprehend the impact of radiotherapy on UCB. In light of these results, we caution against the excessive use of radiotherapy in UCB patients. Furthermore, our nomogram underscored the detrimental prognosis linked to larger tumor sizes in UCB cases, a finding echoed in various studies (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the same time, Our analysis found marital status to be a decisive factor in OS among UCB patients. Specifically, married individuals exhibited more favorable outcomes than their single or other patients. Multiple studies have confirmed that married bladder cancer patients have a lower mortality rate than unmarried and SDW patients (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). This disparity can be attributed to the better financial capabilities of married patients, facilitating access to superior treatments and care. Furthermore, single bladder cancer patients demonstrate a higher propensity for post-treatment psychiatric diagnoses than married ones. Notably, a psychiatric diagnosis is often linked to a less favorable bladder cancer prognosis (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), highlighting the interplay between mental health and survival outcomes in UCB patients.\u003c/p\u003e \u003cp\u003eAge plays a pivotal role in predicting the prognosis of various tumors. As individuals age, a waning immune system can hasten tumor growth and decrease overall survival time. He H et al. have found that in older patients, decreased physical abilities and increased comorbidities significantly increase perioperative mortality and postoperative complications, resulting in a greater risk of bladder cancer-related mortality (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). In our study, age had a smaller influence on UCB patients' prognosis. The research by Huang C et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) has revealed that age may not be an independent predictor. It's worth noting that selection biases, such as patient exclusion, can skew age-related conclusions. Enhancing sample sizes might offer more refined insights into the role of age.\u003c/p\u003e \u003cp\u003eFor effective UCB patient management, we classified patients into low- and high-risk categories using the nomogram. The Kaplan-Meier curves and log-rank tests revealed pronounced disparities between these risk groups. Risk stratification is vital in identifying high-risk individuals, enabling precise surgical interventions and enhanced monitoring. For example, a 71-year-old single white male with UCB, diagnosed at AJCC stage Ⅳ, having a tumor size exceeding 30 mm, who underwent surgery and chemotherapy but not radiotherapy. The cumulative scores from each predictor yielded nomogram scores of 139.68 for OS. According to the nomogram, the expected 1-, 3-, and 5-year survival rates for OS were 61.10%, 35.71%, and 23.93%, respectively. Given these values, this patient would be categorized as high risk, warranting enhanced care due to his unfavorable prognosis.\u003c/p\u003e \u003cp\u003eThe AJCC system has long been the primary tool for prognosticating UCB outcomes. Yet, its limitations are evident. The AJCC often groups UCB patients with varied survival outcomes under a single stage, introducing heterogeneity. This disparity arises as the AJCC system overlooks factors such as age, gender, race, marital status, and treatment type. Our nomogram, which integrated demographic and clinicopathologic features, offered a more comprehensive prognostic tool than the AJCC system, enhancing predictive accuracy and clinical decision support. The DCA underscored the increased clinical utility of our nomogram in predicting survival.\u003c/p\u003e \u003cp\u003eOur study does present certain limitations. Firstly, most of our cases were sourced from the SEER database, which primarily encompasses data on American patients, thus limiting the generalizability of the nomograms to Asian or European populations. Secondly, the external validation data for this study came from a single center with a small sample size. Thus reducing the representativeness of our patients. Thirdly, important prognostic factors like underlying disease status, comorbidities, treatment-related complications, and details regarding the quality of care were not collected (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Lastly, given the retrospective nature of our study, inherent biases associated with such an approach exist. We recommend further studies in the future using randomized, multicenter, large sample size prospective clinical data for enhanced credibility.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study developed novel predictive a model to assess the risk in patients with UCB. The nomogram efficiently predicted OS over 1-, 3-, and 5-year periods, offering clinicians a valuable tool for personalized treatment planning.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe\u0026rsquo;d like to express our gratitude to the SEER database for allowing us to access free and open data.\u0026nbsp;We also thank the Minzu Hospital of Guangxi Zhuang Autonomous Region for providing us with external validation data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJufang Wei:\u0026nbsp;conceived and designed the study,\u0026nbsp;also\u0026nbsp;collected and processed the data, and wrote the manuscript.\u0026nbsp;Chunmeng Wei\u0026nbsp;and\u0026nbsp;Juan Chen:\u0026nbsp;critically reviewed the manuscript.\u0026nbsp;Wenpiao Zhao:\u0026nbsp;revised the format of the manuscript.\u0026nbsp;Xianhui Zhang:\u0026nbsp;collected the data.\u0026nbsp;All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work received no specific funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of training cohort and internal validation cohort were obtained from the official website of SEER database (http://seer.cancer.gov/data/). The data of external validation cohort were obtained from the medical record information system of Minzu Hospital of Guangxi Zhuang Autonomous Region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was approved by the Ethics Committee of Minzu Hospital of Guangxi Zhuang Autonomous Region\u0026nbsp;(No. [2024] 41).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRichters A, Aben KKH, Kiemeney L. The global burden of urinary bladder cancer: an update. World J Urol. 2020;38(8):1895\u0026ndash;904.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBabjuk M, Bohle A, Burger M, Capoun O, Cohen D, Comperat EM, et al. 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Int J Urol. 2001;8(7):366\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\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\u003eCharacteristics of patients with UCB enrolled in training cohort, internal validation cohort, and external validation cohort.\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=\"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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\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\" colname=\"c2\"\u003e \u003cp\u003eTraining cohort\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10926)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInternal validation cohort\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4680)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15606)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExternal validation cohort\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;122)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.744\u003c/p\u003e \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\u003e\u0026lt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2031(18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e894(19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2925(18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39(32.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3224(29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1344(28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4568(29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28(23.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3959(36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1700(36.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5659(36.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41(33.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1712(15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e742(15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2454(15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(11.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.993\u003c/p\u003e \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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2361(21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1011(21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3372(21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21(17.2)\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\u003e8565(78.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3669(78.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12234(78.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101(82.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.724\u003c/p\u003e \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\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9772(89.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4206(89.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13978(89.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e692(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e281(6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e973(6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e462(4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193(4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e655(4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e122(100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.124\u003c/p\u003e \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\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1291(11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e593(12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1884(12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(3.3)\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\u003e6339(58.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2739(58.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9078(58.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110(90.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDW\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3296(30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1348(28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4644(29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8(6.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAJCC stage\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.088\u003c/p\u003e \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\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6429(58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2685(57.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9114(58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68(55.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅡ-Ⅳ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4497(41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1995(42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6492(41.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54(44.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgery\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.807\u003c/p\u003e \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\u003eNo/unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e645(5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e281(6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e926(5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7(5.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10281(94.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4399(94.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14680(94.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e115(94.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRadiotherapy\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.097\u003c/p\u003e \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\u003eNo/unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9552(87.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4046(86.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13598(87.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e115(94.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1374(12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e634(13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2008(12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7(5.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.560\u003c/p\u003e \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\u003eNo/unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7720(70.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3285(70.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11005(70.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46(37.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3206(29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1395(29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4601(29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76(62.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor size(mm)\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.445\u003c/p\u003e \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\u003e\u0026lt;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2590(23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1136(24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3726(23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66(54.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8336(76.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3544(75.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11880(76.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56(45.9)\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\u003e \u003csup\u003ea\u003c/sup\u003eother, American Indian/AK Native, and Asian/Pacific Islander.\u003csup\u003eb\u003c/sup\u003eSDW, separated, divorced, and widowed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate Cox analysis of risk factors for OS in UCB 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analyses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analyses\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \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\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.982\u0026ndash;1.070)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.027(0.957\u0026ndash;1.103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.002\u0026ndash;1.148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.101(1.029\u0026ndash;1.179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.925\u0026ndash;1.090)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.030(0.948\u0026ndash;1.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\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 \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\u003eReference\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 \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.048(0.988\u0026ndash;1.110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.118\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 \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\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 \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\u003eReference\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 \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.992(0.900-1.094)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.880\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 \u003cp\u003eOther\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.971(0.863\u0026ndash;1.093)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.626\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 \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \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\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.815(0.754\u0026ndash;0.881)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.909(0.841\u0026ndash;0.983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDW\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.400(1.292\u0026ndash;1.518)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.390(1.282\u0026ndash;1.507)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAJCC stage\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \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\u003eⅡ-Ⅳ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.393(3.232\u0026ndash;3.563)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.291(3.118\u0026ndash;3.474)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgery\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.333(0.305\u0026ndash;0.363)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.391(0.358\u0026ndash;0.427)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRadiotherapy\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.970(1.847\u0026ndash;2.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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 \u003cp\u003e\u003cb\u003eChemotherapy\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.085(1.030\u0026ndash;1.144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.746(0.705\u0026ndash;0.790)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor size(mm)\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \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\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.575(1.484\u0026ndash;1.672)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.327(1.249\u0026ndash;1.410)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;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\u003e \u003csup\u003ea\u003c/sup\u003eother, American Indian/AK Native, and Asian/Pacific Islander.\u003csup\u003eb\u003c/sup\u003eSDW, separated, divorced, and widowed.\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\u003eNomogram-based scores of all variables of UCB patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\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\u003eClassifications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScores of OS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\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\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\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\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\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 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\u003cp\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\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\u003eⅡ-Ⅳ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003esurgery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo/unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eradiotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo/unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003echemotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo/unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\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\u003e \u003csup\u003ea\u003c/sup\u003eSDW, separated, divorced, and widowed.\u003c/p\u003e "}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Urothelial carcinoma of the bladder, nomogram, prognosis, overall survival, SEER","lastPublishedDoi":"10.21203/rs.3.rs-4076346/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4076346/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis research aimed to create and validate nomogram predicting overall survival (OS) for urothelial carcinoma of the bladder (UCB) patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe sourced 15,606 UCB patients diagnosed between 2004 and 2015 from the Surveillance, Epidemiology, and End Results database. The patients were randomized into training (70%) and internal validation (30%) cohorts. In addition, 122 patients from Minzu Hospital of Guangxi Zhuang Autonomous Region between 2012 and 2022 were selected as the external validation cohort. Utilizing univariate and multivariate Cox regression analyses, we devised nomograms forecasting 1-, 3-, and 5-year OS. Several metrics, including the consistency index (C-index), calibration plots, area under the receiver operator characteristics (ROC) curve, and decision curve analysis (DCA) were used to validate the validity and clinical utility of the model. Patients were categorized into high- and low-risk profiles, and their survival outcomes were contrasted using the Kaplan-Meier method and the log-rank test.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAge, marriage, AJCC stage, tumor size, surgery, and chemotherapy were identified as predictors of OS. In the training cohort, internal validation cohort and external validation cohort, the nomogram for predicting OS achieved C-index values of 0.718 (95% CI: 0.712\u0026ndash;0.724), 0.714 (95% CI: 0.704\u0026ndash;0.724), and 0.725 (95% CI: 0.641\u0026ndash;0.809), respectively. In all cohorts, the calibration plots revealed high consistency between actual and predicted values. The nomogram depicted by ROC and DCA showcased superior stability, predictive value, and clinical applicability for 1, 3-, and 5-year OS. The risk stratification delineated patients into low- and high-risk brackets, revealing significant prognostic distinctions (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBased on the SEER database and Chinese data, we developed a reliable nomogram forecasting 1-, 3-, and 5-year OS for UCB patients. The model can identifie high-risk patients, aiding clinicians in personalised treatment and prognostic evaluations.\u003c/p\u003e","manuscriptTitle":"Development and External Validation of a Nomogram to Predict Prognosis of Patients With Urothelial Carcinoma of Bladder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-15 19:49:22","doi":"10.21203/rs.3.rs-4076346/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5f56c94e-1c5d-4b8f-8c7a-57fb7dad2d54","owner":[],"postedDate":"March 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-29T04:58:03+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-15 19:49:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4076346","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4076346","identity":"rs-4076346","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}