Nomogram Prediction Model for Clear Cell Carcinoma of the Female Reproductive Tract: A SEER Database Study

Nomogram Prediction Model for Clear Cell Carcinoma of the Female Reproductive Tract: A SEER Database Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Nomogram Prediction Model for Clear Cell Carcinoma of the Female Reproductive Tract: A SEER Database Study Suyu Li, Liyuan Huang, Hang Lin, Leilei Zhu, Guangrun Zhou, Jimiao Huang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9317653/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Clear cell carcinoma (CCC) of the female reproductive tract is a rare and aggressive malignancy that lacks an established prognostic prediction model. This study aimed to develop and validate a robust nomogram to predict survival outcomes and facilitate personalized risk stratification for CCC patients. Methods A total of 7,409 patients confirmed to have CCC between 2004 and 2019 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. They were further divided into a model-development cohort (n = 5,187) and a validation cohort (n = 2,222) in a 7:3 ratio. Independent prognostic factors were identified via multivariable regression analysis to construct a predictive nomogram. The discrimination and calibration of the nomogram were evaluated using the concordance index (C-index), the area under the receiver operating characteristic curve (AUC), and calibration plots. The clinical utility and net benefits of the nomogram at different threshold probabilities were quantified using decision curve analysis (DCA). Furthermore, risk stratifications were performed based on the nomogram-derived scores, and survival disparities across risk groups were compared using Kaplan-Meier curves and log-rank tests. Results Eight variables, including primary site, age, tumor size, stage, surgery, chemotherapy, diatant metastasis, and lymph node status were selected to establish the prognostic nomogram for CCC. The model demonstrated robust discriminative performance, with AUC values of 0.795 (12-month) and 0.779 (24-month) in the development cohort, and 0.814 and 0.798 in the validation cohort, respectively. Notably, Harrell’s C-index exceeded 0.7 in both cohorts, while calibration curves indicated high concordance between predicted and observed survival. Risk stratification effectively categorized patients into groups with distinct survival outcomes; the median survival was 130 months for the low-risk group ( P < 0.0001). Notably, ovarian CCC showed a higher incidence and superior prognosis compared to uterine and cervical subtypes. Advanced age, larger tumor size, and distant metastasis were significant predictors of poor outcomes. Conclusions A prognostic nomogram was developed and validated to assist clinicians in evaluating the survival outcomes of patients with CCC of the female reproductive tract. clear cell carcinoma (CCC) prognostic prediction model nomogram SEER database gynecologic malignancies Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Clear cell carcinoma (CCC) is a rare and highly aggressive subtype of gynecological tumors, characterized by its low incidence but poor prognosis. Originating from the Müllerian ducts in the female reproductive system, CCC is distinguished by its unique cellular morphology, particularly the presence of glycogen-rich clear cytoplasm and the hallmark hobnail cells[ 1 ]. Among the female reproductive organs, Ovarian CCC (OCCC) is the most common, followed by corpus uteri CCC (UCCC), with cervical CCC (CCAC) being extremely rare [ 2 ]. OCCC accounts for approximately 4.5% to 10% of all epithelial ovarian cancer (EOC) cases [ 3 – 5 ]. According to the literature, the most common non-endometrioid histological subtype among malignant endometrial carcinoma is serous carcinoma, followed by UCCC, with an incidence rate of 1% to 6%, which is lower than that of OCCC [ 6 ]. CCAC is extremely rare, representing only 4% of all cervical adenocarcinomas [ 7 ]. CCC is prone to early distant metastasis, and advanced-stage CCC often shows poor response to treatment, resulting in a poor prognosis [ 8 , 9 ]. This underscores the challenges in timely diagnosis, effective treatment, and accurate prognostic assessment. Given that CCC exhibits distinct clinical features and biological behaviors depending on its site of origin, understanding the prognostic differences and associated risk factors across different locations is crucial for improving patient outcomes. OCCC is associated with malignant transformation of ovarian endometriosis [ 10 ], while studies have reported that in utero exposure to diethylstilbestrol during pregnancy may lead to CCAC [ 4 ]. However, Stolnicu et al. have proposed that CCAC may develop from cervical endometriosis or tubal endometrioid metaplasia either and is not associated with HPV infection [ 11 ]. This suggests a certain homology in the pathogenesis of reproductive system CCC. Therefore, understanding these prognostic differences and associated risk factors at various sites is crucial for developing personalized treatment strategies for CCC. Previous studies have identified various clinicopathological factors, such as tumor stage, age, grade, and extent of resection, as being associated with the prognosis of patients with CCC [ 12 , 13 ]. However, relying on a single factor is inherently limited. In contrast, a multivariable nomogram model that integrates these clinically relevant factors offers a more comprehensive analysis and holds significant clinical utility [ 14 ]. Therefore, this study is aim to develop a multivariable prognostic prediction model using multicenter retrospective data, including Adjusted R-squared (Adiusted-R), Bayesian information criterion (BIC), and Least Absolute Shrinkage and Selection Operator (LASSO). Through both training and internal validation cohort, we anticipate this model will provide valuable insights for assessing CCC in the female reproductive tract, stratify risk factors, and help optimize personalized clinical management strategies for CCC patients. Methods Study participants Clinical data for this study were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database, specifically the SEER 17 registries (November 2021 submission), covering the period from 2000 to 2019. Since the SEER database is a publicly accessible resource containing de-identified patient information, this study was exempt from Institutional Review Board (IRB) approval. Proper authorization was obtained to access the data for research purposes. To ensure the validity of the research results, we implemented the following selection criteria. The inclusion criteria were as follows: (i) primary malignancy located in the cervix, corpus uteri, or ovary; (ii) histologically confirmed Clear Cell Carcinoma (CCC) according to the International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3) morphology code 8310; (iii) diagnosed between 2004 and 2019. The exclusion criteria were as follows: (i) patients aged < 18 years at diagnosis; (ii) unknown or missing data regarding survival months or vital status; (iii) other histological types or non-primary malignancies,the flow chat can be seen in Fig. 1 . Data collection and group To obtain records relevant to the study of CCC in the female reproductive tract, SEER*Stat software (version 8.4.3), developed by the National Cancer Institute (NCI) in Washington, D.C., was utilized for data retrieval. The variables included in the study were race (white, others), patient age (≤ 60, > 60), stage (I/unknown, II, III, IV), site within the female reproductive tract (cervix, corpus uteri, ovary), grade (I-II, III-IV), tumor size ( 4 cm, unknown), ragional lymph node status (negative/unknown, positive), distant metastasis was defined as the presence or absence of metastatic lesions in the bone, brain, lung, or liver at the time of diagnosis (yes/no), surgical procedures (yes/no), radiation therap (no/unknown/yes), chemotherapy (no/unknown/yes), cancer-specific survival (CSS) (alive or dead of other cause, dead attributable to this cancer), overall survival (OS) (Alive, dead), and other pertinent clinical and pathological details. Baseline tumor staging for the included cohort was standardized by integrating multiple classification systems, including the SEER-modified AJCC (3rd edition), Derived SEER Combined Stage Group, Derived AJCC Stage Group (6th and 7th editions), and the Derived EOD 2018 Stage Group. This comprehensive data extraction process ensured the inclusion of all relevant variables necessary for the study analysis. Statistical analysis Data Partitioning and Baseline Comparison The study population (n = 7,409) was randomly assigned into a development cohort (n = 5,187) and a validation cohort (n = 2,222) in a 7:3 ratio using the createDataPartition function from the caret package in R. To compare categorical variables, we employed the chi-square test and Pearson's chi-square test for the analysis between different groups. Continuous variables were expressed as means ± standard deviations (SD) for normally distributed data or medians with interquartile ranges (IQR) for skewed distributions, with comparisons performed using Student’s t-test or the Mann-Whitney U test, respectively. Variable Selection and Nomogram Construction The prognostic nomogram was developed through a structured multi-step approach. Initially, univariate and multivariate Cox regression analyses were performed to identify features significantly associated with OS. To ensure the parsimony and stability of the model, three advanced variable selection methods—Adiusted-R 2 , BIC, and LASSO regression—were employed. For LASSO, the optimal tuning parameter λ was determined via ten-fold cross-validation. Variables consistently identified by these methods were incorporated into the final multivariable model to construct the nomogram. Model Evaluation and Validation The predictive performance of the nomogram was rigorously evaluated in both the the model-developmen and validation cohorts. Discrimination was assessed using Harrell’s concordance index (C-index) and time-dependent receiver operating characteristic (ROC) curves with area under the curve (AUC) values. Calibration was evaluated via calibration plots to compare predicted versus observed survival probabilities. Furthermore, decision curve analysis (DCA) was performed to quantify the clinical net benefit and utility of the model. Data analysis for this research was performed using the R statistical software package ( http://www.R-project.org ). Statistical significance was assessed using two-tailed tests, and P < 0.05 was considered statistically significant. Results Baseline Clinical Characteristics This study cohort comprised 7,409 patients diagnosed with CCC between 2004 and 2019, categorized by primary tumor site: ovary (OCCC, 59.6%), corpus uteri (UCCC, 34.6%), and cervix (CCAC, 5.7%). Age-stratified analysis indicated that the incidence of UCCC was significantly higher among elderly women (> 60 years), whereas OCCC was more prevalent in younger patients (< 60 years) ( P < 0.001). Distinct clinical outcomes and pathological features were observed across the three sites. The survival rate was highest for OCCC (65.0%), followed by CCAC (53.9%) and UCCC (52.0%). Regarding lymphatic spread, UCCC exhibited the highest rate of regional lymph node involvement (18.2%), compared to 13.7% for CCAC and 10.9% for OCCC. Furthermore, CCAC demonstrated the highest frequency of distant metastasis (5.9%), followed by UCCC (4.0%) and OCCC (2.7%). Specifically, lung and brain metastasis rates in the CCAC group were 4.0% and 0.9%, respectively, which were significantly higher than those in the UCCC and OCCC groups. While significant differences were observed in tumor grade, stage, size, treatment modalities, CSS, and OS across the three groups ( P < 0.001), no significant disparities were found regarding race or liver metastasis (Table 1 ). Table 1 Comparison of demographic and clinical features among different primary sites of clear cell adenocarcinoma. Characteristics Whole populationn n (%) Cervix n (%) Corpus Uteri n (%) Ovary n (%) P -value Total 7409 423 2567 4419 Race 0.115 white 5507 (74.3) 327 (77.3) 1877 (73.1) 3303 (74.7) others 1902 (25.7) 96 (22.7) 690 (26.9) 1116 (25.3) Age 60 3769 (50.9) 198 (46.8) 2016 (78.5) 1555 (35.2) Stage < 0.001 I/Unknown 4991 (67.4) 256 (60.5) 1613 (62.8) 3122 (70.6) II 569 ( 7.7) 41 (9.7) 182 (7.1) 346 (7.8) III 1184 (16.0) 79 (18.7) 450 (17.5) 655 (14.8) IV 665 ( 9.0) 47 (11.1) 322 (12.5) 296 (6.7) Grade < 0.001 I-II 479 ( 6.5) 43 (10.2) 123 (4.8) 313 (7.1) III-IV 6930 (93.5) 380 (89.8) 2444 (95.2) 4106 (92.9) Tumor Size < 0.001 4 cm 3000 (40.5) 109 (25.8) 540 (21) 2351 (53.2) Unknown 3396 (45.8) 199 (47) 1468 (57.2) 1729 (39.1) Surgery < 0.001 No/Unknown 630 ( 8.5) 164 (38.8) 325 (12.7) 141 (3.2) Yes 6779 (91.5) 259 (61.2) 2242 (87.3) 4278 (96.8) Radiotherapy < 0.001 No/Unknown 6029 (81.4) 159 (37.6) 1528 (59.5) 4342 (98.3) Yes 1380 (18.6) 264 (62.4) 1039 (40.5) 77 (1.7) Chemotherapy < 0.001 No/Unknown 2537 (34.2) 200 (47.3) 1333 (51.9) 1004 (22.7) Yes 4872 (65.8) 223 (52.7) 1234 (48.1) 3415 (77.3) Ragional lymph nodes < 0.001 Negative/Unknown 6405 (86.4) 365 (86.3) 2101 (81.8) 3939 (89.1) Positive 1004 (13.6) 58 (13.7) 466 (18.2) 480 (10.9) Bone 0.044 No/Unknown 7355 (99.3) 418 (98.8) 2542 (99) 4395 (99.5) Yes 54 ( 0.7) 5 (1.2) 25 (1) 24 (0.5) Brain 0.003 No/Unknown 7399 (99.9) 419 (99.1) 2564 (99.9) 4416 (99.9) Yes 10 ( 0.1) 4 (0.9) 3 (0.1) 3 (0.1) Liver 0.378 No/Unknown 7289 (98.4) 417 (98.6) 2532 (98.6) 4340 (98.2) Yes 120 ( 1.6) 6 (1.4) 35 (1.4) 79 (1.8) Pulmonary < 0.001 No/Unknown 7278 (98.2) 406 (96) 2506 (97.6) 4366 (98.8) Yes 131 ( 1.8) 17 (4) 61 (2.4) 53 (1.2) Distant metastasis < 0.001 No 7160 (96.6) 398 (94.1) 2464 (96) 4298 (97.3) Yes 249 ( 3.4) 25 (5.9) 103 (4) 121 (2.7) Css < 0.001 Alive or dead of other cause 5104 (68.9) 277 (65.5) 1699 (66.2) 3128 (70.8) Dead (attributable to this cancer) 2305 (31.1) 146 (34.5) 868 (33.8) 1291 (29.2) Survival months, Median (IQR) 37.0 (14.0, 89.0) 33.0 (12.0, 85.5) 30.0 (11.0, 76.0) 43.0 (17.0, 96.0) < 0.001 Vital status < 0.001 Alive 4437 (59.9) 228 (53.9) 1336 (52) 2873 (65) Dead 2972 (40.1) 195 (46.1) 1231 (48) 1546 (35) Abbreviations: Css: Cancer-Specific Survival; IQR: Interquartile Range. Characteristics of the Development and Validation Cohorts The study population was randomly assigned to a development cohort (n = 5,187) and an internal validation cohort (n = 2,222), with detailed characteristics provided in Table 2 . As survival time followed a skewed distribution, data are presented as medians with IQR. The median survival for the development and validation cohorts was 62.0 (23.0, 116.0) months and 20.0 (9.0, 41.0) month, respectively. No significant differences were observed between the two cohorts in terms of race, age, stage, CCC group, tumor size, grade, ragional lymph node status, surgery, radiotherapy, or diatant metastasis ( P > 0.05), indicating that the cohorts were well-balanced and comparable, thereby ensuring the stability and feasibility of the subsequent prognostic model. Table 2 Demographic and clinical characteristics of patients with clear cell carcinoma in the model-development and validation cohorts. Characteristics Whole population n (%) Model-development n (%) Validation cohort n (%) P -value Total 7409 5187 2222 Race 0.171 white 5507 (74.3) 3879 (74.8) 1628 (73.3) others 1902 (25.7) 1308 (25.2) 594 (26.7) Age 0.379 ≤ 60 3640 (49.1) 2531 (48.8) 1109 (49.9) > 60 3769 (50.9) 2656 (51.2) 1113 (50.1) Stage 0.807 I/Unknown 4991 (67.4) 3493 (67.3) 1498 (67.4) II 569 ( 7.7) 401 (7.7) 168 (7.6) III 1184 (16.0) 837 (16.1) 347 (15.6) IV 665 ( 9.0) 456 (8.8) 209 (9.4) group 0.429 Cervix 423 ( 5.7) 306 (5.9) 117 (5.3) Corpus Uteri 2567 (34.6) 1807 (34.8) 760 (34.2) Ovary 4419 (59.6) 3074 (59.3) 1345 (60.5) Grade 0.286 I-II 479 ( 6.5) 325 (6.3) 154 (6.9) III-IV 6930 (93.5) 4862 (93.7) 2068 (93.1) Tumor Size 0.921 4 cm 3000 (40.5) 2097 (40.4) 903 (40.6) Unknown 3396 (45.8) 2372 (45.7) 1024 (46.1) Ragional lymph node 0.717 Negative/Unknown 6405 (86.4) 4489 (86.5) 1916 (86.2) Positive 1004 (13.6) 698 (13.5) 306 (13.8) Distant metastasis 0.924 No 7160 (96.6) 5012 (96.6) 2148 (96.7) Yes 249 ( 3.4) 175 (3.4) 74 (3.3) Surgery 0.86 No/Unknown 630 ( 8.5) 443 (8.5) 187 (8.4) Yes 6779 (91.5) 4744 (91.5) 2035 (91.6) Radiotherapy 0.596 No/Unknown 6029 (81.4) 4229 (81.5) 1800 (81) Yes 1380 (18.6) 958 (18.5) 422 (19) Chemotherapy 0.909 No/Unknown 2537 (34.2) 1774 (34.2) 763 (34.3) Yes 4872 (65.8) 3413 (65.8) 1459 (65.7) Css 0.373 Alive or dead of other cause 5104 (68.9) 3557 (68.6) 1547 (69.6) Dead (attributable to this cancer) 2305 (31.1) 1630 (31.4) 675 (30.4) Survival months, Median (IQR) 37.0 (14.0, 89.0) 20.0 (9.0, 41.0) 62.0 (23.0, 116.0) < 0.001 Vital status 0.228 Alive 4437 (59.9) 3083 (59.4) 1354 (60.9) Dead 2972 (40.1) 2104 (40.6) 868 (39.1) Abbreviations: Css: Cancer-Specific Survival; IQR: Interquartile Range. Univariate and Multivariate Analysis for Clear-cell Carcinoma Univariate Cox regression analysis demonstrated that group, age, stage, grade, tumor size, primary site surgery, lymph node status, chemotherapy, and diatant metastasis were significantly associated with OS. Subsequently, these variables were incorporated into a multivariate Cox regression analysis. The rusults identified age, stage, grade, tumor size, primary site surgery, radiotherapy, chemotherapy, lymph node status, and diatant metastasis as independent prognostic factors for OS. Detailed results of both univariate and multivariate analyses are presented in Table S1 . Variable Selection To ensure the robust selection of prognostic predictors, three complementary statistical approaches—Adjusted-R 2 , BIC and LASSO regression—were employed. Adjusted-R 2 method identified an optimal subset of seven variables (age, tumor size, stage, surgery, radiotherapy, chemotherapy, and distant metastasis), achieving a maximum value of 0.260 (Fig. 2 A, B). Conversely, BIC selected a more parsimonious model comprising six variables (age, tumor size, stage, surgery, chemotherapy, and distant metastasis) with a minimum BIC value of -2161.6 (Fig. 2 C, D). All variables selected across these methods demonstrated statistical significance ( P < 0.05). Finally, LASSO regression, utilizing an optimal tuning parameter (λ = 0.015) determined via cross-validation, identified six core predictors: age, stage, surgery, radiotherapy, chemotherapy, and distant metastasis. The coefficient profile plot against the sequence of ln (λ) is depicted in Fig. 2 E and 2 F. Development of Model Integrating the findings from the univariate and multivariate Cox regression analyses, alongside the variable selection methods (Adjusted R 2 , BIC, and LASSO regression), eight key predictors were ultimately incorporated into the final model: group, age, tumor size, stage, surgery, chemotherapy, lymph node status, and distant metastasis. Based on these variables, a prognostic nomogram was constructed to evaluate its clinical utility (Fig. 3 ). Model Performance and Validation The prognostic model exhibited robust predictive performance and excellent calibration (Fig. 4 ). In the development cohort, the model achieved an AUC of 0.795 (95% CI: 0.777–0.813) for 12-month survival and 0.779 (95% CI: 0.763–0.794) for 24-month survival, demonstrating favorable discrimination. The Hosmer-Lemeshow goodness-of-fit test revealed R² values of 0.102 for the 12-month model and 0.149 for the 24-month model. In the internal validation cohort (Fig. 5 ), the 12-month and 24-month AUC values were 0.814 (95% CI: 0.788–0.840) and 0.798 (95% CI: 0.775–0.822), respectively. The corresponding Brier scores were 0.098 and 0.142, reflecting low prediction error and high accuracy. Furthermore, Harrell’s C-index consistently exceeded 0.7 in both cohorts. The calibration curves for both time points closely aligned with the 45-degree reference line, further confirming the high concordance between the predicted probabilities and actual observed outcomes. Clinical Utility Patients were stratified into high- and low-risk groups based on the integrated risk assessment tool. The DCA was performed to evaluate the clinical utility of the nomogram (Figs. 4 and 5 ). The DCA curves demonstrated that both the 12-month and 24-month models provided a significant net benefit over the "All-treatment" and "No-treatment" strategies across a wide range of threshold probabilities. These findings indicate that the nomogram is a reliable and effective tool for guiding clinical decision-making. Survival Analysis and Risk Stratification To evaluate the clinical utility of the nomogram, patients were stratified into three risk tiers (low, medium, and high) based on the predicted 12-month and 24-month survival probabilities, using cut-off points of 0.4 and 0.7. Kaplan-Meier survival curves demonstrated significant divergence among these groups (Figs. 6 A, B; P < 0.0001). For patients stratified by the 12-month and 24-month models, the median overall survival (mOS) in the low-risk group was approximately 130 months and 145 months, respectively—significantly longer than that observed in the medium- and high-risk groups. Regarding anatomical subtypes, patients with OCCC exhibited a markedly superior survival profile compared to those with CCAC and UCCC ( P < 0.0001, Fig. 6 C). Furthermore, the absence of distant organ metastasis was associated with a dramatic improvement in mOS compared to the presence of metastasis (130 months vs. 15 months, Fig. 6 D). Subgroup analyses further identified that younger age, earlier stage, smaller tumor size, and negative regional lymph nodes were significantly correlated with prolonged OS (Figs. 6 E-H). In terms of therapeutic interventions, both surgical resection and chemotherapy were found to significantly enhance survival outcomes (Figs. 6 I-J). In contrast, radiotherapy did not exert a statistically significant impact on patient prognosis ( P = 0.68, Fig. 6 K). These findings emphasize that early diagnosis and appropriate multimodal treatment remain the cornerstones of improved survival for patients with CCC. Discussion In this large-scale population-based study leveraging the SEER database, we developed and validated a robust prognostic nomogram for female reproductive tract CCC. Our model exhibited superior predictive accuracy and clinical utility, effectively stratifying patients into distinct risk cohorts with a profound survival disparity (median OS: >130 months vs. <20 months). A key finding of our analysis is the prognostic heterogeneity among anatomical subtypes, where OCCC demonstrated a significantly more favorable survival profile than UCCC and CCAC—a divergence likely driven by the higher prevalence of early-stage diagnosis in ovarian cases. Furthermore, we identified advanced age, tumor size, and distant metastasis as critical determinants of poor prognosis, while highlighting the pivotal role of surgical intervention combined with chemotherapy in improving overall survival. These findings provide a reliable tool for personalized risk assessment and clinical decision-making. CCC a relatively rare histological subtype characterized by its aggressive clinical course and a generally unfavorable prognosis. Within the female reproductive tract, CCC predominantly manifests in the ovary (OCCC, 59.64%), followed by the corpus uteri (UCCC, 34.65%) and the cervix (CCAC, 5.71%). Our findings regarding the superior prognosis of OCCC compared to UCCC and CCAC are consistent with the results reported by Rauh-Hain et al.[ 13 ], who observed that OCCC patients achieved a significantly longer median progression-free survival (PFS, 145 months) and overall survival (OS, 155.8 months) than UCCC patients (PFS: 31.4 months; OS: 39.5 months; P < 0.05). Current National Comprehensive Cancer Network (NCCN) guidelines advocate for comprehensive staging surgery as the gold standard for early-stage CCC [ 15 ]. Given that approximately 50% of OCCC and 44% of UCCC cases are diagnosed at Stage I, surgical intervention remains pivotal in optimizing clinical outcomes [ 16 ]. Similarly, CCAC derives substantial benefit from early-stage resection, with reported 3-year survival rates reaching 91% for Stage I–IIA disease [ 17 ]. However, compared to high-grade serous or endometrioid carcinomas, CCC exhibits a higher propensity for extrauterine metastasis and advanced-stage presentation. At the time of diagnosis, nearly 41.6% of OCCC and 50.0% of UCCC cases have already progressed to Stage III or IV. Nonetheless, achieving R0 cytoreduction (complete tumor debulking) provides a critical survival advantage for these advanced-stage patients [ 18 ]. A major therapeutic challenge remains the intrinsic chemoresistance of CCC; the overall response rate (ORR) to first-line platinum-based chemotherapy in advanced OCCC is markedly lower than in serous epithelial ovarian cancer (sEOC) (45% vs. 81%)[ 19 ], A similar pattern of decreased chemo-sensitivity is observed in UCCC relative to endometrioid carcinoma, further underscoring the recalcitrant nature and distinct biological profile of clear cell malignancies [ 20 ] . Stage, tumor grade, and optimal cytoreduction have been established as the primary prognostic determinants in OCCC. Despite its distinct clinical profile, the 5-year survival rate for early-stage OCCC remains inferior to other epithelial ovarian cancer (EOC) subtypes, ranging from 50% to 73% [ 21 , 22 ]. For UCCC and CCAC, survival is significantly influenced by stage, depth of myometrial invasion, lymphovascular space invasion (LVSI), and the implementation of adjuvant therapies, including chemotherapy and radiotherapy [ 3 , 23 ]. In UCCC specifically, advanced age, tumor size, distant metastasis, and molecular markers such as the Ki-67 index and P53 expression serve as critical indicators for prognostic evaluation [ 24 ]. CCAC, a rare non-HPV-related cervical malignancy, [ 25 ], exhibits a 5-year overall survival (OS) rate between 40% and 78%, which precipitously declines to below 50% for Stage II and beyond [ 17 , 26 – 28 ]. These established prognostic factors and survival outcomes are highly consistent with the findings of our present study. Regardless of the primary site, CCC of the female reproductive tract demonstrates a marked propensity for lymphatic dissemination and distant metastasis [ 8 , 12 , 29 , 30 ]. Our analysis further elucidates the metastatic landscape of CCC, identifying the lungs and liver as the most frequent sites of visceral involvement, followed by the bones and brain. Consequently, comprehensive surgical staging and aggressive cytoreduction remain the cornerstones of management to optimize the prognosis of patients with CCC. The heterogeneity in adjuvant treatment regimens has led to divergent clinical outcomes across OCCC, UCCC, and CCAC. For OCCC, Masashi Takano et al. suggested that early-stage patients, particularly those with Stage IA disease, may not require adjuvant therapy given their relatively favorable prognosis, as chemotherapy did not significantly extend progression-free survival (PFS) in this subgroup [ 31 ]. Nonetheless, the optimal therapeutic strategy for OCCC remains a subject of ongoing debate. Research by Chih-Ming Ho emphasized that comprehensive surgical staging—specifically pelvic and para-aortic lymphadenectomy—combined with paclitaxel and carboplatin (TC), significantly improves survival for Stage I OCCC [ 32 ]. UCCC typically necessitates a more aggressive adjuvant approach. According to NCCN guidelines, observation, chemotherapy, or radiotherapy (RT) may be considered for Stage IA UCCC without myometrial invasion. However, chemotherapy with or without RT is the standard recommendation for Stage IA with myometrial invasion and Stages IB–IV, which are consistently classified as high-risk subtypes [ 33 ]. The GOG177 trial demonstrated that the combination of paclitaxel, doxorubicin, and cisplatin (TAP) improved overall response rates, PFS, and OS in recurrent endometrial cancer, including UCCC [ 34 ]. Subsequently, the GOG209 trial established the non-inferiority of TC compared to TAP, with a superior toxicity profile, positioning TC as the preferred first-line regimen [ 35 ]. Furthermore, RT has been shown to significantly enhance PFS in UCCC (67% vs. 36%, P = 0.02) and reduce regional recurrences. While vaginal brachytherapy may suffice for Stages I–II, patients with nodal involvement or advanced disease appear to derive greater benefit from whole-pelvic radiation [ 18 , 24 ]. In contrast, the role of adjuvant therapy for CCAC remains controversial. Some studies indicate that chemotherapy or concurrent chemoradiotherapy (CCRT) yields minimal prognostic benefit, particularly for early-stage disease without pathological risk factors [ 36 , 37 ], Conversely, other reports suggest a potential survival advantage from adjuvant interventions [ 29 ]. This discrepancy is largely attributable to small sample sizes and inconsistent staging across studies. Overall, while chemotherapy shows potential in improving CCC outcomes, the efficacy of RT appears limited and highly dependent on tumor location. Our findings align with this, showing a restricted overall benefit from radiotherapy. This underscores the critical value of our prognostic risk stratification tool in refining clinical decision-making and identifying patients who may truly benefit from intensified therapy. Emerging evidence suggests that OCCC may originate from atypical endometriosis, a hypothesis mirrored in the proposed origin of CCAC from cervical endometriosis. Loss of heterozygosity (LOH) analyses have identified clonal relationships between endometriotic lesions and adjacent malignancies, suggesting a potential pathological homology among CCCs of the female reproductive tract. Specifically, somatic mutations in ARID1A and PTEN within endometriotic foci appear to be early driver events in the pathogenesis of OCCC [ 38 ]. Furthermore, preliminary metabolic transcriptomic clustering indicates that the organ of origin (renal vs. non-renal) does not fundamentally differentiate the core biological features of CCC. Among non-renal CCCs, TP53, ARID1A, and PIK3CA emerge as the most frequently mutated genes. Notably, of the 50 hallmark gene sets analyzed, only two exhibited significant differential expression across different gynecological CCC sites. This molecular congruence suggests that targeting the PI3K-AKT-mTOR, DNA damage repair (DDR), and MYC pathways could be an effective therapeutic strategy regardless of the primary tumor site. Recent clinical investigations have explored the potential of immunotherapy in CCC, particularly given the observed high expression of PD-L1 in various gynecological locations [ 39 ]. While single-agent immune checkpoint inhibitors (ICIs) have yet to demonstrate superior efficacy over conventional chemotherapy [ 40 ], insights from renal cell carcinoma suggest that CCC may benefit more from dual immunotherapy or combinations of ICIs with targeted agents [ 40 – 43 ]. Innovative approaches, including therapeutic vaccines and targeting myeloid checkpoints—such as the CD47 "don't eat me" signal—are currently under active investigation [ 44 ]. Given that at least 20% of OCCC cases exhibit MSI-high status, mismatch repair (MMR) deficiency, or a high tumor mutational burden (TMB-H) [ 45 ], our findings further highlight the necessity of integrating clinical nomograms with novel biomarkers to guide individualized treatment strategies. Despite providing valuable prognostic insights, our study has several limitations that warrant consideration. First, its retrospective nature may introduce inherent selection and information biases. Second, while the SEER database provides a large-scale cohort, the findings may be influenced by geographic and ethnic variations, and our current analysis lacks a granular, stratified evaluation of clinical staging and treatment modalities across specific anatomical sites. Third, the current model primarily relies on clinicopathological parameters; therefore, the integration of molecular biomarkers and multi-omics data in future studies will be essential to enhance predictive precision and clinical applicability. Finally, although internal validation yielded promising results, prospective multicenter studies are required to further validate the model’s performance and ensure its generalizability across diverse clinical settings and populations. Conclusions In summary, our established nomogram underscores the necessity for intensified management and tailored therapeutic intervention for patients with intermediate-and high-risk female reproductive tract CCC. High-risk features—including advanced stage, high tumor grade, age over 60 years, tumor size exceeding 4 cm, distant metastasis, and lymph node involvement—remain robust indicators of a dismal prognosis. Notably, patients who undergo suboptimal cytoreduction (non-R0 resection) or exhibit chemoresistance face significantly worse survival outcomes. These findings emphasize the urgent need for meticulous clinical surveillance and the active exploration of individualized treatment paradigms. Future efforts should integrate genomic profiling and immune biomarker detection to refine risk stratification and improve long-term outcomes through precision oncology. Abbreviations CCC Clear cell carcinoma OCCC Ovarian CCC UCCC Uteri CCC CCAC Cervical CCC EOC Epithelial ovarian cancer BIC Bayesian information criterion LASSO Least Absolute Shrinkage and Selection Operator SEER Surveillance, Epidemiology, and End Results IRB Institutional Review Board NCI National Cancer Institute CSS Cancer-specific survival OS Overall survival SD Standard deviations IQR Interquartile ranges ROC Receiver operating characteristic DCA Decision curve analysis PFS Progression-free survival NCCN National Comprehensive Cancer Network RT Radiation therapy LOH Loss of heterozygosity MMR MSI-high status, mismatch repair C-index Concordance index AUC Area under the receiver operating characteristic curve Declarations Ethics approval and consent to participate Ethical consent was waived due to the SEER database contains anonymous patient information. All data from the SEER database were open access ( https://seer.cancer.gov/data-software/ ). Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Abbreviations BSR, best subsets regression; FSR, forward stepwise regression; BIC, Bayesian information criterion; LASSO, least absolute shrinkage and selection operator. Funding This study was supported by Fujian Provincial Natural Science Foundation of China (Grant number:2024J011054) and Joint Funds for theTechnology Innovation of Science and Fujian Province (Grant number 2024Y9586). Author Contribution Suyu Li, Jimiao Huang and Xiaoying Chen, Diling Pan conceived the study and designed the experiments. Hang Lin, Leilei Zhu and Guangrun Zhou carried out data collection and analysis. Suyu Li, Jimiao Huang and Liyuan Huang assisted in drafting the manuscript, and Xiaoying Chen revised the manuscript. All the authors reviewed and approved the final manuscript. Acknowledgement The authors are grateful for the support provided by Yusha Chen. 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Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviewers invited by journal 06 May, 2026 Editor invited by journal 09 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Submission checks completed at journal 06 Apr, 2026 First submitted to journal 04 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9317653","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":639967678,"identity":"83681e2e-97d6-4bd3-9c4d-4d9ec313da37","order_by":0,"name":"Suyu Li","email":"","orcid":"","institution":"Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Suyu","middleName":"","lastName":"Li","suffix":""},{"id":639967686,"identity":"af26ae05-93fb-405d-aa50-6b497e80a810","order_by":1,"name":"Liyuan Huang","email":"","orcid":"","institution":"Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liyuan","middleName":"","lastName":"Huang","suffix":""},{"id":639967688,"identity":"513e8c0a-e851-492d-80b8-c272a69d697c","order_by":2,"name":"Hang Lin","email":"","orcid":"","institution":"Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Lin","suffix":""},{"id":639967689,"identity":"05485d84-d85d-4a0b-b299-42ae8baabec8","order_by":3,"name":"Leilei Zhu","email":"","orcid":"","institution":"Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Leilei","middleName":"","lastName":"Zhu","suffix":""},{"id":639967697,"identity":"840a165d-a546-481f-9446-72b74fc605eb","order_by":4,"name":"Guangrun Zhou","email":"","orcid":"","institution":"Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guangrun","middleName":"","lastName":"Zhou","suffix":""},{"id":639967702,"identity":"2a007b55-e12f-4af6-b23c-df2e676dda80","order_by":5,"name":"Jimiao Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIie2RsUrEQBCGZ1mYNMPVGyLmFTYErPIwcyxYRRQL20s4iI1cfdf4DD7ChoDXBOsIFicH+gBXKoe5VFablIL7lcN8zMw/AB7PHySOP+v9Nx0XVVDu9FCyI0pS5EbTmRUravQ0BSxfKMqseFRGwyRFFJaVyt8khnu4fa3gfNaxONw4lECUVuv2AzEykF5VkIYdy2jtmrIE5vmDJIyu7UmZP3WMklybPYO29VEqDJthymJcaSEpiz4tVHJQWI8pyZqMBLpkpP6W/EUlm/Z9GbmUWAXbL6CM4/sG0/wui2dbUx+ci/0C+8coOAU/UQCQu8mtHo/H86/4AbCmSGEQCQvpAAAAAElFTkSuQmCC","orcid":"","institution":"Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jimiao","middleName":"","lastName":"Huang","suffix":""},{"id":639967705,"identity":"eec07b24-cef7-44f1-80cd-aaaa4b587347","order_by":6,"name":"Diling Pan","email":"","orcid":"","institution":"Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Diling","middleName":"","lastName":"Pan","suffix":""},{"id":639967713,"identity":"1dc90a77-871a-48b7-9485-fdd31b6691f8","order_by":7,"name":"Xiaoying Chen","email":"","orcid":"","institution":"Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoying","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-04-04 05:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9317653/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9317653/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109332376,"identity":"9e40ec81-4e0c-44c8-b433-228f17ed2c47","added_by":"auto","created_at":"2026-05-15 16:15:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":352551,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design and flowchart.\u003c/p\u003e\n\u003cp\u003eAbbreviations: SEER: Surveillance, Epidemiology, and End Results, ROC, Receiver Operating Characteristic; DCA, Decision Curve Analysis, AUC: Area under the receiver operating characteristic curve.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9317653/v1/e55b5fe364c58c5eaa1b7b22.png"},{"id":109332377,"identity":"51686978-65ef-4822-ae69-4ecd6704ba77","added_by":"auto","created_at":"2026-05-15 16:15:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":379994,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological framework for variable selection.\u003c/p\u003e\n\u003cp\u003e(A, B) Best Subset Regression (BSR) for optimal variable identification. (A) The segmented line indicates an inflection point where the maximum adjusted R\u003csup\u003e2\u003c/sup\u003e value (0.162) was achieved. (B) Distribution of adjusted R\u003csup\u003e2\u003c/sup\u003e values across different variable combinations. (C, D) Forward Stepwise Regression (FSR) analysis. (C) The inflection point identifies the minimum BIC value of -25.7, signaling the optimal model fit. (D) BIC values plotted against the number of included variables. (E) Selection of the optimal tuning parameter (λ) via ten-fold cross-validation in the LASSO model. (F) LASSO coefficient profiles of the candidate variables associated with OS in the development cohort.\u003c/p\u003e\n\u003cp\u003eAbbreviations: BSR, best subsets regression; FSR, forward stepwise regression; BIC, Bayesian information criterion; LASSO, least absolute shrinkage and selection operator.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9317653/v1/263ad85e7f16083742dd9da4.png"},{"id":109405542,"identity":"d5e59346-390a-4bb1-af52-af5a29905b55","added_by":"auto","created_at":"2026-05-17 13:18:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":281186,"visible":true,"origin":"","legend":"\u003cp\u003eA prognostic nomogram for predicting the 12-month and 24-month OS of patients with female reproductive tract CCC.\u003c/p\u003e\n\u003cp\u003eThe model was constructed using independent predictors identified in the development cohort. To estimate the survival probability, the scores for each individual variable are summed to calculate the \"Total Points,\" which are then projected onto the bottom scales.\u003c/p\u003e\n\u003cp\u003eAbbreviations: OS, overall survival; CCC, clear cell carcinoma\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9317653/v1/260cd7151e791826ccbb174a.png"},{"id":109332380,"identity":"51cd0404-5985-46a2-988a-e83441214ea7","added_by":"auto","created_at":"2026-05-15 16:15:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":552970,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance evaluation and clinical utility of the prognostic model in the development cohort.\u003c/p\u003e\n\u003cp\u003e(A) Time-dependent ROC curves showing AUC values of 0.795 (95% CI: 0.777–0.813) for 12-month and 0.779 (95% CI: 0.763–0.794) for 24-month survival. (B) Dynamic C-index curve demonstrating stable discriminative ability over a long-term follow-up period. (C, E) Calibration curves for 12-month (C) and 24-month (E) survival, illustrating high concordance between the predicted probabilities (x-axis) and actual observed outcomes (y-axis). (D, F) DCA for 12-month (D) and 24-month (F) survival, showing substantial net clinical benefit across a wide range of threshold probabilities compared to \"all-treat\" or \"none-treat\" strategies. (G) Risk stratification profile: (Upper) Distribution of patients’ risk scores with a cutoff value of -0.15; (Lower) Survival status and follow-up time for each patient, highlighting the higher mortality density in the high-risk group.\u003c/p\u003e\n\u003cp\u003eAbbreviations: ROC, Receiver Operating Characteristic; DCA, Decision Curve Analysis.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9317653/v1/48896978281afff73f9268fc.png"},{"id":109332381,"identity":"08483375-e85e-4602-abed-3396937f0fd0","added_by":"auto","created_at":"2026-05-15 16:15:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":542820,"visible":true,"origin":"","legend":"\u003cp\u003eValidation and clinical utility of the prognostic model in the internal validation cohort.\u003c/p\u003e\n\u003cp\u003e(A) Time-dependent ROC curves showing AUC values of 0.814 (95% CI: 0.788–0.840) for 12-month and 0.798 (95% CI: 0.775–0.822) for 24-month survival. The corresponding Brier scores were 0.098 and 0.142, respectively, indicating high predictive accuracy. (B) Dynamic C-index curve illustrating the stable and robust discriminative performance of the model over the follow-up period. (C, E) Calibration curves for 12-month (C) and 24-month (E) survival in the validation cohort, demonstrating strong concordance between the predicted probabilities and actual observed outcomes. (D, F) DCA for 12-month (D) and 24-month (F) survival, confirming substantial net clinical benefit across a wide range of threshold probabilities in the validation set. (G) Risk stratification profile: (Upper) Distribution of risk scores with a validated cutoff of -0.32; (Lower) Scatter plot of survival time and status, showing clear separation of outcomes between the high-risk and low-risk groups.\u003c/p\u003e\n\u003cp\u003eAbbreviations: ROC, Receiver Operating Characteristic; DCA, Decision Curve Analysis.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9317653/v1/38406da544045b0f595f57db.png"},{"id":109332382,"identity":"7e92edd3-6e8f-45c1-97bd-329954901853","added_by":"auto","created_at":"2026-05-15 16:15:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":710441,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival analysis and prognostic evaluation of clinical factors in CCC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(Figure 6-1, A, B) OS curves stratified by the nomogram-based risk scores. The low-risk group exhibited a significantly superior median survival (approximately 130 months for the 12-month model and 145 months for the 24-month model) compared to the intermediate- and high-risk groups (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9317653/v1/a71c56801ac81a716dee2b94.png"},{"id":109405826,"identity":"595c0ab8-f222-4504-8b20-fcba98792276","added_by":"auto","created_at":"2026-05-17 13:20:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3334502,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9317653/v1/a4e20fca-686d-4fff-b7cc-22decf7893fe.pdf"},{"id":109405529,"identity":"0a86aaf2-d9dc-4ae7-9601-f9ec8f519c88","added_by":"auto","created_at":"2026-05-17 13:18:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19518,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1. Univariate and multivariate Cox regression analysis in the cohort.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9317653/v1/492dd931ad71bcbdee98a9f3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nomogram Prediction Model for Clear Cell Carcinoma of the Female Reproductive Tract: A SEER Database Study","fulltext":[{"header":"Background","content":"\u003cp\u003eClear cell carcinoma (CCC) is a rare and highly aggressive subtype of gynecological tumors, characterized by its low incidence but poor prognosis. Originating from the M\u0026uuml;llerian ducts in the female reproductive system, CCC is distinguished by its unique cellular morphology, particularly the presence of glycogen-rich clear cytoplasm and the hallmark hobnail cells[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among the female reproductive organs, Ovarian CCC (OCCC) is the most common, followed by corpus uteri CCC (UCCC), with cervical CCC (CCAC) being extremely rare [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. OCCC accounts for approximately 4.5% to 10% of all epithelial ovarian cancer (EOC) cases [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. According to the literature, the most common non-endometrioid histological subtype among malignant endometrial carcinoma is serous carcinoma, followed by UCCC, with an incidence rate of 1% to 6%, which is lower than that of OCCC [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. CCAC is extremely rare, representing only 4% of all cervical adenocarcinomas [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. CCC is prone to early distant metastasis, and advanced-stage CCC often shows poor response to treatment, resulting in a poor prognosis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This underscores the challenges in timely diagnosis, effective treatment, and accurate prognostic assessment.\u003c/p\u003e \u003cp\u003eGiven that CCC exhibits distinct clinical features and biological behaviors depending on its site of origin, understanding the prognostic differences and associated risk factors across different locations is crucial for improving patient outcomes. OCCC is associated with malignant transformation of ovarian endometriosis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], while studies have reported that in utero exposure to diethylstilbestrol during pregnancy may lead to CCAC [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, Stolnicu et al. have proposed that CCAC may develop from cervical endometriosis or tubal endometrioid metaplasia either and is not associated with HPV infection [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This suggests a certain homology in the pathogenesis of reproductive system CCC. Therefore, understanding these prognostic differences and associated risk factors at various sites is crucial for developing personalized treatment strategies for CCC.\u003c/p\u003e \u003cp\u003ePrevious studies have identified various clinicopathological factors, such as tumor stage, age, grade, and extent of resection, as being associated with the prognosis of patients with CCC [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, relying on a single factor is inherently limited. In contrast, a multivariable nomogram model that integrates these clinically relevant factors offers a more comprehensive analysis and holds significant clinical utility [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, this study is aim to develop a multivariable prognostic prediction model using multicenter retrospective data, including Adjusted R-squared (Adiusted-R), Bayesian information criterion (BIC), and Least Absolute Shrinkage and Selection Operator (LASSO). Through both training and internal validation cohort, we anticipate this model will provide valuable insights for assessing CCC in the female reproductive tract, stratify risk factors, and help optimize personalized clinical management strategies for CCC patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eClinical data for this study were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database, specifically the SEER 17 registries (November 2021 submission), covering the period from 2000 to 2019. Since the SEER database is a publicly accessible resource containing de-identified patient information, this study was exempt from Institutional Review Board (IRB) approval. Proper authorization was obtained to access the data for research purposes.\u003c/p\u003e \u003cp\u003eTo ensure the validity of the research results, we implemented the following selection criteria. The inclusion criteria were as follows: (i) primary malignancy located in the cervix, corpus uteri, or ovary; (ii) histologically confirmed Clear Cell Carcinoma (CCC) according to the International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3) morphology code 8310; (iii) diagnosed between 2004 and 2019. The exclusion criteria were as follows: (i) patients aged\u0026thinsp;\u0026lt;\u0026thinsp;18 years at diagnosis; (ii) unknown or missing data regarding survival months or vital status; (iii) other histological types or non-primary malignancies,the flow chat can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection and group\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo obtain records relevant to the study of CCC in the female reproductive tract, SEER*Stat software (version 8.4.3), developed by the National Cancer Institute (NCI) in Washington, D.C., was utilized for data retrieval. The variables included in the study were race (white, others), patient age (\u0026le;\u0026thinsp;60, \u0026gt; 60), stage (I/unknown, II, III, IV), site within the female reproductive tract (cervix, corpus uteri, ovary), grade (I-II, III-IV), tumor size (\u0026lt;\u0026thinsp;2 cm, 2\u0026ndash;4 cm, \u0026gt; 4 cm, unknown), ragional lymph node status (negative/unknown, positive), distant metastasis was defined as the presence or absence of metastatic lesions in the bone, brain, lung, or liver at the time of diagnosis (yes/no), surgical procedures (yes/no), radiation therap (no/unknown/yes), chemotherapy (no/unknown/yes), cancer-specific survival (CSS) (alive or dead of other cause, dead attributable to this cancer), overall survival (OS) (Alive, dead), and other pertinent clinical and pathological details. Baseline tumor staging for the included cohort was standardized by integrating multiple classification systems, including the SEER-modified AJCC (3rd edition), Derived SEER Combined Stage Group, Derived AJCC Stage Group (6th and 7th editions), and the Derived EOD 2018 Stage Group. This comprehensive data extraction process ensured the inclusion of all relevant variables necessary for the study analysis.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eData Partitioning and Baseline Comparison\u003c/p\u003e \u003cp\u003eThe study population (n\u0026thinsp;=\u0026thinsp;7,409) was randomly assigned into a development cohort (n\u0026thinsp;=\u0026thinsp;5,187) and a validation cohort (n\u0026thinsp;=\u0026thinsp;2,222) in a 7:3 ratio using the createDataPartition function from the caret package in R. To compare categorical variables, we employed the chi-square test and Pearson's chi-square test for the analysis between different groups. Continuous variables were expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SD) for normally distributed data or medians with interquartile ranges (IQR) for skewed distributions, with comparisons performed using Student\u0026rsquo;s t-test or the Mann-Whitney U test, respectively.\u003c/p\u003e \u003cp\u003eVariable Selection and Nomogram Construction\u003c/p\u003e \u003cp\u003eThe prognostic nomogram was developed through a structured multi-step approach. Initially, univariate and multivariate Cox regression analyses were performed to identify features significantly associated with OS. To ensure the parsimony and stability of the model, three advanced variable selection methods\u0026mdash;Adiusted-R\u003csup\u003e2\u003c/sup\u003e, BIC, and LASSO regression\u0026mdash;were employed. For LASSO, the optimal tuning parameter λ was determined via ten-fold cross-validation. Variables consistently identified by these methods were incorporated into the final multivariable model to construct the nomogram.\u003c/p\u003e \u003cp\u003eModel Evaluation and Validation\u003c/p\u003e \u003cp\u003eThe predictive performance of the nomogram was rigorously evaluated in both the the model-developmen and validation cohorts. Discrimination was assessed using Harrell\u0026rsquo;s concordance index (C-index) and time-dependent receiver operating characteristic (ROC) curves with area under the curve (AUC) values. Calibration was evaluated via calibration plots to compare predicted versus observed survival probabilities. Furthermore, decision curve analysis (DCA) was performed to quantify the clinical net benefit and utility of the model.\u003c/p\u003e \u003cp\u003eData analysis for this research was performed using the R statistical software package (\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). Statistical significance was assessed using two-tailed tests, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Clinical Characteristics\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study cohort comprised 7,409 patients diagnosed with CCC between 2004 and 2019, categorized by primary tumor site: ovary (OCCC, 59.6%), corpus uteri (UCCC, 34.6%), and cervix (CCAC, 5.7%). Age-stratified analysis indicated that the incidence of UCCC was significantly higher among elderly women (\u0026gt;\u0026thinsp;60 years), whereas OCCC was more prevalent in younger patients (\u0026lt;\u0026thinsp;60 years) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eDistinct clinical outcomes and pathological features were observed across the three sites. The survival rate was highest for OCCC (65.0%), followed by CCAC (53.9%) and UCCC (52.0%). Regarding lymphatic spread, UCCC exhibited the highest rate of regional lymph node involvement (18.2%), compared to 13.7% for CCAC and 10.9% for OCCC. Furthermore, CCAC demonstrated the highest frequency of distant metastasis (5.9%), followed by UCCC (4.0%) and OCCC (2.7%). Specifically, lung and brain metastasis rates in the CCAC group were 4.0% and 0.9%, respectively, which were significantly higher than those in the UCCC and OCCC groups. While significant differences were observed in tumor grade, stage, size, treatment modalities, CSS, and OS across the three groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), no significant disparities were found regarding race or liver metastasis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of demographic and clinical features among different primary sites of clear cell adenocarcinoma.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eWhole populationn n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCervix\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCorpus Uteri\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOvary\u003c/p\u003e \u003cp\u003en (%)\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 \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4419\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\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5507 (74.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e327 (77.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1877 (73.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3303 (74.7)\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\u003eothers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1902 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e690 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1116 (25.3)\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\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3640 (49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225 (53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e551 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2864 (64.8)\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\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3769 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198 (46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2016 (78.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1555 (35.2)\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\u003cb\u003eStage\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=\"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\u003eI/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4991 (67.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e256 (60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1613 (62.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3122 (70.6)\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e569 ( 7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e182 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e346 (7.8)\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\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1184 (16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e450 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e655 (14.8)\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\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e665 ( 9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e322 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e296 (6.7)\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\u003cb\u003eGrade\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=\"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\u003eI-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e479 ( 6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e313 (7.1)\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\u003eIII-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6930 (93.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e380 (89.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2444 (95.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4106 (92.9)\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\u003cb\u003eTumor Size\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=\"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\u0026lt;\u0026thinsp;2 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e353 ( 4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e136 (3.1)\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\u003e2\u0026ndash;4 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e660 ( 8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e391 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e203 (4.6)\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\u0026gt;\u0026thinsp;4 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3000 (40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e540 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2351 (53.2)\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\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3396 (45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e199 (47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1468 (57.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1729 (39.1)\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\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=\"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\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e630 ( 8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164 (38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e325 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e141 (3.2)\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\u003e6779 (91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e259 (61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2242 (87.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4278 (96.8)\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\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=\"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\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6029 (81.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159 (37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1528 (59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4342 (98.3)\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\u003e1380 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e264 (62.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1039 (40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77 (1.7)\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\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=\"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\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2537 (34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200 (47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1333 (51.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1004 (22.7)\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\u003e4872 (65.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223 (52.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1234 (48.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3415 (77.3)\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\u003cb\u003eRagional lymph nodes\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=\"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\u003eNegative/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6405 (86.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e365 (86.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2101 (81.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3939 (89.1)\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\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1004 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e466 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e480 (10.9)\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\u003cb\u003eBone\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.044\u003c/p\u003e \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\u003e7355 (99.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e418 (98.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2542 (99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4395 (99.5)\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\u003e54 ( 0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (0.5)\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\u003cb\u003eBrain\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \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\u003e7399 (99.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e419 (99.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2564 (99.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4416 (99.9)\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\u003e10 ( 0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (0.1)\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\u003cb\u003eLiver\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.378\u003c/p\u003e \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\u003e7289 (98.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e417 (98.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2532 (98.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4340 (98.2)\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\u003e120 ( 1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79 (1.8)\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\u003cb\u003ePulmonary\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=\"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\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7278 (98.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e406 (96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2506 (97.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4366 (98.8)\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\u003e131 ( 1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53 (1.2)\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\u003cb\u003eDistant metastasis\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=\"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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7160 (96.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e398 (94.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2464 (96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4298 (97.3)\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\u003e249 ( 3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121 (2.7)\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\u003cb\u003eCss\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=\"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\u003eAlive or dead of other cause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5104 (68.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e277 (65.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1699 (66.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3128 (70.8)\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\u003eDead (attributable to this cancer)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2305 (31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146 (34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e868 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1291 (29.2)\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\u003cb\u003eSurvival months, Median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.0\u003c/p\u003e \u003cp\u003e(14.0, 89.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.0\u003c/p\u003e \u003cp\u003e(12.0, 85.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003cp\u003e(11.0, 76.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.0\u003c/p\u003e \u003cp\u003e(17.0, 96.0)\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\u003eVital 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=\"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\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4437 (59.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e228 (53.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1336 (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2873 (65)\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\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2972 (40.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195 (46.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1231 (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1546 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: Css: Cancer-Specific Survival; IQR: Interquartile Range.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the Development and Validation Cohorts\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe study population was randomly assigned to a development cohort (n\u0026thinsp;=\u0026thinsp;5,187) and an internal validation cohort (n\u0026thinsp;=\u0026thinsp;2,222), with detailed characteristics provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As survival time followed a skewed distribution, data are presented as medians with IQR. The median survival for the development and validation cohorts was 62.0 (23.0, 116.0) months and 20.0 (9.0, 41.0) month, respectively. No significant differences were observed between the two cohorts in terms of race, age, stage, CCC group, tumor size, grade, ragional lymph node status, surgery, radiotherapy, or diatant metastasis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that the cohorts were well-balanced and comparable, thereby ensuring the stability and feasibility of the subsequent prognostic model.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of patients with clear cell carcinoma in the model-development and validation cohorts.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eWhole population\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel-development\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValidation cohort\u003c/p\u003e \u003cp\u003en (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7409\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5187\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2222\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5507 (74.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3879 (74.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1628 (73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eothers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1902 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1308 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e594 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\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\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3640 (49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2531 (48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1109 (49.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3769 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2656 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1113 (50.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4991 (67.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3493 (67.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1498 (67.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e569 ( 7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e401 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1184 (16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e837 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e347 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e665 ( 9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e456 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e209 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003egroup\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\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e423 ( 5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e306 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorpus Uteri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2567 (34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1807 (34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e760 (34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4419 (59.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3074 (59.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1345 (60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrade\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\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e479 ( 6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e325 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e154 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6930 (93.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4862 (93.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2068 (93.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor Size\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\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e353 ( 4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e252 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;4 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e660 ( 8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e466 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e194 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3000 (40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2097 (40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e903 (40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3396 (45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2372 (45.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1024 (46.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRagional lymph node\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\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6405 (86.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4489 (86.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1916 (86.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1004 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e698 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e306 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistant metastasis\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\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7160 (96.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5012 (96.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2148 (96.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e249 ( 3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\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\u003e0.86\u003c/p\u003e \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\u003e630 ( 8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e443 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6779 (91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4744 (91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2035 (91.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" 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\u003e0.596\u003c/p\u003e \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\u003e6029 (81.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4229 (81.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1800 (81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1380 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e958 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e422 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\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\u003e0.909\u003c/p\u003e \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\u003e2537 (34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1774 (34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e763 (34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4872 (65.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3413 (65.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1459 (65.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCss\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\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlive or dead of other cause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5104 (68.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3557 (68.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1547 (69.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDead (attributable to this cancer)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2305 (31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1630 (31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e675 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurvival months, Median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.0 (14.0, 89.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0 (9.0, 41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.0 (23.0, 116.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eVital 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\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4437 (59.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3083 (59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1354 (60.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2972 (40.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2104 (40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e868 (39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: Css: Cancer-Specific Survival; IQR: Interquartile Range.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUnivariate and Multivariate Analysis for Clear-cell Carcinoma\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eUnivariate Cox regression analysis demonstrated that group, age, stage, grade, tumor size, primary site surgery, lymph node status, chemotherapy, and diatant metastasis were significantly associated with OS. Subsequently, these variables were incorporated into a multivariate Cox regression analysis. The rusults identified age, stage, grade, tumor size, primary site surgery, radiotherapy, chemotherapy, lymph node status, and diatant metastasis as independent prognostic factors for OS. Detailed results of both univariate and multivariate analyses are presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eVariable Selection\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo ensure the robust selection of prognostic predictors, three complementary statistical approaches\u0026mdash;Adjusted-R\u003csup\u003e2\u003c/sup\u003e, BIC and LASSO regression\u0026mdash;were employed. Adjusted-R\u003csup\u003e2\u003c/sup\u003e method identified an optimal subset of seven variables (age, tumor size, stage, surgery, radiotherapy, chemotherapy, and distant metastasis), achieving a maximum value of 0.260 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B). Conversely, BIC selected a more parsimonious model comprising six variables (age, tumor size, stage, surgery, chemotherapy, and distant metastasis) with a minimum BIC value of -2161.6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D). All variables selected across these methods demonstrated statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Finally, LASSO regression, utilizing an optimal tuning parameter (λ\u0026thinsp;=\u0026thinsp;0.015) determined via cross-validation, identified six core predictors: age, stage, surgery, radiotherapy, chemotherapy, and distant metastasis. The coefficient profile plot against the sequence of ln (λ) is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of Model\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIntegrating the findings from the univariate and multivariate Cox regression analyses, alongside the variable selection methods (Adjusted R\u003csup\u003e2\u003c/sup\u003e, BIC, and LASSO regression), eight key predictors were ultimately incorporated into the final model: group, age, tumor size, stage, surgery, chemotherapy, lymph node status, and distant metastasis. Based on these variables, a prognostic nomogram was constructed to evaluate its clinical utility (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eModel Performance and Validation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe prognostic model exhibited robust predictive performance and excellent calibration (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the development cohort, the model achieved an AUC of 0.795 (95% CI: 0.777\u0026ndash;0.813) for 12-month survival and 0.779 (95% CI: 0.763\u0026ndash;0.794) for 24-month survival, demonstrating favorable discrimination. The Hosmer-Lemeshow goodness-of-fit test revealed R\u0026sup2; values of 0.102 for the 12-month model and 0.149 for the 24-month model. In the internal validation cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the 12-month and 24-month AUC values were 0.814 (95% CI: 0.788\u0026ndash;0.840) and 0.798 (95% CI: 0.775\u0026ndash;0.822), respectively. The corresponding Brier scores were 0.098 and 0.142, reflecting low prediction error and high accuracy. Furthermore, Harrell\u0026rsquo;s C-index consistently exceeded 0.7 in both cohorts. The calibration curves for both time points closely aligned with the 45-degree reference line, further confirming the high concordance between the predicted probabilities and actual observed outcomes.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eClinical Utility\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePatients were stratified into high- and low-risk groups based on the integrated risk assessment tool. The DCA was performed to evaluate the clinical utility of the nomogram (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The DCA curves demonstrated that both the 12-month and 24-month models provided a significant net benefit over the \"All-treatment\" and \"No-treatment\" strategies across a wide range of threshold probabilities. These findings indicate that the nomogram is a reliable and effective tool for guiding clinical decision-making.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eSurvival Analysis and Risk Stratification\u003c/p\u003e \u003cp\u003eTo evaluate the clinical utility of the nomogram, patients were stratified into three risk tiers (low, medium, and high) based on the predicted 12-month and 24-month survival probabilities, using cut-off points of 0.4 and 0.7. Kaplan-Meier survival curves demonstrated significant divergence among these groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). For patients stratified by the 12-month and 24-month models, the median overall survival (mOS) in the low-risk group was approximately 130 months and 145 months, respectively\u0026mdash;significantly longer than that observed in the medium- and high-risk groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding anatomical subtypes, patients with OCCC exhibited a markedly superior survival profile compared to those with CCAC and UCCC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Furthermore, the absence of distant organ metastasis was associated with a dramatic improvement in mOS compared to the presence of metastasis (130 months vs. 15 months, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eSubgroup analyses further identified that younger age, earlier stage, smaller tumor size, and negative regional lymph nodes were significantly correlated with prolonged OS (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE-H). In terms of therapeutic interventions, both surgical resection and chemotherapy were found to significantly enhance survival outcomes (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI-J). In contrast, radiotherapy did not exert a statistically significant impact on patient prognosis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.68, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eK). These findings emphasize that early diagnosis and appropriate multimodal treatment remain the cornerstones of improved survival for patients with CCC.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large-scale population-based study leveraging the SEER database, we developed and validated a robust prognostic nomogram for female reproductive tract CCC. Our model exhibited superior predictive accuracy and clinical utility, effectively stratifying patients into distinct risk cohorts with a profound survival disparity (median OS: \u0026gt;130 months vs. \u0026lt;20 months). A key finding of our analysis is the prognostic heterogeneity among anatomical subtypes, where OCCC demonstrated a significantly more favorable survival profile than UCCC and CCAC\u0026mdash;a divergence likely driven by the higher prevalence of early-stage diagnosis in ovarian cases. Furthermore, we identified advanced age, tumor size, and distant metastasis as critical determinants of poor prognosis, while highlighting the pivotal role of surgical intervention combined with chemotherapy in improving overall survival. These findings provide a reliable tool for personalized risk assessment and clinical decision-making.\u003c/p\u003e \u003cp\u003eCCC a relatively rare histological subtype characterized by its aggressive clinical course and a generally unfavorable prognosis. Within the female reproductive tract, CCC predominantly manifests in the ovary (OCCC, 59.64%), followed by the corpus uteri (UCCC, 34.65%) and the cervix (CCAC, 5.71%). Our findings regarding the superior prognosis of OCCC compared to UCCC and CCAC are consistent with the results reported by Rauh-Hain et al.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], who observed that OCCC patients achieved a significantly longer median progression-free survival (PFS, 145 months) and overall survival (OS, 155.8 months) than UCCC patients (PFS: 31.4 months; OS: 39.5 months; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Current National Comprehensive Cancer Network (NCCN) guidelines advocate for comprehensive staging surgery as the gold standard for early-stage CCC [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Given that approximately 50% of OCCC and 44% of UCCC cases are diagnosed at Stage I, surgical intervention remains pivotal in optimizing clinical outcomes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Similarly, CCAC derives substantial benefit from early-stage resection, with reported 3-year survival rates reaching 91% for Stage I\u0026ndash;IIA disease [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, compared to high-grade serous or endometrioid carcinomas, CCC exhibits a higher propensity for extrauterine metastasis and advanced-stage presentation. At the time of diagnosis, nearly 41.6% of OCCC and 50.0% of UCCC cases have already progressed to Stage III or IV. Nonetheless, achieving R0 cytoreduction (complete tumor debulking) provides a critical survival advantage for these advanced-stage patients [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A major therapeutic challenge remains the intrinsic chemoresistance of CCC; the overall response rate (ORR) to first-line platinum-based chemotherapy in advanced OCCC is markedly lower than in serous epithelial ovarian cancer (sEOC) (45% vs. 81%)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], A similar pattern of decreased chemo-sensitivity is observed in UCCC relative to endometrioid carcinoma, further underscoring the recalcitrant nature and distinct biological profile of clear cell malignancies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eStage, tumor grade, and optimal cytoreduction have been established as the primary prognostic determinants in OCCC. Despite its distinct clinical profile, the 5-year survival rate for early-stage OCCC remains inferior to other epithelial ovarian cancer (EOC) subtypes, ranging from 50% to 73% [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For UCCC and CCAC, survival is significantly influenced by stage, depth of myometrial invasion, lymphovascular space invasion (LVSI), and the implementation of adjuvant therapies, including chemotherapy and radiotherapy [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In UCCC specifically, advanced age, tumor size, distant metastasis, and molecular markers such as the Ki-67 index and P53 expression serve as critical indicators for prognostic evaluation [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. CCAC, a rare non-HPV-related cervical malignancy, [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], exhibits a 5-year overall survival (OS) rate between 40% and 78%, which precipitously declines to below 50% for Stage II and beyond [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These established prognostic factors and survival outcomes are highly consistent with the findings of our present study. Regardless of the primary site, CCC of the female reproductive tract demonstrates a marked propensity for lymphatic dissemination and distant metastasis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Our analysis further elucidates the metastatic landscape of CCC, identifying the lungs and liver as the most frequent sites of visceral involvement, followed by the bones and brain. Consequently, comprehensive surgical staging and aggressive cytoreduction remain the cornerstones of management to optimize the prognosis of patients with CCC.\u003c/p\u003e \u003cp\u003eThe heterogeneity in adjuvant treatment regimens has led to divergent clinical outcomes across OCCC, UCCC, and CCAC. For OCCC, Masashi Takano et al. suggested that early-stage patients, particularly those with Stage IA disease, may not require adjuvant therapy given their relatively favorable prognosis, as chemotherapy did not significantly extend progression-free survival (PFS) in this subgroup [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Nonetheless, the optimal therapeutic strategy for OCCC remains a subject of ongoing debate. Research by Chih-Ming Ho emphasized that comprehensive surgical staging\u0026mdash;specifically pelvic and para-aortic lymphadenectomy\u0026mdash;combined with paclitaxel and carboplatin (TC), significantly improves survival for Stage I OCCC [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. UCCC typically necessitates a more aggressive adjuvant approach. According to NCCN guidelines, observation, chemotherapy, or radiotherapy (RT) may be considered for Stage IA UCCC without myometrial invasion. However, chemotherapy with or without RT is the standard recommendation for Stage IA with myometrial invasion and Stages IB\u0026ndash;IV, which are consistently classified as high-risk subtypes [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The GOG177 trial demonstrated that the combination of paclitaxel, doxorubicin, and cisplatin (TAP) improved overall response rates, PFS, and OS in recurrent endometrial cancer, including UCCC [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Subsequently, the GOG209 trial established the non-inferiority of TC compared to TAP, with a superior toxicity profile, positioning TC as the preferred first-line regimen [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Furthermore, RT has been shown to significantly enhance PFS in UCCC (67% vs. 36%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) and reduce regional recurrences. While vaginal brachytherapy may suffice for Stages I\u0026ndash;II, patients with nodal involvement or advanced disease appear to derive greater benefit from whole-pelvic radiation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In contrast, the role of adjuvant therapy for CCAC remains controversial. Some studies indicate that chemotherapy or concurrent chemoradiotherapy (CCRT) yields minimal prognostic benefit, particularly for early-stage disease without pathological risk factors [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], Conversely, other reports suggest a potential survival advantage from adjuvant interventions [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This discrepancy is largely attributable to small sample sizes and inconsistent staging across studies. Overall, while chemotherapy shows potential in improving CCC outcomes, the efficacy of RT appears limited and highly dependent on tumor location. Our findings align with this, showing a restricted overall benefit from radiotherapy. This underscores the critical value of our prognostic risk stratification tool in refining clinical decision-making and identifying patients who may truly benefit from intensified therapy.\u003c/p\u003e \u003cp\u003eEmerging evidence suggests that OCCC may originate from atypical endometriosis, a hypothesis mirrored in the proposed origin of CCAC from cervical endometriosis. Loss of heterozygosity (LOH) analyses have identified clonal relationships between endometriotic lesions and adjacent malignancies, suggesting a potential pathological homology among CCCs of the female reproductive tract. Specifically, somatic mutations in ARID1A and PTEN within endometriotic foci appear to be early driver events in the pathogenesis of OCCC [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Furthermore, preliminary metabolic transcriptomic clustering indicates that the organ of origin (renal vs. non-renal) does not fundamentally differentiate the core biological features of CCC. Among non-renal CCCs, TP53, ARID1A, and PIK3CA emerge as the most frequently mutated genes. Notably, of the 50 hallmark gene sets analyzed, only two exhibited significant differential expression across different gynecological CCC sites. This molecular congruence suggests that targeting the PI3K-AKT-mTOR, DNA damage repair (DDR), and MYC pathways could be an effective therapeutic strategy regardless of the primary tumor site. Recent clinical investigations have explored the potential of immunotherapy in CCC, particularly given the observed high expression of PD-L1 in various gynecological locations [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. While single-agent immune checkpoint inhibitors (ICIs) have yet to demonstrate superior efficacy over conventional chemotherapy [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], insights from renal cell carcinoma suggest that CCC may benefit more from dual immunotherapy or combinations of ICIs with targeted agents [\u003cspan additionalcitationids=\"CR41 CR42\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Innovative approaches, including therapeutic vaccines and targeting myeloid checkpoints\u0026mdash;such as the CD47 \"don't eat me\" signal\u0026mdash;are currently under active investigation [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Given that at least 20% of OCCC cases exhibit MSI-high status, mismatch repair (MMR) deficiency, or a high tumor mutational burden (TMB-H) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], our findings further highlight the necessity of integrating clinical nomograms with novel biomarkers to guide individualized treatment strategies.\u003c/p\u003e \u003cp\u003eDespite providing valuable prognostic insights, our study has several limitations that warrant consideration. First, its retrospective nature may introduce inherent selection and information biases. Second, while the SEER database provides a large-scale cohort, the findings may be influenced by geographic and ethnic variations, and our current analysis lacks a granular, stratified evaluation of clinical staging and treatment modalities across specific anatomical sites. Third, the current model primarily relies on clinicopathological parameters; therefore, the integration of molecular biomarkers and multi-omics data in future studies will be essential to enhance predictive precision and clinical applicability. Finally, although internal validation yielded promising results, prospective multicenter studies are required to further validate the model\u0026rsquo;s performance and ensure its generalizability across diverse clinical settings and populations.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our established nomogram underscores the necessity for intensified management and tailored therapeutic intervention for patients with intermediate-and high-risk female reproductive tract CCC. High-risk features\u0026mdash;including advanced stage, high tumor grade, age over 60 years, tumor size exceeding 4 cm, distant metastasis, and lymph node involvement\u0026mdash;remain robust indicators of a dismal prognosis. Notably, patients who undergo suboptimal cytoreduction (non-R0 resection) or exhibit chemoresistance face significantly worse survival outcomes. These findings emphasize the urgent need for meticulous clinical surveillance and the active exploration of individualized treatment paradigms. Future efforts should integrate genomic profiling and immune biomarker detection to refine risk stratification and improve long-term outcomes through precision oncology.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClear cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOCCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOvarian CCC\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUCCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUteri CCC\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCervical CCC\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEOC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEpithelial ovarian cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBayesian information criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSEER\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSurveillance, Epidemiology, and End Results\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIRB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstitutional Review Board\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Cancer Institute\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCancer-specific survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOverall survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviations\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile ranges\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDecision curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProgression-free survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCCN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Comprehensive Cancer Network\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRadiation therapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLOH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLoss of heterozygosity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMSI-high status, mismatch repair\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eC-index\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConcordance index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the receiver operating characteristic curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eEthical consent was waived due to the SEER database contains anonymous patient information. All data from the SEER database were open access (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://seer.cancer.gov/data-software/\u003c/span\u003e\u003cspan address=\"https://seer.cancer.gov/data-software/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAbbreviations\u003c/h2\u003e \u003cp\u003eBSR, best subsets regression; FSR, forward stepwise regression; BIC, Bayesian information criterion; LASSO, least absolute shrinkage and selection operator.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by Fujian Provincial Natural Science Foundation of China (Grant number:2024J011054) and Joint Funds for theTechnology Innovation of Science and Fujian Province (Grant number 2024Y9586).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSuyu Li, Jimiao Huang and Xiaoying Chen, Diling Pan conceived the study and designed the experiments. Hang Lin, Leilei Zhu and Guangrun Zhou carried out data collection and analysis. Suyu Li, Jimiao Huang and Liyuan Huang assisted in drafting the manuscript, and Xiaoying Chen revised the manuscript. All the authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors are grateful for the support provided by Yusha Chen.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data from the SEER database were open access (https://seer.cancer.gov/data-software/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGadducci A, Cosio S, Spirito N, Cionini L. Clear cell carcinoma of the endometrium: a biological and clinical enigma. Anticancer Res. 2010;30(4):1327\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu Z. 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Abstract A27: Microsatellite instability and tumor mutation burden as factors in ovarian clear-cell carcinoma therapy selection CLINICAL. 2020; 26 (13_Supple): A27\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1557-3265.ovca19-a27\u003c/span\u003e\u003cspan address=\"10.1158/1557-3265.ovca19-a27\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"clear cell carcinoma (CCC), prognostic prediction model, nomogram, SEER database, gynecologic malignancies","lastPublishedDoi":"10.21203/rs.3.rs-9317653/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9317653/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eClear cell carcinoma (CCC) of the female reproductive tract is a rare and aggressive malignancy that lacks an established prognostic prediction model. This study aimed to develop and validate a robust nomogram to predict survival outcomes and facilitate personalized risk stratification for CCC patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 7,409 patients confirmed to have CCC between 2004 and 2019 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. They were further divided into a model-development cohort (n\u0026thinsp;=\u0026thinsp;5,187) and a validation cohort (n\u0026thinsp;=\u0026thinsp;2,222) in a 7:3 ratio. Independent prognostic factors were identified via multivariable regression analysis to construct a predictive nomogram. The discrimination and calibration of the nomogram were evaluated using the concordance index (C-index), the area under the receiver operating characteristic curve (AUC), and calibration plots. The clinical utility and net benefits of the nomogram at different threshold probabilities were quantified using decision curve analysis (DCA). Furthermore, risk stratifications were performed based on the nomogram-derived scores, and survival disparities across risk groups were compared using Kaplan-Meier curves and log-rank tests.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEight variables, including primary site, age, tumor size, stage, surgery, chemotherapy, diatant metastasis, and lymph node status were selected to establish the prognostic nomogram for CCC. The model demonstrated robust discriminative performance, with AUC values of 0.795 (12-month) and 0.779 (24-month) in the development cohort, and 0.814 and 0.798 in the validation cohort, respectively. Notably, Harrell\u0026rsquo;s C-index exceeded 0.7 in both cohorts, while calibration curves indicated high concordance between predicted and observed survival. Risk stratification effectively categorized patients into groups with distinct survival outcomes; the median survival was \u0026lt;\u0026thinsp;20 months for the high-risk group versus \u0026gt;\u0026thinsp;130 months for the low-risk group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Notably, ovarian CCC showed a higher incidence and superior prognosis compared to uterine and cervical subtypes. Advanced age, larger tumor size, and distant metastasis were significant predictors of poor outcomes.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eA prognostic nomogram was developed and validated to assist clinicians in evaluating the survival outcomes of patients with CCC of the female reproductive tract.\u003c/p\u003e","manuscriptTitle":"Nomogram Prediction Model for Clear Cell Carcinoma of the Female Reproductive Tract: A SEER Database Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 16:15:23","doi":"10.21203/rs.3.rs-9317653/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"279729023310587912087901920121819550937","date":"2026-05-19T00:47:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-06T16:11:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-09T04:23:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T23:32:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-06T23:32:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2026-04-04T05:20:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"27684017-fac9-4897-9c98-e7801ee1012c","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"279729023310587912087901920121819550937","date":"2026-05-19T00:47:09+00:00","index":59,"fulltext":""},{"type":"reviewersInvited","content":"30","date":"2026-05-06T16:11:57+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T16:15:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 16:15:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9317653","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9317653","identity":"rs-9317653","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}