Prognostic Nomograms and Risk Stratification for Rhabdomyosarcoma Across Anatomic Sites

Prognostic Nomograms and Risk Stratification for Rhabdomyosarcoma Across Anatomic Sites | 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 Prognostic Nomograms and Risk Stratification for Rhabdomyosarcoma Across Anatomic Sites Yuan Zheng, Di Zhang, Qihao Sun, Jiaoyang Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7780087/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Rhabdomyosarcoma (RMS) is a rare soft tissue sarcoma that predominantly affects children and adolescents. Current prognostic models are limited by their focus on single anatomic sites or small patient cohorts. This study aims to develop and validate two prognostic nomograms incorporating clinical, pathological, and treatment-related factors to predict overall survival (OS) and cancer-specific survival (CSS) in RMS patients across different anatomic locations. Methods Data from patients diagnosed with primary RMS between 2010 and 2018 were extracted. Prognostic nomograms were constructed based on independent risk factors, with model performance assessed via the concordance index (C-index), calibration curves, and decision curve analysis (DCA). Risk stratification was performed based on nomogram-derived scores. Results The study included 4,335 patients, randomly divided into training (n = 3,034) and validation (n = 1,301) cohorts. The median age was 19 years, with 53.9% male predominance. Common primary sites were the head/neck (29.3%), extremities (24.4%), and genitourinary tract (20.5%). Alveolar (32.8%) and embryonal (23.9%) subtypes were most frequent. Key independent prognostic factors included age, tumor size, histologic subtype, T stage, nodal status, metastasis, and treatment modalities (all P < 0.05). The OS nomogram achieved C-indices of 0.759 (training) and 0.785 (validation), while the CSS nomogram reached 0.790 and 0.774, outperforming conventional staging systems. Calibration curves and DCA confirmed strong predictive accuracy and clinical utility. Risk stratification effectively differentiated low-, intermediate-, and high-risk groups ( P < 0.001), with consistent validation results. Conclusion These comprehensive nomograms offer individualized OS and CSS predictions for RMS patients, serving as valuable tools for prognostic evaluation. Rhabdomyosarcoma soft tissue sarcoma overall survival cancer-specific survival prognostic model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in children, accounting for approximately 5% of all pediatric malignancies and a smaller proportion of adolescent and adult cancers 1, 2 . It originates from primitive mesenchymal cells with myogenic differentiation and can arise in various anatomical locations, including the head and neck, genitourinary tract, extremities, and trunk 3, 4 . Histologically, RMS is classified into embryonal, alveolar, and other rare subtypes, each demonstrating distinct biological behavior and differential responses to therapy 5–7 . Despite advances in multimodal treatment, survival outcomes remain highly variable 8, 9 . Reported five-year overall survival (OS) rates exceed 70% in patients with localized tumors, but fall below 30% in those with metastatic disease at diagnosis 10, 11 . This pronounced variability underscores the biological complexity of RMS and continues to challenge efforts toward personalized treatment planning and long-term disease control. Historically, risk-adapted management of RMS has relied on classification systems such as the Intergroup Rhabdomyosarcoma Study (IRS) clinical grouping and the Children’s Oncology Group staging criteria 12 . These frameworks incorporate key prognostic variables, including tumor size, anatomical site, nodal status, and presence of distant metastasis 13 . While these tools have played a central role in protocol development and trial design, their categorical structure inherently limits prognostic granularity. In particular, they do not account for histologic subtype-specific behavior, the therapeutic interventions received, or inter-individual variability in outcomes among patients with similar stage classifications. Consequently, these traditional systems offer only coarse risk stratification and fall short in delivering individualized prognostic estimates or supporting precision treatment decisions. To address these limitations, we aimed to construct a robust, clinicopathology-informed nomogram to predict OS and cancer-specific survival (CSS) in patients with RMS. Leveraging data from a large, representative cohort, our goal was to develop a practical and accessible prognostic tool to support personalized risk assessment and guide clinical decision-making across a broad spectrum of RMS. 2. Materials and Methods 2.1. Data Source and Patient Selection We retrospectively extracted data from the Surveillance, Epidemiology, and End Results (SEER) database. Patients diagnosed with primary RMS between January 1, 2010, and December 31, 2018, were identified based on ICD-O-3 histology codes: 8900/3 (rhabdomyosarcoma, NOS), 8910/3 (embryonal rhabdomyosarcoma), and 8920/3 (alveolar rhabdomyosarcoma). Inclusion criteria were as follows: (1) histologically confirmed diagnosis of RMS; (2) known survival status and follow-up time; and (3) available data for key clinical variables, including tumor characteristics and treatment details. Exclusion criteria were diagnosis based on autopsy or death certificate only and survival time equal to 0 months or an unknown survival status (Fig. 1 ) . This retrospective study was approved by the institutional review board, with informed consent waived, and was conducted using de-identified SEER data in compliance with the Declaration of Helsinki and relevant ethical regulations. 2.2. Variables and Endpoints A range of variables encompassing demographic, tumor, and treatment characteristics were collected and analyzed. Demographic factors included the year of diagnosis, age at diagnosis, sex, and race. Tumor characteristics included the primary anatomical site, tumor site subgroup, tumor size, histological subtype, and staging information. Treatment modalities included surgery, radiotherapy, and chemotherapy. 2.3. Statistical Analysis All statistical analyses were performed using R software. Categorical variables were summarized as frequencies and percentages. Survival was estimated by Kaplan–Meier with log-rank tests, and prognostic factors were identified by univariate and multivariate Cox regression. Nomograms for OS and CSS were constructed from the final multivariable models to predict 1-, 3-, and 5-year survival. Model performance was assessed by C-index, and calibration was evaluated using calibration plots comparing predicted versus observed outcomes. Internal validation was performed using 1,000 bootstrap resamples in the validation cohort. Time-dependent receiver operating characteristic (ROC) curves and area under the curve (AUC) values for survival outcomes were calculated using the time-ROC package to assess predictive accuracy over time. Clinical utility was examined through decision curve analysis (DCA), comparing the net benefit of the nomograms with the traditional staging. Patients were stratified into risk groups according to the nomogram scores utilizing X-tile software, and survival differences between the groups were compared. P < 0.05 was considered statistically significant. 3. Results 3.1. Patient Characteristics The entire cohort was randomly divided into a training cohort (n = 3,034) and a validation cohort (n = 1,301). The median age at diagnosis was 19 years, and 53.9% of patients were male. The most common primary tumor site was the head and neck region (29.3%), followed by the extremities and trunk (24.4%) and the genitourinary tract (20.5%). Based on histological subtype, alveolar RMS accounted for 32.8% of cases, embryonal RMS for 23.9%, and other rare subtypes for 31.4%. Tumor size was < 6.6 cm in 32.6% of patients and ≥ 6.6 cm in 32.3%. Among patients with known T stage, the majority (39.4%) had T1–T2 disease. Additionally, 12.5% of patients had regional lymph node metastasis, and 17.5% presented with distant metastasis at diagnosis. Surgical resection was performed in 55.8% of cases, radiotherapy was administered in 32.4%, and 76.8% of patients received chemotherapy. Baseline demographic and clinicopathological characteristics of the training and validation cohorts are summarized in Table 1 ." Table 1 Baseline characteristics of the overall study population. Characteristics Overall cohort Training cohort Validation cohort P n = 4335 n = 3034 n = 1301 Year of diagnosis 0.255 2000–2005 1055 (24.3) 734 (24.2) 321 (24.7) 2006–2010 971 (22.4) 694 (22.9) 277 (21.3) 2011–2015 1008 (23.3) 683 (22.5) 325 (25.0) 2016–2021 1301 (30.0) 923 (30.4) 378 (29.1) Age 0.815 ≤ 18 2107 (48.6) 1465 (48.3) 642 (49.3) 19–60 1282 (29.6) 903 (29.8) 379 (29.1) > 60 946 (21.8) 666 (22.0) 280 (21.5) Race 0.254 White 3245 (74.9) 2277 (75.0) 968 (74.4) Black 683 (15.8) 486 (16.0) 197 (15.1) Other 407 (9.4) 271 (8.9) 136 (10.5) Sex 0.367 Male 1038 (23.9) 730 (24.1) 308 (23.7) Female 3297 (76.1) 2304 (75.9) 993 (76.3) Marital status 0.815 Married 1038 (23.9) 730 (24.1) 308 (23.7) Others 3297 (76.1) 2304 (75.9) 993 (76.3) Histologic subtype 0.579 Embryonal 1037 (23.9) 713 (23.5) 324 (24.9) Alveolar 1422 (32.8) 1008 (33.2) 414 (31.8) Pleomorphic 514 (11.9) 367 (12.1) 147 (11.3) Others 1362 (31.4) 946 (31.2) 416 (32.0) Primary anatomical site 0.147 Head and neck 1271 (29.3) 910 (30.0) 361 (27.7) Thoracic region 108 (2.5) 84 (2.8) 24 (1.8) Abdominal region 247 (5.7) 162 (5.3) 85 (6.5) Peritoneum/retroperitoneum 584 (13.5) 416 (13.7) 168 (12.9) Genitourinary tract 889 (20.5) 615 (20.3) 274 (21.1) Extremities and trunk 1056 (24.4) 728 (24.0) 328 (25.2) Others 180 (4.2) 119 (3.9) 61 (4.7) Tumor site subgroup 0.411 Favorable 1528 (35.2) 1060 (34.9) 468 (36.0) Unfavorable 2773 (64.0) 1947 (64.2) 826 (63.5) NA 34 (0.8) 27 (0.9) 7 (0.5) Grade 0.640 I-II 52 (1.2) 39 (1.3) 13 (1.0) III-IV 173 (4.0) 124 (4.1) 49 (3.8) NA 4110 (94.8) 2871 (94.6) 1239 (95.2) SEER stage 0.516 Localized 1307 (30.1) 912 (30.1) 395 (30.4) Regional 1302 (30.0) 928 (30.6) 374 (28.7) Distant 1481 (34.2) 1019 (33.6) 462 (35.5) NA 245 (5.7) 175 (5.8) 70 (5.4) TNM stage 0.522 I 381 (8.8) 258 (8.5) 123 (9.5) II 117 (2.7) 84 (2.8) 33 (2.5) III 391 (9.0) 269 (8.9) 122 (9.4) IV 816 (18.8) 559 (18.4) 257 (19.8) NA 2630 (60.7) 1864 (61.4) 766 (58.9) T stage 0.508 T1-T2 1710 (39.4) 1181 (38.9) 529 (40.7) T3-T4 200 (4.6) 144 (4.7) 56 (4.3) NA 2425 (55.9) 1709 (56.3) 716 (55.0) Nodal involvement 0.696 No regional LN 1591 (36.7) 1104 (36.4) 487 (37.4) Regional LN 542 (12.5) 376 (12.4) 166 (12.8) NA 2202 (50.8) 1554 (51.2) 648 (49.8) Metastasis at diagnosis 0.138 No distant metastasis 1550 (35.8) 1099 (36.2) 451 (34.7) Distant metastasis 758 (17.5) 508 (16.7) 250 (19.2) NA 2027 (46.8) 1427 (47.0) 600 (46.1) Tumor size (cm) 0.175 < 6.6 1414 (32.6) 964 (31.8) 450 (34.6) ≥ 6.6 1402 (32.3) 988 (32.6) 414 (31.8) NA 1519 (35.0) 1082 (35.7) 437 (33.6) Primary site surgery 0.604 No 1915 (44.2) 1332 (43.9) 583 (44.8) Yes 2420 (55.8) 1702 (56.1) 718 (55.2) Radiotherapy 0.401 No 2931 (67.6) 2039 (67.2) 892 (68.6) Yes 1404 (32.4) 995 (32.8) 409 (31.4) Chemotherapy 0.765 No 1006 (23.2) 689 (22.7) 317 (24.4) Yes 3329 (76.8) 2345 (77.3) 984 (75.6) 3.2 Survival Outcomes and Subgroup Analysis The median OS for the entire cohort was 39 months, and the median CSS was 75 months. The 1-, 3-, and 5-year OS rates were 73.31%, 51.12%, and 46.15%, respectively, while the corresponding CSS rates were 76.92%, 55.81%, and 51.34% ( Supplementary Fig. 1 ). Kaplan–Meier survival analysis revealed significant differences in both OS and CSS across multiple clinicopathological subgroups (Fig. 2 , log-rank P 60 years (Fig. 2 A, 2 J). Primary anatomical site also significantly influenced survival outcomes. Patients with tumors located in the head and neck region had the most favorable prognosis, whereas those with thoracic tumors exhibited markedly poorer outcomes (Fig. 2 B, 2 K). To further evaluate the impact of anatomical tumor sites, we stratified tumor locations into favorable, unfavorable, and NA categories based on previous literature and organ-specific prognostic characteristics. This classification revealed substantial survival heterogeneity across these subgroups, with favorable-site tumors associated with significantly improved OS and CSS compared to unfavorable-site tumors (Fig. 2 C, 2 L). Additional stratified analyses revealed worse survival outcomes with higher T-stage (T3–T4 vs. T1–T2; Fig. 2 D, 2 M), as well as across different histologic subtypes, with embryonal subtype showing relatively better prognosis than alveolar, pleomorphic, or other subtypes (Fig. 2 E, 2 N). Similarly, patients with localized disease exhibited significantly longer survival compared to those with regional or distant-stage disease (Fig. 2 F, 2 O). Moreover, receipt of surgery, radiotherapy, and chemotherapy were all associated with significant survival benefits (Fig. 2 G–I, 2 P-R). 3.3 Prognostic Factor Analysis Univariate Cox regression analysis identified several variables significantly associated with OS, including age at diagnosis, tumor size, histological subtype, T stage, nodal involvement, metastasis at diagnosis, surgery, radiotherapy, and chemotherapy (all P < 0.05). Similar results were observed for CSS, as detailed in Supplementary Table 1 . In the multivariate Cox regression analysis for OS, several factors remained independently associated with prognosis. Age > 60 years was a strong predictor of poor OS (HR = 4.66, 95% CI: 3.96–5.50, P < 0.001), followed by age 19–60 years (HR = 2.28, 95% CI: 2.00–2.60, P < 0.001), compared with those aged ≤ 18 years. Female sex was associated with improved survival (HR = 0.789, 95% CI: 0.640–0.972, P = 0.026). Patients with alveolar histology had worse outcomes compared to embryonal subtype (HR = 0.60, 95% CI: 0.51–0.70, P < 0.001). Tumors located in thoracic, abdominal, retroperitoneal, genitourinary, and extremity/trunk regions were associated with significantly worse OS relative to those in the head and neck. Specifically, thoracic tumors had an HR of 2.12 (95% CI: 1.62–2.77, P < 0.001), and retroperitoneal tumors had an HR of 1.87 (95% CI: 1.57–2.22, P < 0.001). Patients with distant-stage disease had significantly increased risk of mortality (HR = 3.89, 95% CI: 3.33–4.55, P < 0.001). Notably, the absence of surgical treatment was strongly associated with inferior OS (HR = 0.61, 95% CI: 0.53–0.69, P < 0.001), as was lack of radiotherapy (HR = 0.76, 95% CI: 0.67–0.87, P < 0.001) and chemotherapy (HR = 0.63, 95% CI: 0.55–0.71, P 60 years: HR = 3.98, 95% CI: 3.32–4.77; both P < 0.001) and alveolar histology (HR = 0.60, 95% CI: 0.51–0.72, P < 0.001) were independent predictors of poor CSS. Tumors located in thoracic (HR = 2.28, 95% CI: 1.67–3.10), abdominal (HR = 2.00, 95% CI: 1.53–2.61), and retroperitoneal regions (HR = 1.89, 95% CI: 1.53–2.35) also had significantly worse CSS (all P < 0.001). Patients with distant metastases had a markedly increased risk of cancer-specific death (HR = 4.50, 95% CI: 3.76–5.38, P < 0.001). The lack of surgery (HR = 0.60, 95% CI: 0.51–0.69, P < 0.001), radiotherapy (HR = 0.77, 95% CI: 0.67–0.89, P < 0.001), or chemotherapy (HR = 0.76, 95% CI: 0.66–0.89, P < 0.001) remained strong predictors of unfavorable CSS ( Supplementary Table 2) . 3.4 Construction and Evaluation of the Prognostic Nomogram Furthermore, two comprehensive prognostic tools were developed: one for OS and another for CSS, incorporating all significant independent predictors. These nomograms enable clinicians to estimate survival probabilities at 1-, 3-, and 5-year intervals through a visual scoring system, where each prognostic factor contributes points proportional to its relative importance in the model (Figs. 3 A and 3 B). The predictive performance of the nomograms was rigorously evaluated through multiple validation metrics. For OS prediction, the concordance indices reached 0.759 (training cohort) and 0.785 (validation cohort), representing a 15.7–30.2% improvement over conventional SEER staging (0.656–0.672) and a 30.4–84.7% enhancement compared to TNM staging (0.425–0.582). The CSS nomogram showed comparable discriminative ability, with C-indices of 0.790 and 0.774 in training and validation cohorts respectively, outperforming existing staging systems by 16.0-54.3%. Time-dependent ROC analysis further confirmed the superior predictive accuracy of our models. The OS nomogram achieved AUC values of 0.858, 0.842, and 0.859 for 1-, 3-, and 5-year predictions in the training set - significantly higher ( P < 0.001) than both TNM (0.590–0.609) and SEER (0.683–0.708) systems. This advantage persisted in the validation cohort (AUCs: 0.851, 0.843, 0.838). Similarly, the CSS nomogram maintained robust predictive performance across all timepoints (training AUCs: 0.817 − 0.787; validation AUCs: 0.759–0.836), consistently surpassing conventional staging methods (AUC differences: 0.101–0.258). Model calibration was excellent, with calibration curves demonstrating close alignment between predicted and observed survival outcomes in both cohorts (Fig. 4 ). DCA revealed that the nomograms provided superior net benefit across clinically relevant threshold probabilities, suggesting their practical utility for risk stratification and therapeutic decision-making ( Fig. 4 ). 3.5 Risk Stratification Patients were stratified into three risk groups—low, intermediate, and high—according to the total risk scores derived from the nomogram. For OS, the cutoff values were 92 and 134: patients with scores ≤ 92 were classified as low-risk, those with scores between 93 and 134 as intermediate-risk, and those with scores > 134 as high-risk. For CSS, the corresponding cutoff values were 98 and 146. Kaplan–Meier survival analysis demonstrated significant differences in survival outcomes among the three risk groups (log-rank P < 0.001; Fig. 5 ). In the training cohort, the 1-, 3-, and 5-year OS rates were 89.1%, 70.0%, and 65.2% for the low-risk group (n = 1713), 72.9%, 39.6%, and 31.2% for the intermediate-risk group (n = 628), and 35.1%, 16.2%, and 13.1% for the high-risk group (n = 693), respectively. A similar trend was observed for CSS, with 1-, 3-, and 5-year rates of 91.4%, 74.9%, and 71.4% in the low-risk group (n = 1552), 76.0%, 45.0%, and 37.6% in the intermediate-risk group (n = 912), and 36.9%, 17.5%, and 13.4% in the high-risk group (n = 570). The validation cohort consistently reproduced these prognostic patterns. Low-risk patients (n = 766 for OS; n = 680 for CSS) maintained 5-year survival rates of 62.5% OS and 67.9% CSS, while high-risk patients (n = 309 for OS; n = 256 for CSS) had 5-year OS rates of 10.3% and 5-year CSS rates of 15.2%. The intermediate-risk group (n = 226 for OS; n = 365 for CSS) showed 5-year survival rates of 33.6% and 38.6%, respectively, confirming the stability of this three-tier classification system. 4. Discussion In this population-based study, we developed and validated nomograms for predicting survival outcomes in patients with RMS. These nomograms incorporated readily available clinical and pathological variables and demonstrated satisfactory discriminatory performance and clinical utility in both training and validation cohorts. To our knowledge, this is among the first large-scale population-level analyses that systematically evaluate both OS and CSS across an unselected RMS cohort, including all age groups, histological subtypes, and tumor locations. By simultaneously modeling OS and CSS, our study provides complementary insights into prognosis: OS reflects mortality from all causes, capturing the influence of comorbidities and non-cancer-related deaths, while CSS isolates mortality directly attributable to RMS. The divergence observed between OS and CSS in subgroups such as older patients or those with favorable tumor sites underscores the importance of considering both endpoints in clinical prognostication and follow-up planning. For instance, CSS may better indicate tumor aggressiveness and therapeutic urgency in high-risk patients, whereas OS may better capture survivorship issues in lower-risk or long-term survivors. Similar distinctions have been noted in prior sarcoma studies 14 . Our findings underscore the prognostic importance of several key factors—age at diagnosis, histological subtype, primary tumor site, disease stage, and treatment strategies—all of which align with established literature 15–17 . In particular, older age was significantly associated with poorer outcomes. Likewise, the alveolar subtype was independently linked to worse survival compared to the embryonal subtype, reflecting its more aggressive biological behavior 18 . Tumors originating in traditionally favorable sites, such as the head and neck (including orbit and non-para-meningeal regions), showed markedly better survival than those in unfavorable sites including thoracic region, corroborating earlier prognostic results 19 . Moreover, multimodal treatment incorporating radiotherapy and chemotherapy conferred survival benefits, particularly in patients with advanced local disease or incomplete resection, supporting current consensus guidelines 20 . Taken together, the findings emphasize the clinical relevance of integrating classical prognostic indicators with distinct survival endpoints and underscore the utility of a comprehensive predictive model that captures both disease-specific and all-cause survival risks in RMS. While traditional staging systems remain valuable for risk stratification and treatment decision-making, their ability to predict individualized outcomes is limited by their categorical nature and inability to incorporate multiple prognostic factors simultaneously 21, 22 . Our nomograms address these limitations by integrating multiple independent prognostic variables into a unified model that provides personalized survival estimates. The nomograms outperformed the TNM and SEER staging systems with higher concordance indices and superior predictive accuracy demonstrated by ROC and calibration analyses. Furthermore, DCA demonstrated that the nomogram yielded greater net clinical benefit across a range of threshold probabilities, underscoring its value in real-world decision-making contexts. Although the IRS clinical grouping system is a cornerstone of RMS risk stratification in cooperative group trials, it could not be included in our comparative analysis due to limitations of the SEER database, which lacks key variables such as surgical margin status, tumor invasiveness, and chemotherapy details. Nevertheless, several prognostic factors emphasized in IRS algorithms—such as age, histologic subtype, and site—are incorporated into our model, suggesting conceptual alignment despite methodological differences. Altogether, our nomogram complements existing staging frameworks by offering a refined, patient-specific risk estimation tool. Its strong performance metrics and broad applicability make it a valuable adjunct to traditional systems, particularly in contexts where detailed IRS data are unavailable or when individualized prognosis is needed to guide therapy or trial enrollment. Notably, a notable strength of our nomogram lies in its ability to stratify patients into distinct risk categories based on total score thresholds. By defining different risk subgroups, the model provides an intuitive framework for translating continuous prognostic information into clinically actionable strata. Kaplan–Meier curves demonstrated clearly separated survival outcomes across these subgroups, consistently observed in both cohorts for OS and CSS. This internal consistency supports the robustness and reproducibility of the model. Such risk stratification has tangible clinical implications. In routine practice, oncologists are frequently faced with decisions that hinge not just on population-level risk factors, but on patient-level prognosis—such as the intensity of surveillance, the need for adjuvant therapy, or referral for clinical trials. Our model bridges that gap by enabling a more refined assessment of each patient's trajectory. For example, patients categorized as low-risk might be spared from overtreatment and offered less intensive follow-up schedules, while high-risk individuals could be prioritized for aggressive multimodal therapy or inclusion in experimental treatment protocols. In addition, this stratification framework enhances the transparency of clinical communication. Presenting a patient with a quantified, evidence-based risk category may facilitate more informed shared decision-making and help manage expectations regarding prognosis and treatment goals. Importantly, the practicality of the nomogram—built on routinely available clinicopathological parameters—ensures its applicability even in resource-limited settings where molecular testing or comprehensive staging data may be unavailable. This widens its potential utility beyond academic centers and into real-world settings where RMS care is often delivered. Despite these strengths, several limitations must be acknowledged. The retrospective design introduced inherent selection biases. Crucially, SEER lacks important prognostic variables including surgical margin status, tumor invasiveness, and molecular markers such as FOXO1 fusion status, which have known prognostic value in RMS 23 . Detailed chemotherapy data including agents, doses, and treatment response were also unavailable, limiting evaluation of treatment effects. Prospective multicenter studies with integrated molecular, radiomic, and treatment-response biomarkers are needed to enhance predictive accuracy, validate generalizability, and refine clinical utility. Ultimately, our nomograms provide a practical tool to aid prognostication, guide treatment planning, and support risk-adapted strategies in RMS. 5. Conclusion This study presents the first comprehensive nomogram for predicting DSS in RMS patients across all anatomic sites. The model integrates clinical, pathological, and treatment-related factors, demonstrating superior accuracy compared to traditional staging systems. Future studies should incorporate molecular biomarkers to further refine prognostic assessment. Declarations Acknowledgments The authors would like to express their gratitude for the open access provided by the SEER database. Data Availability Statement All data utilized in this study are publicly available through the SEER database. Author Contributions Yuan Zheng: Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing Di Zhang: Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing Qihao Sun: Writing – review & editing Jiaoyang Lu: Supervision, Validation, Writing – review & editing, Project administration, Funding acquisition Disclosures The authors declare no conflicts of interest related to this study. It was conducted without any commercial or financial relationships that could be perceived as a potential conflict of interest. Funding This study received funding from the Natural Science Foundation of Shandong Province (ZR2024QH547), the National Natural Science Foundation of China (82370524) and the Taishan Scholars program. 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Pediatr Blood Cancer . Apr 2025;72:e31538. doi:10.1002/pbc.31538 Smith LM, Anderson JR, Qualman SJ, et al. Which patients with microscopic disease and rhabdomyosarcoma experience relapse after therapy? A report from the soft tissue sarcoma committee of the children's oncology group. J Clin Oncol . Oct 15 2001;19:4058-64. doi:10.1200/jco.2001.19.20.4058 Trama A, Lasalvia P, Stark D, et al. Incidence and survival of European adolescents and young adults diagnosed with sarcomas: EUROCARE-6 results. Eur J Cancer . Feb 25 2025;217:115212. doi:10.1016/j.ejca.2024.115212 Meza JL, Anderson J, Pappo AS, et al. Analysis of prognostic factors in patients with nonmetastatic rhabdomyosarcoma treated on intergroup rhabdomyosarcoma studies III and IV: the Children's Oncology Group. J Clin Oncol . Aug 20 2006;24:3844-51. doi:10.1200/jco.2005.05.3801 Chisholm JC, Marandet J, Rey A, et al. Prognostic factors after relapse in nonmetastatic rhabdomyosarcoma: a nomogram to better define patients who can be salvaged with further therapy. J Clin Oncol . Apr 1 2011;29:1319-25. doi:10.1200/jco.2010.32.1984 Breneman JC, Lyden E, Pappo AS, et al. Prognostic factors and clinical outcomes in children and adolescents with metastatic rhabdomyosarcoma--a report from the Intergroup Rhabdomyosarcoma Study IV. J Clin Oncol . Jan 1 2003;21:78-84. doi:10.1200/jco.2003.06.129 Koscielniak E, Stegmaier S, Ljungman G, et al. Prognostic factors in patients with localized and metastatic alveolar rhabdomyosarcoma. A report from two studies and two registries of the Cooperative Weichteilsarkom Studiengruppe CWS. Cancer Med . Jan 2025;14:e70215. doi:10.1002/cam4.70215 Tao Y, Cheng W, Zhen H, et al. Clinical features, treatment and prognosis of primary pulmonary rhabdomyosarcoma: A systemic review. BMC Pediatr . Mar 11 2025;25:185. doi:10.1186/s12887-025-05521-y Van Gaal JC, De Bont ES, Kaal SE, et al. Building the bridge between rhabdomyosarcoma in children, adolescents and young adults: the road ahead. Crit Rev Oncol Hematol . Jun 2012;82:259-79. doi:10.1016/j.critrevonc.2011.06.005 Li Z, Zhao L, Liu H, et al. Descriptive epidemiology and prognostic factors of atypical teratoid/rhabdoid tumors in the United States, 2001-2021. Neurosurg Rev . Jan 20 2025;48:65. doi:10.1007/s10143-025-03214-9 Haduong JH, Heske CM, Allen-Rhoades W, et al. An update on rhabdomyosarcoma risk stratification and the rationale for current and future Children's Oncology Group clinical trials. Pediatr Blood Cancer . Apr 2022;69:e29511. doi:10.1002/pbc.29511 Shern JF, Chen L, Chmielecki J, et al. Comprehensive genomic analysis of rhabdomyosarcoma reveals a landscape of alterations affecting a common genetic axis in fusion-positive and fusion-negative tumors. Cancer Discov . Feb 2014;4:216-31. doi:10.1158/2159-8290.Cd-13-0639 Supplementary Files SupplementaryFigures.pdf Figure S1. Kaplan-Meier survival curves and corresponding risk tables for overall survival (OS) and cancer-specific survival (CSS) across study cohorts. (A) OS in the overall cohort. (B) OS in the training cohort. (C) OS in the validation cohort. (D) CSS in the overall cohort. (E) CSS in the training cohort. (F) OS in the validation cohort. Figure S2. Optimal cutoff value determination for nomogram risk stratification. Distribution of total nomogram scores in the training cohort for OS (A, B) and CSS (D, E), with dashed lines indicating optimal cutoff points identified by X-tile analysis. The determined thresholds effectively stratified patients into distinct low-, intermediate-, and high-risk groups ( P <0.001 by log-rank test) (C, F). SupplementaryTables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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-7780087","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":529200271,"identity":"981d1943-4750-4316-bcdc-9c00bf40982e","order_by":0,"name":"Yuan Zheng","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Zheng","suffix":""},{"id":529200272,"identity":"c9648f13-d434-4044-82bb-0f5b4718021c","order_by":1,"name":"Di Zhang","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Zhang","suffix":""},{"id":529200273,"identity":"48cce8d5-c86a-40ce-af25-7cb284c0543c","order_by":2,"name":"Qihao Sun","email":"","orcid":"","institution":"David Geffen School of Medicine: University of California Los Angeles David Geffen School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qihao","middleName":"","lastName":"Sun","suffix":""},{"id":529200274,"identity":"a07053d1-7205-46ce-884e-a7733fc2d45e","order_by":3,"name":"Jiaoyang Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACPgaGhAMQJvMBBh4QfYCAFjaEFrYEorXAAI8BkVokEh4e+FFxWN6cf83nD2/bGOT4biQwfi7AryXhYM+Zw4Y7Z7zdJjm3jcFY8kYCs/QMAloO8LYdZtxw4+w2Zt42hsQNNxLYmHkI2fK37bD9hhtnHn8GaqknSsthoC2JG873MEgDtSQYENTC8yDhsMyZ9OQNN9jMJOeckzCceeZhszQ+LfzsOckf31RY2244f/jxhzdlNvJ8x5MPfsanBRgdCUCimYFBAkQzSAAxYwNeDQwM7AeARB3QvgMEFI6CUTAKRsGIBQBISlMQ3iCtcAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-6859-8390","institution":"Qilu Hospital of Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Jiaoyang","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2025-10-04 13:08:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7780087/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7780087/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94602962,"identity":"4bbb0946-8742-4e5e-8442-a674f6724608","added_by":"auto","created_at":"2025-10-28 20:05:49","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":25531,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx.docx","url":"https://assets-eu.researchsquare.com/files/rs-7780087/v1/e447e2ffc7a29d89d71d945b.docx"},{"id":94612408,"identity":"b097cda3-6120-42c8-9cb4-b1d90eccf5b8","added_by":"auto","created_at":"2025-10-29 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02:08:17","extension":"pdf","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":270597,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3nomogram.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7780087/v1/675e5543e0fa7ebb7de4dedd.pdf"},{"id":94602977,"identity":"1ab4d929-caeb-4caf-a092-b151cc34bdac","added_by":"auto","created_at":"2025-10-28 20:05:50","extension":"pdf","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2422522,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4Performance.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7780087/v1/4682e2aa8ba6abcf1a371dd8.pdf"},{"id":94602976,"identity":"de743e08-5b88-4906-aba8-4b21c09d6748","added_by":"auto","created_at":"2025-10-28 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20:05:50","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":91786,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7780087/v1/8c4dfe3eb0f81aa0755fd662.html"},{"id":94602958,"identity":"f311aaa7-0e66-4d2b-93f2-302f8b4d49f7","added_by":"auto","created_at":"2025-10-28 20:05:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57401,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy Design and Analytical Workflow\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFlowchart illustrating the development and validation of prognostic nomograms for rhabdomyosarcoma. (A) Data extraction from SEER database (2000-2021) of primary RMS patients. (B) Identification of key predictors through univariate and multivariate Cox regression analyses, including: age, primary site, histologic subtype, SEER stage, T stage, and treatment modalities (surgery/radiotherapy/chemotherapy). (C) Construction of dual nomograms for overall survival (OS) and cancer-specific survival (CSS) prediction. (D) Comprehensive validation framework incorporating: discrimination (C-index), calibration curves, decision curve analysis (DCA), and internal validation via bootstrap resampling. (E) Clinical application for risk-adapted therapeutic decision-making.\u003c/p\u003e","description":"","filename":"Binder11.png","url":"https://assets-eu.researchsquare.com/files/rs-7780087/v1/bbb55d4cb5d777ef9b96170b.png"},{"id":94612530,"identity":"e5d0df13-3ce7-486f-9372-b05998d1993c","added_by":"auto","created_at":"2025-10-29 02:10:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":304097,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier Survival Analysis by Clinicopathological Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A, J) Survival outcomes by age group (≤18, 19–60, \u0026gt;60 years). (B, K) Survival outcomes by primary anatomical tumor site (head/neck, thoracic, abdominal, retroperitoneal, genitourinary, extremities/trunk, others). (C, L) Survival comparison across anatomical site risk groups (favorable, unfavorable, NA), classified based on site-specific prognostic literature. (D, M) Survival according to T-stage (T1–T2 vs. T3–T4). (E, N) Survival differences among histologic subtypes (embryonal, alveolar, pleomorphic, others). (F, O) Survival based on SEER summary stage (localized, regional, distant). (G–I, P–R) Impact of treatment modalities (surgery, radiotherapy, and chemotherapy) on survival outcomes. All comparisons were statistically significant based on the log-rank test (\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.0001). The number at risk tables indicate the number and percentage of patients remaining at each time point relative to the baseline cohort size.\u003c/p\u003e","description":"","filename":"Binder12.png","url":"https://assets-eu.researchsquare.com/files/rs-7780087/v1/a1530d5a722bcaeff9972360.png"},{"id":94602959,"identity":"e611d53a-eaf3-40d7-ad3e-8f3c9914de9e","added_by":"auto","created_at":"2025-10-28 20:05:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51786,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognostic Nomograms for OS and CSS Prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePoint-based scoring system incorporating: (A) OS nomogram with predictors: age (≤18 to \u0026gt;60 years), primary site (7 categories), histology (4 subtypes), SEER stage, T-stage, and treatment modalities (surgery/radiotherapy/chemotherapy). Total points convert to 1-, 3-, and 5-year survival probabilities. (B) CSS nomogram with identical variable structure but distinct weighting. Higher total points indicate poorer prognosis. The linear predictor scale demonstrates model's log-hazard ratio transformation.\u003c/p\u003e","description":"","filename":"Binder13.png","url":"https://assets-eu.researchsquare.com/files/rs-7780087/v1/e45916939e5000708cb752dd.png"},{"id":94602960,"identity":"68886200-c358-4faa-ba4e-d88d53c6182a","added_by":"auto","created_at":"2025-10-28 20:05:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":289541,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComprehensive Validation of Prognostic Nomograms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Time-dependent ROC curves comparing nomogram, TNM staging, and SEER staging for OS prediction at 1/3/5 years in training cohort. (B) Time-dependent ROC curves comparing nomogram, TNM staging, and SEER staging for CSS prediction at 1/3/5 years in training cohort. (C-D) Calibration plots reveal excellent concordance between nomogram-predicted and observed survival probabilities for (C) OS and (D) CSS, with bootstrap-corrected curves closely following the ideal 45° line. (E) Decision curve analysis confirms greater clinical utility of both nomograms across all clinically relevant risk thresholds in the training cohort. (F-H) Validation cohort maintains robust performance: (F) OS prediction, (G) CSS prediction, with (H-I) well-calibrated survival estimates for both endpoints. (J) Decision curve analysis replicates the training cohort findings, demonstrating consistent net benefit superiority of the nomograms over alternative staging systems in clinical decision-making scenarios.\u003c/p\u003e","description":"","filename":"Binder14.png","url":"https://assets-eu.researchsquare.com/files/rs-7780087/v1/c6d2bb3a9a297d57c29f0d58.png"},{"id":94602966,"identity":"e9620342-4d01-4876-867e-4fd754a646ba","added_by":"auto","created_at":"2025-10-28 20:05:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":86302,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRisk Stratification Survival Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-B) OS in training and validation cohorts stratified by nomogram-derived risk groups (low/intermediate/high). (C-D) Corresponding CSS analyses. High-risk patients showed significantly poorer outcomes (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.0001). Risk categorization was based on nomogram total point and has consistent discrimination across cohorts.\u003c/p\u003e","description":"","filename":"Binder15.png","url":"https://assets-eu.researchsquare.com/files/rs-7780087/v1/24e202048debb509fccbd799.png"},{"id":96250742,"identity":"0bf5595b-8a87-4812-bba6-e21c6efc91c3","added_by":"auto","created_at":"2025-11-19 07:38:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1890512,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7780087/v1/e28783ca-7b97-4e0f-80d3-bdb89832bf03.pdf"},{"id":94612510,"identity":"2039054f-4ab1-4679-915b-65ef8a3e19b4","added_by":"auto","created_at":"2025-10-29 02:10:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":505350,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1. Kaplan-Meier survival curves and corresponding risk tables for overall survival (OS) and cancer-specific survival (CSS) across study cohorts.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) OS in the overall cohort. (B) OS in the training cohort. (C) OS in the validation cohort. (D) CSS in the overall cohort. (E) CSS in the training cohort. (F) OS in the validation cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S2. Optimal cutoff value determination for nomogram risk stratification.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDistribution of total nomogram scores in the training cohort for OS (A, B) and CSS (D, E), with dashed lines indicating optimal cutoff points identified by X-tile analysis. The determined thresholds effectively stratified patients into distinct low-, intermediate-, and high-risk groups (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001 by log-rank test) (C, F).\u003c/p\u003e","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7780087/v1/31c06d7ccc856a1c5c15d3c2.pdf"},{"id":94612436,"identity":"d0863048-4e82-41bf-99f8-0fd80adfb373","added_by":"auto","created_at":"2025-10-29 02:10:26","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":32441,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7780087/v1/74e4d885b746ef7f4ba90fa9.docx"}],"financialInterests":"","formattedTitle":"Prognostic Nomograms and Risk Stratification for Rhabdomyosarcoma Across Anatomic Sites","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in children, accounting for approximately 5% of all pediatric malignancies and a smaller proportion of adolescent and adult cancers\u003csup\u003e1, 2\u003c/sup\u003e. It originates from primitive mesenchymal cells with myogenic differentiation and can arise in various anatomical locations, including the head and neck, genitourinary tract, extremities, and trunk\u003csup\u003e3, 4\u003c/sup\u003e. Histologically, RMS is classified into embryonal, alveolar, and other rare subtypes, each demonstrating distinct biological behavior and differential responses to therapy\u003csup\u003e5\u0026ndash;7\u003c/sup\u003e. Despite advances in multimodal treatment, survival outcomes remain highly variable\u003csup\u003e8, 9\u003c/sup\u003e. Reported five-year overall survival (OS) rates exceed 70% in patients with localized tumors, but fall below 30% in those with metastatic disease at diagnosis\u003csup\u003e10, 11\u003c/sup\u003e. This pronounced variability underscores the biological complexity of RMS and continues to challenge efforts toward personalized treatment planning and long-term disease control.\u003c/p\u003e\u003cp\u003eHistorically, risk-adapted management of RMS has relied on classification systems such as the Intergroup Rhabdomyosarcoma Study (IRS) clinical grouping and the Children\u0026rsquo;s Oncology Group staging criteria\u003csup\u003e12\u003c/sup\u003e. These frameworks incorporate key prognostic variables, including tumor size, anatomical site, nodal status, and presence of distant metastasis\u003csup\u003e13\u003c/sup\u003e. While these tools have played a central role in protocol development and trial design, their categorical structure inherently limits prognostic granularity. In particular, they do not account for histologic subtype-specific behavior, the therapeutic interventions received, or inter-individual variability in outcomes among patients with similar stage classifications. Consequently, these traditional systems offer only coarse risk stratification and fall short in delivering individualized prognostic estimates or supporting precision treatment decisions.\u003c/p\u003e\u003cp\u003eTo address these limitations, we aimed to construct a robust, clinicopathology-informed nomogram to predict OS and cancer-specific survival (CSS) in patients with RMS. Leveraging data from a large, representative cohort, our goal was to develop a practical and accessible prognostic tool to support personalized risk assessment and guide clinical decision-making across a broad spectrum of RMS.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Data Source and Patient Selection\u003c/h2\u003e\u003cp\u003eWe retrospectively extracted data from the Surveillance, Epidemiology, and End Results (SEER) database. Patients diagnosed with primary RMS between January 1, 2010, and December 31, 2018, were identified based on ICD-O-3 histology codes: 8900/3 (rhabdomyosarcoma, NOS), 8910/3 (embryonal rhabdomyosarcoma), and 8920/3 (alveolar rhabdomyosarcoma). Inclusion criteria were as follows: (1) histologically confirmed diagnosis of RMS; (2) known survival status and follow-up time; and (3) available data for key clinical variables, including tumor characteristics and treatment details. Exclusion criteria were diagnosis based on autopsy or death certificate only and survival time equal to 0 months or an unknown survival status (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. This retrospective study was approved by the institutional review board, with informed consent waived, and was conducted using de-identified SEER data in compliance with the Declaration of Helsinki and relevant ethical regulations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Variables and Endpoints\u003c/h2\u003e\u003cp\u003eA range of variables encompassing demographic, tumor, and treatment characteristics were collected and analyzed. Demographic factors included the year of diagnosis, age at diagnosis, sex, and race. Tumor characteristics included the primary anatomical site, tumor site subgroup, tumor size, histological subtype, and staging information. Treatment modalities included surgery, radiotherapy, and chemotherapy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Statistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using R software. Categorical variables were summarized as frequencies and percentages. Survival was estimated by Kaplan\u0026ndash;Meier with log-rank tests, and prognostic factors were identified by univariate and multivariate Cox regression. Nomograms for OS and CSS were constructed from the final multivariable models to predict 1-, 3-, and 5-year survival.\u003c/p\u003e\u003cp\u003eModel performance was assessed by C-index, and calibration was evaluated using calibration plots comparing predicted versus observed outcomes. Internal validation was performed using 1,000 bootstrap resamples in the validation cohort. Time-dependent receiver operating characteristic (ROC) curves and area under the curve (AUC) values for survival outcomes were calculated using the time-ROC package to assess predictive accuracy over time. Clinical utility was examined through decision curve analysis (DCA), comparing the net benefit of the nomograms with the traditional staging. Patients were stratified into risk groups according to the nomogram scores utilizing X-tile software, and survival differences between the groups were compared. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Patient Characteristics\u003c/h2\u003e\u003cp\u003eThe entire cohort was randomly divided into a training cohort (n\u0026thinsp;=\u0026thinsp;3,034) and a validation cohort (n\u0026thinsp;=\u0026thinsp;1,301). The median age at diagnosis was 19 years, and 53.9% of patients were male. The most common primary tumor site was the head and neck region (29.3%), followed by the extremities and trunk (24.4%) and the genitourinary tract (20.5%). Based on histological subtype, alveolar RMS accounted for 32.8% of cases, embryonal RMS for 23.9%, and other rare subtypes for 31.4%. Tumor size was \u0026lt;\u0026thinsp;6.6 cm in 32.6% of patients and \u0026ge;\u0026thinsp;6.6 cm in 32.3%. Among patients with known T stage, the majority (39.4%) had T1\u0026ndash;T2 disease. Additionally, 12.5% of patients had regional lymph node metastasis, and 17.5% presented with distant metastasis at diagnosis. Surgical resection was performed in 55.8% of cases, radiotherapy was administered in 32.4%, and 76.8% of patients received chemotherapy. Baseline demographic and clinicopathological characteristics of the training and validation cohorts are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eBaseline characteristics of the overall study population.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\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\u003eOverall cohort\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTraining cohort\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eValidation cohort\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;4335\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u0026nbsp;= 3034\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003en\u0026nbsp;= 1301\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\u003eYear of diagnosis\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\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2000\u0026ndash;2005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1055 (24.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e734 (24.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e321 (24.7)\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\u003e2006\u0026ndash;2010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e971 (22.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e694 (22.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e277 (21.3)\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\u003e2011\u0026ndash;2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1008 (23.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e683 (22.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e325 (25.0)\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\u003e2016\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1301 (30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e923 (30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e378 (29.1)\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\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\u003e0.815\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2107 (48.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1465 (48.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e642 (49.3)\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\u003e19\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1282 (29.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e903 (29.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e379 (29.1)\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\u0026gt;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e946 (21.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e666 (22.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e280 (21.5)\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\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\u003e0.254\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\u003e3245 (74.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2277 (75.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e968 (74.4)\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\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e683 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e486 (16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e197 (15.1)\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\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e407 (9.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e271 (8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e136 (10.5)\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\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.367\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1038 (23.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e730 (24.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e308 (23.7)\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\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3297 (76.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2304 (75.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e993 (76.3)\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\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.815\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1038 (23.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e730 (24.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e308 (23.7)\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\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3297 (76.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2304 (75.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e993 (76.3)\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\u003eHistologic subtype\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\u003e0.579\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmbryonal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1037 (23.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e713 (23.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e324 (24.9)\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\u003eAlveolar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1422 (32.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1008 (33.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e414 (31.8)\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\u003ePleomorphic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e514 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e367 (12.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e147 (11.3)\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\u003e1362 (31.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e946 (31.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e416 (32.0)\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\u003ePrimary anatomical site\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\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHead and neck\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1271 (29.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e910 (30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e361 (27.7)\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\u003eThoracic region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e108 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24 (1.8)\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\u003eAbdominal region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e247 (5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e162 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85 (6.5)\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\u003ePeritoneum/retroperitoneum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e584 (13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e416 (13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e168 (12.9)\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\u003eGenitourinary tract\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e889 (20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e615 (20.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e274 (21.1)\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\u003eExtremities and trunk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1056 (24.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e728 (24.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e328 (25.2)\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\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e180 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e119 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61 (4.7)\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\u003eTumor site subgroup\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\u003e0.411\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFavorable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1528 (35.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1060 (34.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e468 (36.0)\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\u003eUnfavorable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2773 (64.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1947 (64.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e826 (63.5)\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\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (0.5)\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\u003e0.640\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\u003e52 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13 (1.0)\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\u003e173 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e124 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49 (3.8)\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\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4110 (94.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2871 (94.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1239 (95.2)\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\u003e\u003cb\u003eSEER stage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.516\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocalized\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1307 (30.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e912 (30.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e395 (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\u003eRegional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1302 (30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e928 (30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e374 (28.7)\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\u003eDistant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1481 (34.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1019 (33.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e462 (35.5)\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\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e245 (5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e175 (5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70 (5.4)\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\u003eTNM stage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.522\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e381 (8.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e258 (8.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e123 (9.5)\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\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e117 (2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33 (2.5)\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\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e391 (9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e269 (8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e122 (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\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e816 (18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e559 (18.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e257 (19.8)\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\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2630 (60.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1864 (61.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e766 (58.9)\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\u003cb\u003eT stage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.508\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1-T2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1710 (39.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1181 (38.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e529 (40.7)\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\u003eT3-T4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e200 (4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e144 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56 (4.3)\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\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2425 (55.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1709 (56.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e716 (55.0)\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\u003eNodal involvement\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\u003e0.696\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo regional LN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1591 (36.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1104 (36.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e487 (37.4)\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\u003eRegional LN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e542 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e376 (12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e166 (12.8)\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\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2202 (50.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1554 (51.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e648 (49.8)\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\u003eMetastasis at diagnosis\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\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo distant metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1550 (35.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1099 (36.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e451 (34.7)\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\u003eDistant metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e758 (17.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e508 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e250 (19.2)\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\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2027 (46.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1427 (47.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e600 (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\u003eTumor size (cm)\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\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt; 6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1414 (32.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e964 (31.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e450 (34.6)\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\u0026ge;\u0026thinsp;6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1402 (32.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e988 (32.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e414 (31.8)\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\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1519 (35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1082 (35.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e437 (33.6)\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\u003ePrimary site surgery\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\u003e0.604\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\u003e1915 (44.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1332 (43.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e583 (44.8)\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2420 (55.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1702 (56.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e718 (55.2)\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\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\u003e0.401\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\u003e2931 (67.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2039 (67.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e892 (68.6)\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1404 (32.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e995 (32.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e409 (31.4)\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\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\u003e0.765\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\u003e1006 (23.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e689 (22.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e317 (24.4)\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\u003e3329 (76.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2345 (77.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e984 (75.6)\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\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Survival Outcomes and Subgroup Analysis\u003c/h2\u003e\u003cp\u003eThe median OS for the entire cohort was 39 months, and the median CSS was 75 months. The 1-, 3-, and 5-year OS rates were 73.31%, 51.12%, and 46.15%, respectively, while the corresponding CSS rates were 76.92%, 55.81%, and 51.34% (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eKaplan\u0026ndash;Meier survival analysis revealed significant differences in both OS and CSS across multiple clinicopathological subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Specifically, age was a strong prognostic factor, with patients aged\u0026thinsp;\u0026le;\u0026thinsp;18 years demonstrating superior survival compared to those aged 19\u0026ndash;60 years and \u0026gt;\u0026thinsp;60 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ). Primary anatomical site also significantly influenced survival outcomes. Patients with tumors located in the head and neck region had the most favorable prognosis, whereas those with thoracic tumors exhibited markedly poorer outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eK). To further evaluate the impact of anatomical tumor sites, we stratified tumor locations into favorable, unfavorable, and NA categories based on previous literature and organ-specific prognostic characteristics. This classification revealed substantial survival heterogeneity across these subgroups, with favorable-site tumors associated with significantly improved OS and CSS compared to unfavorable-site tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eL). Additional stratified analyses revealed worse survival outcomes with higher T-stage (T3\u0026ndash;T4 vs. T1\u0026ndash;T2; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eM), as well as across different histologic subtypes, with embryonal subtype showing relatively better prognosis than alveolar, pleomorphic, or other subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eN). Similarly, patients with localized disease exhibited significantly longer survival compared to those with regional or distant-stage disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eO). Moreover, receipt of surgery, radiotherapy, and chemotherapy were all associated with significant survival benefits (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG\u0026ndash;I, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eP-R).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Prognostic Factor Analysis\u003c/h2\u003e\u003cp\u003eUnivariate Cox regression analysis identified several variables significantly associated with OS, including age at diagnosis, tumor size, histological subtype, T stage, nodal involvement, metastasis at diagnosis, surgery, radiotherapy, and chemotherapy (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similar results were observed for CSS, as detailed in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eIn the multivariate Cox regression analysis for OS, several factors remained independently associated with prognosis. Age\u0026thinsp;\u0026gt;\u0026thinsp;60 years was a strong predictor of poor OS (HR\u0026thinsp;=\u0026thinsp;4.66, 95% CI: 3.96\u0026ndash;5.50, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by age 19\u0026ndash;60 years (HR\u0026thinsp;=\u0026thinsp;2.28, 95% CI: 2.00\u0026ndash;2.60, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), compared with those aged\u0026thinsp;\u0026le;\u0026thinsp;18 years. Female sex was associated with improved survival (HR\u0026thinsp;=\u0026thinsp;0.789, 95% CI: 0.640\u0026ndash;0.972, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026). Patients with alveolar histology had worse outcomes compared to embryonal subtype (HR\u0026thinsp;=\u0026thinsp;0.60, 95% CI: 0.51\u0026ndash;0.70, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Tumors located in thoracic, abdominal, retroperitoneal, genitourinary, and extremity/trunk regions were associated with significantly worse OS relative to those in the head and neck. Specifically, thoracic tumors had an HR of 2.12 (95% CI: 1.62\u0026ndash;2.77, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and retroperitoneal tumors had an HR of 1.87 (95% CI: 1.57\u0026ndash;2.22, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with distant-stage disease had significantly increased risk of mortality (HR\u0026thinsp;=\u0026thinsp;3.89, 95% CI: 3.33\u0026ndash;4.55, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, the absence of surgical treatment was strongly associated with inferior OS (HR\u0026thinsp;=\u0026thinsp;0.61, 95% CI: 0.53\u0026ndash;0.69, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as was lack of radiotherapy (HR\u0026thinsp;=\u0026thinsp;0.76, 95% CI: 0.67\u0026ndash;0.87, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and chemotherapy (HR\u0026thinsp;=\u0026thinsp;0.63, 95% CI: 0.55\u0026ndash;0.71, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Multivariate analysis for CSS revealed consistent findings. Advanced age (19\u0026ndash;60 years: HR\u0026thinsp;=\u0026thinsp;2.21, 95% CI: 1.92\u0026ndash;2.55; \u0026gt;60 years: HR\u0026thinsp;=\u0026thinsp;3.98, 95% CI: 3.32\u0026ndash;4.77; both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and alveolar histology (HR\u0026thinsp;=\u0026thinsp;0.60, 95% CI: 0.51\u0026ndash;0.72, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were independent predictors of poor CSS. Tumors located in thoracic (HR\u0026thinsp;=\u0026thinsp;2.28, 95% CI: 1.67\u0026ndash;3.10), abdominal (HR\u0026thinsp;=\u0026thinsp;2.00, 95% CI: 1.53\u0026ndash;2.61), and retroperitoneal regions (HR\u0026thinsp;=\u0026thinsp;1.89, 95% CI: 1.53\u0026ndash;2.35) also had significantly worse CSS (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with distant metastases had a markedly increased risk of cancer-specific death (HR\u0026thinsp;=\u0026thinsp;4.50, 95% CI: 3.76\u0026ndash;5.38, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The lack of surgery (HR\u0026thinsp;=\u0026thinsp;0.60, 95% CI: 0.51\u0026ndash;0.69, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), radiotherapy (HR\u0026thinsp;=\u0026thinsp;0.77, 95% CI: 0.67\u0026ndash;0.89, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), or chemotherapy (HR\u0026thinsp;=\u0026thinsp;0.76, 95% CI: 0.66\u0026ndash;0.89, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) remained strong predictors of unfavorable CSS (\u003cb\u003eSupplementary Table\u0026nbsp;2)\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Construction and Evaluation of the Prognostic Nomogram\u003c/h2\u003e\u003cp\u003eFurthermore, two comprehensive prognostic tools were developed: one for OS and another for CSS, incorporating all significant independent predictors. These nomograms enable clinicians to estimate survival probabilities at 1-, 3-, and 5-year intervals through a visual scoring system, where each prognostic factor contributes points proportional to its relative importance in the model (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe predictive performance of the nomograms was rigorously evaluated through multiple validation metrics. For OS prediction, the concordance indices reached 0.759 (training cohort) and 0.785 (validation cohort), representing a 15.7\u0026ndash;30.2% improvement over conventional SEER staging (0.656\u0026ndash;0.672) and a 30.4\u0026ndash;84.7% enhancement compared to TNM staging (0.425\u0026ndash;0.582). The CSS nomogram showed comparable discriminative ability, with C-indices of 0.790 and 0.774 in training and validation cohorts respectively, outperforming existing staging systems by 16.0-54.3%. Time-dependent ROC analysis further confirmed the superior predictive accuracy of our models. The OS nomogram achieved AUC values of 0.858, 0.842, and 0.859 for 1-, 3-, and 5-year predictions in the training set - significantly higher (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than both TNM (0.590\u0026ndash;0.609) and SEER (0.683\u0026ndash;0.708) systems. This advantage persisted in the validation cohort (AUCs: 0.851, 0.843, 0.838). Similarly, the CSS nomogram maintained robust predictive performance across all timepoints (training AUCs: 0.817\u0026thinsp;\u0026minus;\u0026thinsp;0.787; validation AUCs: 0.759\u0026ndash;0.836), consistently surpassing conventional staging methods (AUC differences: 0.101\u0026ndash;0.258). Model calibration was excellent, with calibration curves demonstrating close alignment between predicted and observed survival outcomes in both cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). DCA revealed that the nomograms provided superior net benefit across clinically relevant threshold probabilities, suggesting their practical utility for risk stratification and therapeutic decision-making \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Risk Stratification\u003c/h2\u003e\u003cp\u003ePatients were stratified into three risk groups\u0026mdash;low, intermediate, and high\u0026mdash;according to the total risk scores derived from the nomogram. For OS, the cutoff values were 92 and 134: patients with scores\u0026thinsp;\u0026le;\u0026thinsp;92 were classified as low-risk, those with scores between 93 and 134 as intermediate-risk, and those with scores\u0026thinsp;\u0026gt;\u0026thinsp;134 as high-risk. For CSS, the corresponding cutoff values were 98 and 146.\u003c/p\u003e\u003cp\u003eKaplan\u0026ndash;Meier survival analysis demonstrated significant differences in survival outcomes among the three risk groups (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In the training cohort, the 1-, 3-, and 5-year OS rates were 89.1%, 70.0%, and 65.2% for the low-risk group (n\u0026thinsp;=\u0026thinsp;1713), 72.9%, 39.6%, and 31.2% for the intermediate-risk group (n\u0026thinsp;=\u0026thinsp;628), and 35.1%, 16.2%, and 13.1% for the high-risk group (n\u0026thinsp;=\u0026thinsp;693), respectively. A similar trend was observed for CSS, with 1-, 3-, and 5-year rates of 91.4%, 74.9%, and 71.4% in the low-risk group (n\u0026thinsp;=\u0026thinsp;1552), 76.0%, 45.0%, and 37.6% in the intermediate-risk group (n\u0026thinsp;=\u0026thinsp;912), and 36.9%, 17.5%, and 13.4% in the high-risk group (n\u0026thinsp;=\u0026thinsp;570). The validation cohort consistently reproduced these prognostic patterns. Low-risk patients (n\u0026thinsp;=\u0026thinsp;766 for OS; n\u0026thinsp;=\u0026thinsp;680 for CSS) maintained 5-year survival rates of 62.5% OS and 67.9% CSS, while high-risk patients (n\u0026thinsp;=\u0026thinsp;309 for OS; n\u0026thinsp;=\u0026thinsp;256 for CSS) had 5-year OS rates of 10.3% and 5-year CSS rates of 15.2%. The intermediate-risk group (n\u0026thinsp;=\u0026thinsp;226 for OS; n\u0026thinsp;=\u0026thinsp;365 for CSS) showed 5-year survival rates of 33.6% and 38.6%, respectively, confirming the stability of this three-tier classification system.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this population-based study, we developed and validated nomograms for predicting survival outcomes in patients with RMS. These nomograms incorporated readily available clinical and pathological variables and demonstrated satisfactory discriminatory performance and clinical utility in both training and validation cohorts.\u003c/p\u003e\u003cp\u003eTo our knowledge, this is among the first large-scale population-level analyses that systematically evaluate both OS and CSS across an unselected RMS cohort, including all age groups, histological subtypes, and tumor locations. By simultaneously modeling OS and CSS, our study provides complementary insights into prognosis: OS reflects mortality from all causes, capturing the influence of comorbidities and non-cancer-related deaths, while CSS isolates mortality directly attributable to RMS. The divergence observed between OS and CSS in subgroups such as older patients or those with favorable tumor sites underscores the importance of considering both endpoints in clinical prognostication and follow-up planning. For instance, CSS may better indicate tumor aggressiveness and therapeutic urgency in high-risk patients, whereas OS may better capture survivorship issues in lower-risk or long-term survivors. Similar distinctions have been noted in prior sarcoma studies\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur findings underscore the prognostic importance of several key factors\u0026mdash;age at diagnosis, histological subtype, primary tumor site, disease stage, and treatment strategies\u0026mdash;all of which align with established literature\u003csup\u003e15\u0026ndash;17\u003c/sup\u003e. In particular, older age was significantly associated with poorer outcomes. Likewise, the alveolar subtype was independently linked to worse survival compared to the embryonal subtype, reflecting its more aggressive biological behavior\u003csup\u003e18\u003c/sup\u003e. Tumors originating in traditionally favorable sites, such as the head and neck (including orbit and non-para-meningeal regions), showed markedly better survival than those in unfavorable sites including thoracic region, corroborating earlier prognostic results\u003csup\u003e19\u003c/sup\u003e. Moreover, multimodal treatment incorporating radiotherapy and chemotherapy conferred survival benefits, particularly in patients with advanced local disease or incomplete resection, supporting current consensus guidelines\u003csup\u003e20\u003c/sup\u003e. Taken together, the findings emphasize the clinical relevance of integrating classical prognostic indicators with distinct survival endpoints and underscore the utility of a comprehensive predictive model that captures both disease-specific and all-cause survival risks in RMS.\u003c/p\u003e\u003cp\u003eWhile traditional staging systems remain valuable for risk stratification and treatment decision-making, their ability to predict individualized outcomes is limited by their categorical nature and inability to incorporate multiple prognostic factors simultaneously\u003csup\u003e21, 22\u003c/sup\u003e. Our nomograms address these limitations by integrating multiple independent prognostic variables into a unified model that provides personalized survival estimates. The nomograms outperformed the TNM and SEER staging systems with higher concordance indices and superior predictive accuracy demonstrated by ROC and calibration analyses. Furthermore, DCA demonstrated that the nomogram yielded greater net clinical benefit across a range of threshold probabilities, underscoring its value in real-world decision-making contexts. Although the IRS clinical grouping system is a cornerstone of RMS risk stratification in cooperative group trials, it could not be included in our comparative analysis due to limitations of the SEER database, which lacks key variables such as surgical margin status, tumor invasiveness, and chemotherapy details. Nevertheless, several prognostic factors emphasized in IRS algorithms\u0026mdash;such as age, histologic subtype, and site\u0026mdash;are incorporated into our model, suggesting conceptual alignment despite methodological differences. Altogether, our nomogram complements existing staging frameworks by offering a refined, patient-specific risk estimation tool. Its strong performance metrics and broad applicability make it a valuable adjunct to traditional systems, particularly in contexts where detailed IRS data are unavailable or when individualized prognosis is needed to guide therapy or trial enrollment.\u003c/p\u003e\u003cp\u003eNotably, a notable strength of our nomogram lies in its ability to stratify patients into distinct risk categories based on total score thresholds. By defining different risk subgroups, the model provides an intuitive framework for translating continuous prognostic information into clinically actionable strata. Kaplan\u0026ndash;Meier curves demonstrated clearly separated survival outcomes across these subgroups, consistently observed in both cohorts for OS and CSS. This internal consistency supports the robustness and reproducibility of the model. Such risk stratification has tangible clinical implications. In routine practice, oncologists are frequently faced with decisions that hinge not just on population-level risk factors, but on patient-level prognosis\u0026mdash;such as the intensity of surveillance, the need for adjuvant therapy, or referral for clinical trials. Our model bridges that gap by enabling a more refined assessment of each patient's trajectory. For example, patients categorized as low-risk might be spared from overtreatment and offered less intensive follow-up schedules, while high-risk individuals could be prioritized for aggressive multimodal therapy or inclusion in experimental treatment protocols. In addition, this stratification framework enhances the transparency of clinical communication. Presenting a patient with a quantified, evidence-based risk category may facilitate more informed shared decision-making and help manage expectations regarding prognosis and treatment goals. Importantly, the practicality of the nomogram\u0026mdash;built on routinely available clinicopathological parameters\u0026mdash;ensures its applicability even in resource-limited settings where molecular testing or comprehensive staging data may be unavailable. This widens its potential utility beyond academic centers and into real-world settings where RMS care is often delivered.\u003c/p\u003e\u003cp\u003eDespite these strengths, several limitations must be acknowledged. The retrospective design introduced inherent selection biases. Crucially, SEER lacks important prognostic variables including surgical margin status, tumor invasiveness, and molecular markers such as FOXO1 fusion status, which have known prognostic value in RMS\u003csup\u003e23\u003c/sup\u003e. Detailed chemotherapy data including agents, doses, and treatment response were also unavailable, limiting evaluation of treatment effects. Prospective multicenter studies with integrated molecular, radiomic, and treatment-response biomarkers are needed to enhance predictive accuracy, validate generalizability, and refine clinical utility. Ultimately, our nomograms provide a practical tool to aid prognostication, guide treatment planning, and support risk-adapted strategies in RMS.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study presents the first comprehensive nomogram for predicting DSS in RMS patients across all anatomic sites. The model integrates clinical, pathological, and treatment-related factors, demonstrating superior accuracy compared to traditional staging systems. Future studies should incorporate molecular biomarkers to further refine prognostic assessment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their gratitude for the open access provided by the SEER database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data utilized in this study are publicly available through the SEER database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYuan Zheng: Conceptualization, Data curation, Formal analysis, Methodology, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eDi Zhang: Conceptualization, Data curation, Formal analysis, Methodology, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eQihao Sun: Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eJiaoyang Lu: Supervision, Validation, Writing \u0026ndash; review \u0026amp; editing, Project administration, Funding acquisition\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest related to this study. It was conducted without any commercial or financial relationships that could be perceived as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis study received funding from the Natural Science Foundation of Shandong Province (ZR2024QH547), the National Natural Science Foundation of China (82370524) and the Taishan Scholars program.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval was not applicable as all data were retrieved from the public SEER database, which contains de-identified information. Therefore, ethical approval was unnecessary.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWeiss AR, Ferrari A, Mascarenhas L, et al. 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Soft Tissue Sarcomas in Adolescents and Young Adults. \u003cem\u003eJ Clin Oncol\u003c/em\u003e. Feb 20 2024;42:675-685. doi:10.1200/jco.23.01275\u003c/li\u003e\n\u003cli\u003eTerwisscha van Scheltinga S, Schoot RA, Routh JC, et al. Lymph Node Staging and Treatment in Pediatric Patients With Soft Tissue Sarcomas: A Consensus Opinion From the Children\u0026apos;s Oncology Group, European paediatric Soft Tissue Sarcoma Study Group, and the Cooperative Weichteilsarkom Studiengruppe. \u003cem\u003ePediatr Blood Cancer\u003c/em\u003e. Apr 2025;72:e31538. doi:10.1002/pbc.31538\u003c/li\u003e\n\u003cli\u003eSmith LM, Anderson JR, Qualman SJ, et al. Which patients with microscopic disease and rhabdomyosarcoma experience relapse after therapy? A report from the soft tissue sarcoma committee of the children\u0026apos;s oncology group. \u003cem\u003eJ Clin Oncol\u003c/em\u003e. Oct 15 2001;19:4058-64. doi:10.1200/jco.2001.19.20.4058\u003c/li\u003e\n\u003cli\u003eTrama A, Lasalvia P, Stark D, et al. Incidence and survival of European adolescents and young adults diagnosed with sarcomas: EUROCARE-6 results. \u003cem\u003eEur J Cancer\u003c/em\u003e. Feb 25 2025;217:115212. doi:10.1016/j.ejca.2024.115212\u003c/li\u003e\n\u003cli\u003eMeza JL, Anderson J, Pappo AS, et al. Analysis of prognostic factors in patients with nonmetastatic rhabdomyosarcoma treated on intergroup rhabdomyosarcoma studies III and IV: the Children\u0026apos;s Oncology Group. \u003cem\u003eJ Clin Oncol\u003c/em\u003e. Aug 20 2006;24:3844-51. doi:10.1200/jco.2005.05.3801\u003c/li\u003e\n\u003cli\u003eChisholm JC, Marandet J, Rey A, et al. Prognostic factors after relapse in nonmetastatic rhabdomyosarcoma: a nomogram to better define patients who can be salvaged with further therapy. \u003cem\u003eJ Clin Oncol\u003c/em\u003e. Apr 1 2011;29:1319-25. doi:10.1200/jco.2010.32.1984\u003c/li\u003e\n\u003cli\u003eBreneman JC, Lyden E, Pappo AS, et al. Prognostic factors and clinical outcomes in children and adolescents with metastatic rhabdomyosarcoma--a report from the Intergroup Rhabdomyosarcoma Study IV. \u003cem\u003eJ Clin Oncol\u003c/em\u003e. Jan 1 2003;21:78-84. doi:10.1200/jco.2003.06.129\u003c/li\u003e\n\u003cli\u003eKoscielniak E, Stegmaier S, Ljungman G, et al. Prognostic factors in patients with localized and metastatic alveolar rhabdomyosarcoma. A report from two studies and two registries of the Cooperative Weichteilsarkom Studiengruppe CWS. \u003cem\u003eCancer Med\u003c/em\u003e. Jan 2025;14:e70215. doi:10.1002/cam4.70215\u003c/li\u003e\n\u003cli\u003eTao Y, Cheng W, Zhen H, et al. Clinical features, treatment and prognosis of primary pulmonary rhabdomyosarcoma: A systemic review. \u003cem\u003eBMC Pediatr\u003c/em\u003e. Mar 11 2025;25:185. doi:10.1186/s12887-025-05521-y\u003c/li\u003e\n\u003cli\u003eVan Gaal JC, De Bont ES, Kaal SE, et al. Building the bridge between rhabdomyosarcoma in children, adolescents and young adults: the road ahead. \u003cem\u003eCrit Rev Oncol Hematol\u003c/em\u003e. Jun 2012;82:259-79. doi:10.1016/j.critrevonc.2011.06.005\u003c/li\u003e\n\u003cli\u003eLi Z, Zhao L, Liu H, et al. Descriptive epidemiology and prognostic factors of atypical teratoid/rhabdoid tumors in the United States, 2001-2021. \u003cem\u003eNeurosurg Rev\u003c/em\u003e. Jan 20 2025;48:65. doi:10.1007/s10143-025-03214-9\u003c/li\u003e\n\u003cli\u003eHaduong JH, Heske CM, Allen-Rhoades W, et al. An update on rhabdomyosarcoma risk stratification and the rationale for current and future Children\u0026apos;s Oncology Group clinical trials. \u003cem\u003ePediatr Blood Cancer\u003c/em\u003e. Apr 2022;69:e29511. doi:10.1002/pbc.29511\u003c/li\u003e\n\u003cli\u003eShern JF, Chen L, Chmielecki J, et al. Comprehensive genomic analysis of rhabdomyosarcoma reveals a landscape of alterations affecting a common genetic axis in fusion-positive and fusion-negative tumors. \u003cem\u003eCancer Discov\u003c/em\u003e. Feb 2014;4:216-31. doi:10.1158/2159-8290.Cd-13-0639\u003cbr\u003e \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rhabdomyosarcoma, soft tissue sarcoma, overall survival, cancer-specific survival, prognostic model","lastPublishedDoi":"10.21203/rs.3.rs-7780087/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7780087/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eRhabdomyosarcoma (RMS) is a rare soft tissue sarcoma that predominantly affects children and adolescents. Current prognostic models are limited by their focus on single anatomic sites or small patient cohorts. This study aims to develop and validate two prognostic nomograms incorporating clinical, pathological, and treatment-related factors to predict overall survival (OS) and cancer-specific survival (CSS) in RMS patients across different anatomic locations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData from patients diagnosed with primary RMS between 2010 and 2018 were extracted. Prognostic nomograms were constructed based on independent risk factors, with model performance assessed via the concordance index (C-index), calibration curves, and decision curve analysis (DCA). Risk stratification was performed based on nomogram-derived scores.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe study included 4,335 patients, randomly divided into training (n\u0026thinsp;=\u0026thinsp;3,034) and validation (n\u0026thinsp;=\u0026thinsp;1,301) cohorts. The median age was 19 years, with 53.9% male predominance. Common primary sites were the head/neck (29.3%), extremities (24.4%), and genitourinary tract (20.5%). Alveolar (32.8%) and embryonal (23.9%) subtypes were most frequent. Key independent prognostic factors included age, tumor size, histologic subtype, T stage, nodal status, metastasis, and treatment modalities (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The OS nomogram achieved C-indices of 0.759 (training) and 0.785 (validation), while the CSS nomogram reached 0.790 and 0.774, outperforming conventional staging systems. Calibration curves and DCA confirmed strong predictive accuracy and clinical utility. Risk stratification effectively differentiated low-, intermediate-, and high-risk groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with consistent validation results.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThese comprehensive nomograms offer individualized OS and CSS predictions for RMS patients, serving as valuable tools for prognostic evaluation.\u003c/p\u003e","manuscriptTitle":"Prognostic Nomograms and Risk Stratification for Rhabdomyosarcoma Across Anatomic Sites","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-28 20:05:45","doi":"10.21203/rs.3.rs-7780087/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0af90579-2b87-4ea6-874d-5994b7365e2a","owner":[],"postedDate":"October 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-18T01:34:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-28 20:05:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7780087","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7780087","identity":"rs-7780087","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}