Development and Validation of a Prognostic Model for Nasal cavity Squamous Cell Carcinoma Based on the SEER Database

Development and Validation of a Prognostic Model for Nasal cavity Squamous Cell Carcinoma Based on the SEER Database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Development and Validation of a Prognostic Model for Nasal cavity Squamous Cell Carcinoma Based on the SEER Database Qiwei Zeng, Ming Sheng, Si Ouyang, Bin Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6372710/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract This study sought to construct and validate a prognostic nomogram for predicting cause-specific survival (CSS) in patients with nasal cavity squamous cell carcinoma (NCSCC). We conducted a retrospective analysis of clinical data from NCSCC patients registered in the SEER database between 2007 and 2015. Statistical analyses were performed to assess CSS rates and identify prognostic factors. The study cohort comprised 580 NCSCC patients, with CSS probabilities of 89.1%, 74.8%, and 63.6% at 1, 3, and 5 years, respectively. Multivariate Cox regression analysis identified age, American Joint Committee on Cancer (AJCC) stage, and radiotherapy administration as independent prognostic factors significantly associated with CSS. The accuracy of the prediction was evaluated using the C-index and calibration curve. Decision curve analysis (DCA) and receiver operating characteristic (ROC) were utilized to compare the nomogram with the AJCC stage system in order to assess its superiority. We developed and validated a predictive model for 1-, 3-, and 5-year CSS in NCSCC based on a large retrospective cohort. The nomogram demonstrates clinical utility in guiding individualized treatment strategies and patient management. Biological sciences/Cancer/Cancer models Biological sciences/Cancer/Head and neck cancer Health sciences/Medical research Health sciences/Oncology Nasal Cavity Squamous Cell Carcinoma Prognostic Model SEER Database Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Head and neck squamous cell carcinoma (HNSCC) represents the seventh most prevalent malignancy worldwide, with an estimated annual incidence of 890,000 new cases, constituting 4.5% of global cancer diagnoses, and accounting for 450,000 deaths, which corresponds to 4.6% of total cancer-related mortality 1 . This neoplasm exhibits a predilection for elderly male populations. Nasal cavity squamous cell carcinoma (NCSCC), a relatively uncommon subtype within the head and neck region, demonstrates an incidence rate of approximately 0.148 cases per 100,000 individuals 2 . Established etiological factors for NCSCC include tobacco smoking and human papillomavirus (HPV) infection, as evidenced by multiple studies 3 , 4 . The limited epidemiological data on nasal cavity malignancies, compounded by the historical classification of nasal cavity and sinonasal cancers as a unified entity 5 – 8 , has resulted in a paucity of large-scale studies specifically examining survival outcomes in NCSCC. Malignancies of the nasal cavity demonstrate considerable histological diversity compared to other head and neck subsites. Nasal cavity malignancies encompass a diverse spectrum of histopathological subtypes, including but not limited to melanoma, adenocarcinoma, salivary gland-type carcinomas, olfactory neuroblastoma, and sinonasal undifferentiated carcinoma. However, epidemiological evidence consistently demonstrates that squamous cell carcinoma (SCC) represents the predominant histological variant, accounting for the majority of cases 3 . The distinct pathological subtypes exhibit significant variations in epidemiological prevalence, therapeutic approaches, and prognostic trajectories. By restricting analytical investigations to a specific pathological subtype, researchers can effectively minimize confounding variables, thereby enhancing the precision of study outcomes and improving their clinical applicability. As NCSCC represents the predominant histological subtype among nasal cavity malignancies, accounting for the majority of cases 9 , this study aims to analyze cancer-specific survival (CSS) and prognostic factors in NCSCC patients using survival data from the SEER database. A predictive model will be developed to visualize clinical data, providing actionable insights for clinical decision-making. Methods Clinical Data In this study, data on NCSCC patients were downloaded using SEER Stat 8.4.5 software from the database "Incidence - SEER Research Data, 17 Registries, Nov 2023 Sub (2000–2021) - Linked To County Attributes - Time Dependent (1990–2022) Income/Rurality, 1969–2022 Counties, National Cancer Institute, DCCPS, Surveillance Research Program, released April 2024, based on the November 2023 submission." The specific workflow is illustrated in Fig. 1 . Variable Selection The study analyzed 16 demographic and clinical variables. Considering the predominantly middle-aged and elderly population of NCSCC patients, age stratification was established using the cohort's median age of 60 years, dividing participants into non-elderly (< 60 years) and elderly (≥ 60 years) groups. Demographic variables included sex, race, and marital status. Clinical characteristics comprised tumor differentiation grade (well-, moderately-, poorly-, and undifferentiated), tumor size (categorized into three tiers at 2-cm intervals), surgical intervention, radiotherapy, chemotherapy, systemic therapy, AJCC TNM stage (6th edition), SEER combined stage (SCSS), T-stage (early [T1-T2] vs. advanced [T3-T4]), N-stage (localized [N0-N1] vs. extensive [N3-N4] nodal involvement), and M-stage. The dataset was randomly divided into training and validation cohorts in a 7:3 ratio. Statistical analysis Statistical analyses were performed using R software (version 4.43). Comparative analyses between the training and validation cohorts were conducted using chi-square tests. The development of the prognostic model in the training cohort followed a three-stage analytical approach: initially, potential predictors were screened through univariable Cox regression analysis, with variables demonstrating a significance level of p < 0.10 proceeding to subsequent analysis. These selected variables were then subjected to LASSO regression for feature selection and dimensionality reduction. Finally, the variables retained from the LASSO regression were incorporated into a multivariable Cox proportional hazards model, which served as the foundation for constructing the nomogram and generating the corresponding forest plot. Model validation encompassed the assessment of discrimination capability through Harrell's concordance index (C-index) and evaluation of calibration accuracy using bootstrap-corrected calibration curves (1,000 iterations). The clinical utility of the nomogram was compared against the AJCC staging system through decision curve analysis (DCA). Results Clinical and Demographic Characteristics The demographic and clinical characteristics of 580 patients with NCSCC, encompassing age, sex, race, tumor grade, SEER combined stage, surgical intervention, radiotherapy, and chemotherapy status, are comprehensively detailed in Table 1 , illustrating their respective distribution patterns. The P-values for all variables between the modeling group and validation group were greater than 0.05, indicating no significant difference between the two groups. CSS analysis demonstrated that the cohort exhibited survival probabilities of 89.1% at 1 year, 74.8% at 3 years, and 63.6% at 5 years. Table 1 Clinical and Demographic Characteristics Variables Total Training Validation P (n = 580) (n = 406) (n = 174) Age, n (%) 0.623 < 60years 207 (36) 148 (36) 59 (34) ≥ 60years 373 (64) 258 (64) 115 (66) Sex, n (%) 0.829 Female 178 (31) 123 (30) 55 (32) Male 402 (69) 283 (70) 119 (68) Race, n (%) 0.516 White 515 (89) 364 (90) 151 (87) Black 31 (5) 19 (5) 12 (7) Other 34 (6) 23 (6) 11 (6) Marital status, n (%) 0.29 Single 120 (21) 86 (21) 34 (20) Married 337 (58) 241 (59) 96 (55) Other 123 (21) 79 (19) 44 (25) Tumor size, n (%) 0.312 < 2cm 221 (38) 154 (38) 67 (39) < 4, ≥2 213 (37) 156 (38) 57 (33) ≥ 4cm 146 (25) 96 (24) 50 (29) Grade, n (%) 0.209 I 113 (19) 83 (20) 30 (17) II 266 (46) 183 (45) 83 (48) III 195 (34) 138 (34) 57 (33) IV 6 (1) 2 (0) 4 (2) SCSS, n (%) 0.16 Localized 282 (49) 201 (50) 81 (47) Regional 151 (26) 111 (27) 40 (23) Distant 147 (25) 94 (23) 53 (30) AJCC Stage, n (%) 0.5 I 257 (44) 183 (45) 74 (43) II 106 (18) 78 (19) 28 (16) III 57 (10) 36 (9) 21 (12) IV 160 (28) 109 (27) 51 (29) AJCC T, n (%) 0.204 T1-T2 387 (67) 278 (68) 109 (63) T3-T4 193 (33) 128 (32) 65 (37) AJCC N, n (%) 0.592 N0-N1 540 (93) 376 (93) 164 (94) N2-N3 40 (7) 30 (7) 10 (6) AJCC M, n (%) 0.136 M0 571 (98) 402 (99) 169 (97) M1 9 (2) 4 (1) 5 (3) Surgery, n (%) 0.79 No 98 (17) 67 (17) 31 (18) Yes 482 (83) 339 (83) 143 (82) lymphadenectomy, n (%) 0.956 No 509 (88) 357 (88) 152 (87) Yes 71 (12) 49 (12) 22 (13) Radiation, n (%) 0.196 No 345 (59) 234 (58) 111 (64) Yes 235 (41) 172 (42) 63 (36) Chemotherapy, n (%) 0.346 No 461 (79) 318 (78) 143 (82) Yes 119 (21) 88 (22) 31 (18) Systemic therapy, n (%) 0.775 No 498 (86) 347 (85) 151 (87) Yes 82 (14) 59 (15) 23 (13) Univariable Cox Regression Analysis Univariable Cox regression analysis identified the following variables with p-values < 0.10: tumor grade, AJCC stage, AJCC T stage, AJCC N stage, AJCC M stage, surgical intervention, radiotherapy, tumor size, race, age, marital status, and SCSS. Comprehensive results are presented in Table 2 . Table 2 Univariable Cox Regression Analysis Variables HR 95%CI P value Age < 60years Raf Raf Raf ≥ 60years 2.54 1.81–3.58 < 0.001 Sex Female Raf Raf Raf Male 0.95 0.7–1.28 0.724 Race White Raf Raf Raf Black 0.93 0.48–1.03 0.828 Other 1.82 1.81–3.2 0.038 Marital status Single Raf Raf Raf Married 0.84 0.58-1 0.328 Other 1.51 1.2–2.29 0.05 Tumor size < 2cm Raf Raf Raf < 4cm, ≥2cm 1.07 0.77–1.21 0.679 ≥ 4cm 1.73 1.49–2.46 0.002 Grade I Raf Raf Raf II 1.06 0.73–0.83 0.75 III 1.22 1.08–1.55 0.319 IV 4.47 1.8-18.55 0.039 SCSS Localized Raf Raf Raf Regional 1.24 0.89–1.25 0.207 Distant 1.75 1.75–2.45 0.001 AJCC Stage I Raf Raf Raf II 1.25 0.84–0.91 0.267 III 1.51 1.58–1.87 0.11 IV 2.2 2.5–3.06 < 0.001 AJCC T T1-T2 Raf Raf Raf T3-T4 1.72 1.29–2.29 < 0.001 AJCC N N0-N1 Raf Raf Raf N2-N3 2.65 1.7–4.13 < 0.001 AJCC M M0 Raf Raf Raf M1 2.64 0.84–8.26 0.096 Surgery No Raf Raf Raf Yes 0.53 0.38–0.74 < 0.001 Lymphadenectomy No Raf Raf Raf Yes 1.26 0.84–1.91 0.269 Radiation No Raf Raf Raf Yes 0.73 0.55–0.97 0.033 Chemotherapy No Raf Raf Raf Yes 1.26 0.91–1.74 0.171 Systemic therapy No Raf Raf Raf Yes 1.09 0.73–1.61 0.673 LASSO regression analysis The variable selection process of LASSO regression is shown in Fig. 2 . As illustrated in Fig. 2 A, as the penalty coefficient (λ) increases, the compression of coefficient estimates for independent variables intensifies, accompanied by a gradual reduction in the number of variables until they are compressed to zero. Figure 2 B displays the relationship between the number of independent variables and log(λ). The two dashed lines represent λ(min) and λ(1se), respectively. λ(min) corresponds to the optimal coefficient when the mean squared error is minimized, selecting 8 variables, while λ(1se) represents the best coefficient within one standard error of the mean squared error, selecting 1 variable. Considering the practicality of clinical prediction models, we ultimately adopted the 8 most potential variables selected by λ(min) for inclusion in the prediction model: Age, Race, Surgery, Radiation, AJCC N, AJCC Stage, Tumor size, and Marital status. Multivariable Cox Regression Analysis and Nomogram Construction The screened variables were incorporated into multivariate Cox regression analysis, which identified AJCC Stage, Radiation, and Age as independent factors influencing CSS in NCSCC patients. The first row of the nomogram displays scores corresponding to each variable, and the cumulative total score from the nomogram can predict 1-, 3-, and 5-year CSS for individual patients. See Fig. 3 for details. Validation and Clinical Utility Assessment of the Prediction Model The model was validated using the C-index and calibration curves. As shown in Fig. 4 , the C-indices for predicting 1-, 3-, and 5-year CSS with the nomogram were all > 0.70, indicating the strong discriminatory ability of the nomogram. Additionally, calibration curves were employed to assess model calibration (Fig. 5 ). In the training cohort, the calibration curve for 1-year CSS clustered around the ideal curve, while the 3- and 5-year calibration curves nearly overlapped with the ideal curve. When the predictive coefficients from the Cox regression analysis in the training set were applied to the validation set, the calibration curves for the validation cohort also aligned closely with the ideal curve, demonstrating excellent model consistency. To evaluate the clinical utility of the model, decision curve analysis (DCA) was performed (Fig. 6 ). The horizontal purple line represents the net benefit of no treatment, while the blue diagonal line denotes the net benefit of the treatment strategy. The results revealed that the new model provided a higher overall net benefit compared to the AJCC staging system, with a broader probability threshold range for survival. This suggests that the new model offers greater clinical utility and potential net benefit for patients. Discussion Malignancies of the head and neck region represent a heterogeneous group of neoplasms characterized by their complex oncological nature, originating from diverse anatomical subsites. Among these, nasal cavity malignancies constitute a distinct clinical entity that is relatively uncommon in otolaryngological practice, as evidenced by epidemiological studies 2 . The incidence of head and neck malignancies has shown a sustained upward trend in multiple countries in recent years 1 . However, to date, no studies have utilized multi-institutional data or large-scale national databases to statistically evaluate demographic characteristics and survival outcomes in NCSCC. Furthermore, most previously published studies have grouped nasal cavity malignancies with paranasal sinus malignancies, resulting in a lack of large-sample survival analyses specifically focused on nasal cavity malignancies 8 , 10 . While nasal cavity malignancies exhibit histologically heterogeneous subtypes, SCC remains the most prevalent histological type 11 , Therefore, leveraging the Surveillance, Epidemiology, and End Results (SEER) database, we aimed to develop a prognostic nomogram through comprehensive analysis of patients' demographic and clinicopathological characteristics, to provide actionable insights for clinicians and researchers in related fields. This study further benchmarked the novel model against the conventional AJCC staging system to ascertain its potential superiority in prognostic stratification. Multivariate Cox regression analysis revealed that among demographic characteristics, age—as anticipated—emerged as a significant independent prognostic factor for NCSCC 6 . In contrast, race, sex, and marital status did not demonstrate statistically significant prognostic value in this cohort. In this study, systemic therapy, chemotherapy, surgical intervention, and lymphadenectomy demonstrated no significant prognostic impact on NCSCC. Systemic therapy, primarily chemotherapy, serves as an alternative treatment modality, typically reserved for advanced-stage patients. It may be integrated with surgery and radiotherapy or used as a standalone approach in advanced disease 12 . Furthermore, chemotherapy alone showed no observable survival benefit in NCSCC. A retrospective analysis by Sarah Nicole Hamilton involving 159 NCSCC patients found no association between concurrent chemotherapy and local recurrence or overall survival (OS). However, the limited sample size of chemotherapy-treated patients (likely representing high-risk subgroups) challenges the generalizability of these conclusions 13 . The potential prognostic benefits of chemotherapy in NCSCC should not be categorically dismissed. In a retrospective analysis of 21 sinonasal malignancies by Chan-Young Ock et al., patients receiving neoadjuvant chemotherapy achieved significant T-stage downstaging and increased feasibility of eye-preserving salvage surgery 10 . Furthermore, a meta-analysis by Ruth S. Goh et al. demonstrated that induction chemotherapy combined with definitive treatment significantly improved OS (HR = 0.56, 95% CI [0.36–0.86], p = 0.009), suggesting that rational integration of chemotherapy into multimodal treatment strategies may confer survival benefits 14 . Lymphadenectomy demonstrated no significant prognostic impact on NCSCC in this study. This observation may be attributable to the early-stage predominance of nasal cavity malignancies, which exhibit low rates of cervical lymph node metastasis 3 , 7 . In a retrospective analysis of 39 NCSCC patients by Christoph Becker et al., only 1 of 310 dissected lymph nodes harbored metastatic disease 3 . Similarly, our cohort revealed that 93% of cases were classified as AJCC N0-N1. Although surgical resection remains the mainstay of treatment for nasal cavity malignancies 3 , 6 , 12 , no significant survival benefit was observed in this analysis. Radiotherapy emerged as an independent prognostic factor for NCSCC in this study. Early survival analyses of NCSCC suggested generally poorer outcomes with radiotherapy 15 . However, researchers at MD Anderson Cancer Center reported outcomes in a cohort of 68 nasal cavity carcinoma patients, where 32 (47%) underwent definitive radiotherapy. The 5-year local control rate reached 81.1%, with comparable outcomes between definitive radiotherapy and postoperative adjuvant radiotherapy (P = 0.10). No significant difference in 5-year survival was observed between postoperative radiotherapy and definitive radiotherapy alone 16 . These findings suggest that optimized radiotherapy techniques with precise dose delivery can achieve therapeutic equivalence to surgery-based multimodal approaches, particularly for early-stage NCSCC 14 . Although radiotherapy carries inherent toxicities, proton beam therapy has demonstrated reduced toxicity profiles without compromising oncologic outcomes 17 . Based on our findings and current evidence, we propose radiotherapy as the primary and essential treatment modality for NCSCC, while reserving surgery and chemotherapy for individualized adjuvant strategies based on patient-specific characteristics. In tumor staging, AJCC staging demonstrated significant prognostic relevance in NCSCC, primarily attributed to its incorporation of tumor invasion extent and distant metastasis status—critical prognostic determinants in squamous cell carcinoma 2 , 6 . Reported median overall survival (OS) for NCSCC with distant metastasis is 14.3 months 18 . Intriguingly, our study failed to identify distant metastasis as an independent prognostic factor, which seemingly contradicts previous findings and clinical consensus. Given the rarity of distant metastasis in nasal cavity squamous cell carcinoma 2 , 3 , we hypothesize this discrepancy may reflect insufficient statistical power from the limited subsample size (n = 4, 1% of the training cohort). Additionally, tumor differentiation grade showed no prognostic significance in our analysis, aligning with prior studies reporting no robust association between histological differentiation and survival outcomes in NCSCC 13 . Multivariable analysis revealed a non-significant trend toward worse prognosis with increasing tumor size (P > 0.05), consistent with existing literature 13 . Following the construction of the nomogram and consideration of identified prognostic factors, we conducted a comprehensive evaluation of the model, which is essential for validating any clinical prediction model prior to real-world implementation. Overall, the model demonstrated a C-index > 0.7, indicating robust discriminatory ability. Furthermore, calibration curves confirmed excellent agreement between predicted and observed outcomes. An increasing number of researchers now employ DCA to assess the net clinical benefit of therapeutic interventions. Our results revealed that the novel model provided superior overall net benefit compared to the AJCC staging system across a broader range of probability thresholds for survival, suggesting its enhanced clinical utility in optimizing patient management and facilitating better clinical decision-making. This study has several limitations. First, the retrospective design utilizing data from the SEER database may introduce information bias due to inherent constraints in data completeness and documentation variability. Second, the prognostic model did not incorporate critical biological markers (e.g., HPV status, molecular profiles) or behavioral factors (e.g., smoking, alcohol consumption), necessitating future prospective cohort studies to refine predictive accuracy. Third, while internal validation demonstrated robust performance, external validation across independent multicenter cohorts remains essential to confirm generalizability and clinical applicability. In conclusion, we developed the first nomogram for predicting 1-, 3-, and 5-year CSS in NCSCC patients using a large retrospective cohort. This tool integrates key demographic and clinicopathological variables with rigorous validation, confirming its discriminative power (C-index > 0.7) and clinical utility (superior net benefit vs. AJCC staging). The model provides clinicians with a practical and intuitive framework for personalized risk stratification and therapeutic decision-making. Future efforts will focus on external validation and the incorporation of emerging biomarkers to enhance prognostic precision. Declarations Acknowledgments We express our sincere gratitude to Ms. Hongxiang Qu, M.S., from the School of Mathematics and Statistics, Hunan Normal University, for her expert guidance and valuable advice on the statistical analysis in this study. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author Contributions Qw Z, M S, and S Oy: Jointly completed data organization, manuscript drafting, and statistical data analysis. B L: Research supervision, paper review, and revision. All authors read and approved the final manuscript. Data Availability The datasets analyzed in this study are available in the SEER database repository: https://seer.cancer.gov/. Ethics approval The data utilized in this study were obtained from the publicly available SEER database and contained no personally identifiable information. Therefore, this research was exempt from ethics committee approval in accordance with regulations governing de-identified public datasets. Patient consent for publication Not applicable. References Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin , 71 (3), 209-249 (2021). Unsal AA, Dubal PM, Patel TD, et al. Squamous cell carcinoma of the nasal cavity: A population-based analysis. Laryngoscope , 126 (3), 560-565 (2016). Becker C, Kayser G, Pfeiffer J. Squamous cell cancer of the nasal cavity: New insights and implications for diagnosis and treatment. 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Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 24 Jun, 2025 Reviews received at journal 17 Jun, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviews received at journal 18 May, 2025 Reviewers agreed at journal 07 May, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers agreed at journal 14 Apr, 2025 Reviewers invited by journal 14 Apr, 2025 Editor assigned by journal 14 Apr, 2025 Editor invited by journal 07 Apr, 2025 Submission checks completed at journal 04 Apr, 2025 First submitted to journal 03 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6372710","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":439329259,"identity":"ba52ec2a-c395-431f-b16a-edd02f3f5c10","order_by":0,"name":"Qiwei Zeng","email":"","orcid":"","institution":"First Affiliated Hospital of Hunan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Qiwei","middleName":"","lastName":"Zeng","suffix":""},{"id":439329261,"identity":"e1a220a8-425e-447f-bb9a-81adbc565f76","order_by":1,"name":"Ming Sheng","email":"","orcid":"","institution":"First Affiliated Hospital of Hunan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Sheng","suffix":""},{"id":439329262,"identity":"5c2050c8-288f-4ef0-8373-04f14990ec12","order_by":2,"name":"Si Ouyang","email":"","orcid":"","institution":"First Affiliated Hospital of Hunan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Si","middleName":"","lastName":"Ouyang","suffix":""},{"id":439329263,"identity":"a4555ca0-4bf4-4b57-9f89-55debd3ece8c","order_by":3,"name":"Bin Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIie3RMUsDMRTA8RcCueXprTko9is8cXEo1o+So9BJy4HLTeWgcC79ACd+DME5RwaXoOuBy4lfIN06dGh0bcnVzSH/IVN+kJcHEIv9w845gNmSvBjz9LtXNME0qcJEcGAGiuury8e1oL6cj7K1HiAA3IArc7AWst6aCXW3AyRBMgVJxZp7TXn9gdABc5u70MM8aUguErlQfV5/InuuePb0GiYaST6wRhH9ED7Sgp+dQPKqU/6s31FINUzML7HWT2Q14jARRetn8Z9cz0mVM5TYroKzpKl5cW639Kvk5mtLN9Pp26p1mwA5Fqv+dj8Wi8ViB+0BXNdQi9C33A8AAAAASUVORK5CYII=","orcid":"","institution":"First Affiliated Hospital of Hunan Normal University","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-04-04 02:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6372710/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6372710/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-10355-w","type":"published","date":"2025-07-24T15:58:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80294928,"identity":"4ed7cc20-b44a-4fdb-b511-58412c9247d2","added_by":"auto","created_at":"2025-04-10 08:30:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":483981,"visible":true,"origin":"","legend":"\u003cp\u003ePatient Selection Flowchart\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6372710/v1/74f1ccba67912884465b949a.png"},{"id":80294922,"identity":"2e966c54-5a09-4347-bd42-c5d456c4f0d1","added_by":"auto","created_at":"2025-04-10 08:30:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":293317,"visible":true,"origin":"","legend":"\u003cp\u003eLASSO regression: Coefficient path plot of LASSO regression (A) and variable selection via 10-fold cross-validation (B)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6372710/v1/e7c02ff49dcd19e6b10f3a71.png"},{"id":80294924,"identity":"d9fef5fd-1ef4-47f4-8f49-5effb8689d3a","added_by":"auto","created_at":"2025-04-10 08:30:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":440097,"visible":true,"origin":"","legend":"\u003cp\u003eThe forest plot of multivariate Cox regression analysis (A) and the nomogram (B)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6372710/v1/64724635fa311139bb7076bf.png"},{"id":80294926,"identity":"90ba780d-73e4-42d3-baae-91fae913e05e","added_by":"auto","created_at":"2025-04-10 08:30:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":307886,"visible":true,"origin":"","legend":"\u003cp\u003eC-index of the prediction model: training cohort (A) and validation cohort (B)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6372710/v1/0fdd9aa59d3587cf9391f20c.png"},{"id":80296978,"identity":"9a27718e-70f9-4775-9ba5-510b07e52259","added_by":"auto","created_at":"2025-04-10 08:38:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":354744,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves for predicting 1- (A), 3- (B), and 5-year (C) CSS in the training cohort; calibration curves for predicting 1- (D), 3- (E), and 5-year (F) CSS in the validation cohort\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6372710/v1/6d42785e80b6d64710e4a1e9.png"},{"id":80296972,"identity":"28bc019c-dc48-4e20-b2ca-698a58e9ce3b","added_by":"auto","created_at":"2025-04-10 08:38:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":142193,"visible":true,"origin":"","legend":"\u003cp\u003eDCA curves for the nomogram prediction model and AJCC staging: median CSS (A), 1-year CSS (B), 3-year CSS (C), and 5-year CSS (D)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6372710/v1/ea2a7ae29b86256ff42510d9.png"},{"id":87756770,"identity":"66eb84f1-24ad-4acc-a26b-33572209bde5","added_by":"auto","created_at":"2025-07-28 16:09:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2900461,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6372710/v1/2727e30e-7fb5-4775-b7ec-9f125e56b56f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Prognostic Model for Nasal cavity Squamous Cell Carcinoma Based on the SEER Database","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHead and neck squamous cell carcinoma (HNSCC) represents the seventh most prevalent malignancy worldwide, with an estimated annual incidence of 890,000 new cases, constituting 4.5% of global cancer diagnoses, and accounting for 450,000 deaths, which corresponds to 4.6% of total cancer-related mortality\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. This neoplasm exhibits a predilection for elderly male populations. Nasal cavity squamous cell carcinoma (NCSCC), a relatively uncommon subtype within the head and neck region, demonstrates an incidence rate of approximately 0.148 cases per 100,000 individuals \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Established etiological factors for NCSCC include tobacco smoking and human papillomavirus (HPV) infection, as evidenced by multiple studies\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The limited epidemiological data on nasal cavity malignancies, compounded by the historical classification of nasal cavity and sinonasal cancers as a unified entity\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, has resulted in a paucity of large-scale studies specifically examining survival outcomes in NCSCC. Malignancies of the nasal cavity demonstrate considerable histological diversity compared to other head and neck subsites. Nasal cavity malignancies encompass a diverse spectrum of histopathological subtypes, including but not limited to melanoma, adenocarcinoma, salivary gland-type carcinomas, olfactory neuroblastoma, and sinonasal undifferentiated carcinoma. However, epidemiological evidence consistently demonstrates that squamous cell carcinoma (SCC) represents the predominant histological variant, accounting for the majority of cases\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The distinct pathological subtypes exhibit significant variations in epidemiological prevalence, therapeutic approaches, and prognostic trajectories. By restricting analytical investigations to a specific pathological subtype, researchers can effectively minimize confounding variables, thereby enhancing the precision of study outcomes and improving their clinical applicability.\u003c/p\u003e \u003cp\u003eAs NCSCC represents the predominant histological subtype among nasal cavity malignancies, accounting for the majority of cases\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, this study aims to analyze cancer-specific survival (CSS) and prognostic factors in NCSCC patients using survival data from the SEER database. A predictive model will be developed to visualize clinical data, providing actionable insights for clinical decision-making.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eClinical Data\u003c/h2\u003e\n \u003cp\u003eIn this study, data on NCSCC patients were downloaded using SEER Stat 8.4.5 software from the database \u0026quot;Incidence - SEER Research Data, 17 Registries, Nov 2023 Sub (2000\u0026ndash;2021) - Linked To County Attributes - Time Dependent (1990\u0026ndash;2022) Income/Rurality, 1969\u0026ndash;2022 Counties, National Cancer Institute, DCCPS, Surveillance Research Program, released April 2024, based on the November 2023 submission.\u0026quot; The specific workflow is illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eVariable Selection\u003c/h3\u003e\n\u003cp\u003eThe study analyzed 16 demographic and clinical variables. Considering the predominantly middle-aged and elderly population of NCSCC patients, age stratification was established using the cohort\u0026apos;s median age of 60 years, dividing participants into non-elderly (\u0026lt;\u0026thinsp;60 years) and elderly (\u0026ge;\u0026thinsp;60 years) groups. Demographic variables included sex, race, and marital status. Clinical characteristics comprised tumor differentiation grade (well-, moderately-, poorly-, and undifferentiated), tumor size (categorized into three tiers at 2-cm intervals), surgical intervention, radiotherapy, chemotherapy, systemic therapy, AJCC TNM stage (6th edition), SEER combined stage (SCSS), T-stage (early [T1-T2] vs. advanced [T3-T4]), N-stage (localized [N0-N1] vs. extensive [N3-N4] nodal involvement), and M-stage. The dataset was randomly divided into training and validation cohorts in a 7:3 ratio.\u003c/p\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eStatistical analyses were performed using R software (version 4.43). Comparative analyses between the training and validation cohorts were conducted using chi-square tests. The development of the prognostic model in the training cohort followed a three-stage analytical approach: initially, potential predictors were screened through univariable Cox regression analysis, with variables demonstrating a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.10 proceeding to subsequent analysis. These selected variables were then subjected to LASSO regression for feature selection and dimensionality reduction. Finally, the variables retained from the LASSO regression were incorporated into a multivariable Cox proportional hazards model, which served as the foundation for constructing the nomogram and generating the corresponding forest plot. Model validation encompassed the assessment of discrimination capability through Harrell\u0026apos;s concordance index (C-index) and evaluation of calibration accuracy using bootstrap-corrected calibration curves (1,000 iterations). The clinical utility of the nomogram was compared against the AJCC staging system through decision curve analysis (DCA).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003ch3\u003eClinical and Demographic Characteristics\u003c/h3\u003e\n\u003cp\u003eThe demographic and clinical characteristics of 580 patients with NCSCC, encompassing age, sex, race, tumor grade, SEER combined stage, surgical intervention, radiotherapy, and chemotherapy status, are comprehensively detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, illustrating their respective distribution patterns. The P-values for all variables between the modeling group and validation group were greater than 0.05, indicating no significant difference between the two groups. CSS analysis demonstrated that the cohort exhibited survival probabilities of 89.1% at 1 year, 74.8% at 3 years, and 63.6% at 5 years.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinical and Demographic Characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003eVariables\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eTotal\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eTraining\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eValidation\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u003cem\u003eP\u003c/em\u003e\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e(n\u0026thinsp;=\u0026thinsp;580)\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e(n\u0026thinsp;=\u0026thinsp;406)\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e(n\u0026thinsp;=\u0026thinsp;174)\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAge, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.623\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;60years\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e207 (36)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e148 (36)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e59 (34)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026ge;\u0026thinsp;60years\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e373 (64)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e258 (64)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e115 (66)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSex, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.829\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eFemale\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e178 (31)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e123 (30)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e55 (32)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eMale\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e402 (69)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e283 (70)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e119 (68)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eRace, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.516\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eWhite\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e515 (89)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e364 (90)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e151 (87)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eBlack\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e31 (5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e19 (5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e12 (7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eOther\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e34 (6)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e23 (6)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e11 (6)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eMarital status, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.29\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSingle\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e120 (21)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e86 (21)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e34 (20)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eMarried\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e337 (58)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e241 (59)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e96 (55)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eOther\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e123 (21)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e79 (19)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e44 (25)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eTumor size, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.312\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;2cm\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e221 (38)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e154 (38)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e67 (39)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;4, \u0026ge;2\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e213 (37)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e156 (38)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e57 (33)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026ge;\u0026thinsp;4cm\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e146 (25)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e96 (24)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e50 (29)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eGrade, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.209\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eI\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e113 (19)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e83 (20)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e30 (17)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eII\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e266 (46)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e183 (45)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e83 (48)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eIII\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e195 (34)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e138 (34)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e57 (33)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eIV\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6 (1)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2 (0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4 (2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSCSS, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eLocalized\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e282 (49)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e201 (50)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e81 (47)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eRegional\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e151 (26)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e111 (27)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e40 (23)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eDistant\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e147 (25)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e94 (23)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e53 (30)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAJCC Stage, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.5\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eI\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e257 (44)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e183 (45)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e74 (43)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eII\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e106 (18)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e78 (19)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e28 (16)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eIII\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e57 (10)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e36 (9)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e21 (12)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eIV\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e160 (28)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e109 (27)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e51 (29)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAJCC T, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.204\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eT1-T2\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e387 (67)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e278 (68)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e109 (63)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eT3-T4\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e193 (33)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e128 (32)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e65 (37)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAJCC N, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.592\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eN0-N1\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e540 (93)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e376 (93)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e164 (94)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eN2-N3\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e40 (7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e30 (7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e10 (6)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAJCC M, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.136\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eM0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e571 (98)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e402 (99)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e169 (97)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eM1\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e9 (2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4 (1)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5 (3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSurgery, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.79\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNo\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e98 (17)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e67 (17)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e31 (18)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e482 (83)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e339 (83)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e143 (82)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003elymphadenectomy, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.956\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNo\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e509 (88)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e357 (88)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e152 (87)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e71 (12)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e49 (12)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e22 (13)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eRadiation, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.196\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNo\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e345 (59)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e234 (58)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e111 (64)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e235 (41)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e172 (42)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e63 (36)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eChemotherapy, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.346\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNo\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e461 (79)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e318 (78)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e143 (82)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e119 (21)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e88 (22)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e31 (18)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSystemic therapy, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.775\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNo\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e498 (86)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e347 (85)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e151 (87)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e82 (14)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e59 (15)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e23 (13)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eUnivariable Cox Regression Analysis\u003c/h2\u003e\n \u003cp\u003eUnivariable Cox regression analysis identified the following variables with \u003cem\u003ep-values\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10: tumor grade, AJCC stage, AJCC T stage, AJCC N stage, AJCC M stage, surgical intervention, radiotherapy, tumor size, race, age, marital status, and SCSS. Comprehensive results are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariable Cox Regression Analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eVariables\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u003cem\u003eHR\u003c/em\u003e\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u003cem\u003eP value\u003c/em\u003e\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAge\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;60years\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026ge;\u0026thinsp;60years\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.54\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.81\u0026ndash;3.58\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSex\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eFemale\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eMale\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.95\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.7\u0026ndash;1.28\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.724\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eRace\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eWhite\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eBlack\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.48\u0026ndash;1.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.828\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eOther\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.81\u0026ndash;3.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.038\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eMarital status\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSingle\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eMarried\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.84\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.58-1\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.328\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eOther\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.51\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.2\u0026ndash;2.29\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eTumor size\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;2cm\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;4cm, \u0026ge;2cm\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.07\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.77\u0026ndash;1.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.679\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026ge;\u0026thinsp;4cm\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.73\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.49\u0026ndash;2.46\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.002\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eGrade\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eI\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eII\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.73\u0026ndash;0.83\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.75\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eIII\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.22\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.08\u0026ndash;1.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.319\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eIV\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4.47\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.8-18.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.039\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSCSS\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eLocalized\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eRegional\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.24\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.89\u0026ndash;1.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.207\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eDistant\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.75\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.75\u0026ndash;2.45\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAJCC Stage\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eI\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eII\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.84\u0026ndash;0.91\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.267\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eIII\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.51\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.58\u0026ndash;1.87\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.11\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eIV\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.5\u0026ndash;3.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAJCC T\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eT1-T2\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eT3-T4\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.72\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.29\u0026ndash;2.29\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAJCC N\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eN0-N1\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eN2-N3\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.65\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.7\u0026ndash;4.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAJCC M\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eM0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eM1\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.84\u0026ndash;8.26\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.096\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSurgery\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNo\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.53\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.38\u0026ndash;0.74\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eLymphadenectomy\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNo\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.26\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.84\u0026ndash;1.91\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.269\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eRadiation\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNo\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.73\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.55\u0026ndash;0.97\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.033\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eChemotherapy\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNo\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.26\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.91\u0026ndash;1.74\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.171\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSystemic therapy\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNo\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRaf\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.73\u0026ndash;1.61\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.673\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eLASSO regression analysis\u003c/h3\u003e\n\u003cp\u003eThe variable selection process of LASSO regression is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. As illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA, as the penalty coefficient (\u0026lambda;) increases, the compression of coefficient estimates for independent variables intensifies, accompanied by a gradual reduction in the number of variables until they are compressed to zero. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB displays the relationship between the number of independent variables and log(\u0026lambda;). The two dashed lines represent \u0026lambda;(min) and \u0026lambda;(1se), respectively. \u0026lambda;(min) corresponds to the optimal coefficient when the mean squared error is minimized, selecting 8 variables, while \u0026lambda;(1se) represents the best coefficient within one standard error of the mean squared error, selecting 1 variable. Considering the practicality of clinical prediction models, we ultimately adopted the 8 most potential variables selected by \u0026lambda;(min) for inclusion in the prediction model: Age, Race, Surgery, Radiation, AJCC N, AJCC Stage, Tumor size, and Marital status.\u003c/p\u003e\n\u003ch3\u003eMultivariable Cox Regression Analysis and Nomogram Construction\u003c/h3\u003e\n\u003cp\u003eThe screened variables were incorporated into multivariate Cox regression analysis, which identified AJCC Stage, Radiation, and Age as independent factors influencing CSS in NCSCC patients. The first row of the nomogram displays scores corresponding to each variable, and the cumulative total score from the nomogram can predict 1-, 3-, and 5-year CSS for individual patients. See Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e for details.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eValidation and Clinical Utility Assessment of the Prediction Model\u003c/h2\u003e\n \u003cp\u003eThe model was validated using the C-index and calibration curves. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, the C-indices for predicting 1-, 3-, and 5-year CSS with the nomogram were all \u0026gt;\u0026thinsp;0.70, indicating the strong discriminatory ability of the nomogram. Additionally, calibration curves were employed to assess model calibration (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). In the training cohort, the calibration curve for 1-year CSS clustered around the ideal curve, while the 3- and 5-year calibration curves nearly overlapped with the ideal curve. When the predictive coefficients from the Cox regression analysis in the training set were applied to the validation set, the calibration curves for the validation cohort also aligned closely with the ideal curve, demonstrating excellent model consistency. To evaluate the clinical utility of the model, decision curve analysis (DCA) was performed (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The horizontal purple line represents the net benefit of no treatment, while the blue diagonal line denotes the net benefit of the treatment strategy. The results revealed that the new model provided a higher overall net benefit compared to the AJCC staging system, with a broader probability threshold range for survival. This suggests that the new model offers greater clinical utility and potential net benefit for patients.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMalignancies of the head and neck region represent a heterogeneous group of neoplasms characterized by their complex oncological nature, originating from diverse anatomical subsites. Among these, nasal cavity malignancies constitute a distinct clinical entity that is relatively uncommon in otolaryngological practice, as evidenced by epidemiological studies\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The incidence of head and neck malignancies has shown a sustained upward trend in multiple countries in recent years\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. However, to date, no studies have utilized multi-institutional data or large-scale national databases to statistically evaluate demographic characteristics and survival outcomes in NCSCC. Furthermore, most previously published studies have grouped nasal cavity malignancies with paranasal sinus malignancies, resulting in a lack of large-sample survival analyses specifically focused on nasal cavity malignancies\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. While nasal cavity malignancies exhibit histologically heterogeneous subtypes, SCC remains the most prevalent histological type\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, Therefore, leveraging the Surveillance, Epidemiology, and End Results (SEER) database, we aimed to develop a prognostic nomogram through comprehensive analysis of patients' demographic and clinicopathological characteristics, to provide actionable insights for clinicians and researchers in related fields. This study further benchmarked the novel model against the conventional AJCC staging system to ascertain its potential superiority in prognostic stratification.\u003c/p\u003e \u003cp\u003eMultivariate Cox regression analysis revealed that among demographic characteristics, age\u0026mdash;as anticipated\u0026mdash;emerged as a significant independent prognostic factor for NCSCC\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In contrast, race, sex, and marital status did not demonstrate statistically significant prognostic value in this cohort.\u003c/p\u003e \u003cp\u003eIn this study, systemic therapy, chemotherapy, surgical intervention, and lymphadenectomy demonstrated no significant prognostic impact on NCSCC. Systemic therapy, primarily chemotherapy, serves as an alternative treatment modality, typically reserved for advanced-stage patients. It may be integrated with surgery and radiotherapy or used as a standalone approach in advanced disease\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Furthermore, chemotherapy alone showed no observable survival benefit in NCSCC. A retrospective analysis by Sarah Nicole Hamilton involving 159 NCSCC patients found no association between concurrent chemotherapy and local recurrence or overall survival (OS). However, the limited sample size of chemotherapy-treated patients (likely representing high-risk subgroups) challenges the generalizability of these conclusions\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The potential prognostic benefits of chemotherapy in NCSCC should not be categorically dismissed. In a retrospective analysis of 21 sinonasal malignancies by Chan-Young Ock et al., patients receiving neoadjuvant chemotherapy achieved significant T-stage downstaging and increased feasibility of eye-preserving salvage surgery\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Furthermore, a meta-analysis by Ruth S. Goh et al. demonstrated that induction chemotherapy combined with definitive treatment significantly improved OS (HR\u0026thinsp;=\u0026thinsp;0.56, 95% CI [0.36\u0026ndash;0.86], p\u0026thinsp;=\u0026thinsp;0.009), suggesting that rational integration of chemotherapy into multimodal treatment strategies may confer survival benefits\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLymphadenectomy demonstrated no significant prognostic impact on NCSCC in this study. This observation may be attributable to the early-stage predominance of nasal cavity malignancies, which exhibit low rates of cervical lymph node metastasis\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In a retrospective analysis of 39 NCSCC patients by Christoph Becker et al., only 1 of 310 dissected lymph nodes harbored metastatic disease\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Similarly, our cohort revealed that 93% of cases were classified as AJCC N0-N1. Although surgical resection remains the mainstay of treatment for nasal cavity malignancies\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, no significant survival benefit was observed in this analysis.\u003c/p\u003e \u003cp\u003eRadiotherapy emerged as an independent prognostic factor for NCSCC in this study. Early survival analyses of NCSCC suggested generally poorer outcomes with radiotherapy\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, researchers at MD Anderson Cancer Center reported outcomes in a cohort of 68 nasal cavity carcinoma patients, where 32 (47%) underwent definitive radiotherapy. The 5-year local control rate reached 81.1%, with comparable outcomes between definitive radiotherapy and postoperative adjuvant radiotherapy (P\u0026thinsp;=\u0026thinsp;0.10). No significant difference in 5-year survival was observed between postoperative radiotherapy and definitive radiotherapy alone\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. These findings suggest that optimized radiotherapy techniques with precise dose delivery can achieve therapeutic equivalence to surgery-based multimodal approaches, particularly for early-stage NCSCC\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Although radiotherapy carries inherent toxicities, proton beam therapy has demonstrated reduced toxicity profiles without compromising oncologic outcomes\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Based on our findings and current evidence, we propose radiotherapy as the primary and essential treatment modality for NCSCC, while reserving surgery and chemotherapy for individualized adjuvant strategies based on patient-specific characteristics.\u003c/p\u003e \u003cp\u003eIn tumor staging, AJCC staging demonstrated significant prognostic relevance in NCSCC, primarily attributed to its incorporation of tumor invasion extent and distant metastasis status\u0026mdash;critical prognostic determinants in squamous cell carcinoma\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Reported median overall survival (OS) for NCSCC with distant metastasis is 14.3 months\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Intriguingly, our study failed to identify distant metastasis as an independent prognostic factor, which seemingly contradicts previous findings and clinical consensus. Given the rarity of distant metastasis in nasal cavity squamous cell carcinoma\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, we hypothesize this discrepancy may reflect insufficient statistical power from the limited subsample size (n\u0026thinsp;=\u0026thinsp;4, 1% of the training cohort). Additionally, tumor differentiation grade showed no prognostic significance in our analysis, aligning with prior studies reporting no robust association between histological differentiation and survival outcomes in NCSCC\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Multivariable analysis revealed a non-significant trend toward worse prognosis with increasing tumor size (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), consistent with existing literature \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFollowing the construction of the nomogram and consideration of identified prognostic factors, we conducted a comprehensive evaluation of the model, which is essential for validating any clinical prediction model prior to real-world implementation. Overall, the model demonstrated a C-index\u0026thinsp;\u0026gt;\u0026thinsp;0.7, indicating robust discriminatory ability. Furthermore, calibration curves confirmed excellent agreement between predicted and observed outcomes. An increasing number of researchers now employ DCA to assess the net clinical benefit of therapeutic interventions. Our results revealed that the novel model provided superior overall net benefit compared to the AJCC staging system across a broader range of probability thresholds for survival, suggesting its enhanced clinical utility in optimizing patient management and facilitating better clinical decision-making.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the retrospective design utilizing data from the SEER database may introduce information bias due to inherent constraints in data completeness and documentation variability. Second, the prognostic model did not incorporate critical biological markers (e.g., HPV status, molecular profiles) or behavioral factors (e.g., smoking, alcohol consumption), necessitating future prospective cohort studies to refine predictive accuracy. Third, while internal validation demonstrated robust performance, external validation across independent multicenter cohorts remains essential to confirm generalizability and clinical applicability.\u003c/p\u003e \u003cp\u003eIn conclusion, we developed the first nomogram for predicting 1-, 3-, and 5-year CSS in NCSCC patients using a large retrospective cohort. This tool integrates key demographic and clinicopathological variables with rigorous validation, confirming its discriminative power (C-index\u0026thinsp;\u0026gt;\u0026thinsp;0.7) and clinical utility (superior net benefit vs. AJCC staging). The model provides clinicians with a practical and intuitive framework for personalized risk stratification and therapeutic decision-making. Future efforts will focus on external validation and the incorporation of emerging biomarkers to enhance prognostic precision.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e We express our sincere gratitude to Ms. Hongxiang Qu, M.S., from the School of Mathematics and Statistics, Hunan Normal University, for her expert guidance and valuable advice on the statistical analysis in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQw Z, M S, and S Oy: Jointly completed data organization, manuscript drafting, and statistical data analysis. B L: Research supervision, paper review, and revision. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in this study are available in the SEER database repository: https://seer.cancer.gov/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data utilized in this study were obtained from the publicly available SEER database and contained no personally identifiable information. Therefore, this research was exempt from ethics committee approval in accordance with regulations governing de-identified public datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e, \u003cstrong\u003e71\u003c/strong\u003e(3), 209-249 (2021).\u003c/li\u003e\n\u003cli\u003eUnsal AA, Dubal PM, Patel TD, et al. Squamous cell carcinoma of the nasal cavity: A population-based analysis. \u003cem\u003eLaryngoscope\u003c/em\u003e, \u003cstrong\u003e126\u003c/strong\u003e(3), 560-565 (2016).\u003c/li\u003e\n\u003cli\u003eBecker C, Kayser G, Pfeiffer J. Squamous cell cancer of the nasal cavity: New insights and implications for diagnosis and treatment. \u003cem\u003eHead Neck\u003c/em\u003e, \u003cstrong\u003e38 Suppl 1\u003c/strong\u003e, E2112-2117 (2016).\u003c/li\u003e\n\u003cli\u003eChowdhury N, Alvi S, Kimura K, et al. Outcomes of HPV-related nasal squamous cell carcinoma. \u003cem\u003eLaryngoscope\u003c/em\u003e, \u003cstrong\u003e127\u003c/strong\u003e(7), 1600-1603 (2017).\u003c/li\u003e\n\u003cli\u003eScurry WC, Jr., Goldenberg D, Chee MY, Lengerich EJ, Liu Y, Fedok FG. Regional recurrence of squamous cell carcinoma of the nasal cavity: a systematic review and meta-analysis. \u003cem\u003eArch Otolaryngol Head Neck Surg\u003c/em\u003e, \u003cstrong\u003e133\u003c/strong\u003e(8), 796-800 (2007).\u003c/li\u003e\n\u003cli\u003eBhattacharyya N. 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A clinicopathological study of masses of nasal cavity paranasal sinuses and nasopharynx. \u003cem\u003eInternational Journal of Otorhinolaryngology and Head and Neck Surgery\u003c/em\u003e, (2017).\u003c/li\u003e\n\u003cli\u003eOck CY, Keam B, Kim TM, et al. Induction chemotherapy in head and neck squamous cell carcinoma of the paranasal sinus and nasal cavity: a role in organ preservation. \u003cem\u003eKorean J Intern Med\u003c/em\u003e, \u003cstrong\u003e31\u003c/strong\u003e(3), 570-578 (2016).\u003c/li\u003e\n\u003cli\u003eKumari S, Pandey S, Verma M, Rana AK, Kumari S. Clinicopathological Challenges in Tumors of the Nasal Cavity and Paranasal Sinuses: Our Experience. \u003cem\u003eCureus\u003c/em\u003e, \u003cstrong\u003e14\u003c/strong\u003e(9), e29128 (2022).\u003c/li\u003e\n\u003cli\u003eJakimovska F, Stojkovski I, Kjosevska E. 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Definitive radiotherapy with or without chemotherapy for T3-4N0 squamous cell carcinoma of the maxillary sinus and nasal cavity. \u003cem\u003eJpn J Clin Oncol\u003c/em\u003e, \u003cstrong\u003e40\u003c/strong\u003e(6), 542-548 (2010).\u003c/li\u003e\n\u003cli\u003eAllen MW, Schwartz DL, Rana V, et al. Long-term radiotherapy outcomes for nasal cavity and septal cancers. \u003cem\u003eInt J Radiat Oncol Biol Phys\u003c/em\u003e, \u003cstrong\u003e71\u003c/strong\u003e(2), 401-406 (2008).\u003c/li\u003e\n\u003cli\u003eMimica X, Yu Y, McGill M, et al. Organ preservation for patients with anterior mucosal squamous cell carcinoma of the nasal cavity: Rhinectomy-free survival in those refusing surgery. \u003cem\u003eHead Neck\u003c/em\u003e, \u003cstrong\u003e41\u003c/strong\u003e(8), 2741-2747 (2019).\u003c/li\u003e\n\u003cli\u003eQian Y, Tang L, Yao J, et al. Pembrolizumab with chemotherapy for patients with recurrent or metastatic nasal cavity and paranasal sinus squamous cell carcinoma: A prospective phase ll study. \u003cem\u003eClin Cancer Res\u003c/em\u003e, (2025).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Nasal Cavity Squamous Cell Carcinoma, Prognostic Model, SEER Database, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-6372710/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6372710/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study sought to construct and validate a prognostic nomogram for predicting cause-specific survival (CSS) in patients with nasal cavity squamous cell carcinoma (NCSCC). We conducted a retrospective analysis of clinical data from NCSCC patients registered in the SEER database between 2007 and 2015. Statistical analyses were performed to assess CSS rates and identify prognostic factors. The study cohort comprised 580 NCSCC patients, with CSS probabilities of 89.1%, 74.8%, and 63.6% at 1, 3, and 5 years, respectively. Multivariate Cox regression analysis identified age, American Joint Committee on Cancer (AJCC) stage, and radiotherapy administration as independent prognostic factors significantly associated with CSS. The accuracy of the prediction was evaluated using the C-index and calibration curve. Decision curve analysis (DCA) and receiver operating characteristic (ROC) were utilized to compare the nomogram with the AJCC stage system in order to assess its superiority. We developed and validated a predictive model for 1-, 3-, and 5-year CSS in NCSCC based on a large retrospective cohort. The nomogram demonstrates clinical utility in guiding individualized treatment strategies and patient management.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Prognostic Model for Nasal cavity Squamous Cell Carcinoma Based on the SEER Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 08:30:48","doi":"10.21203/rs.3.rs-6372710/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-24T08:44:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-17T13:21:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17087606391814092660310284102517218879","date":"2025-06-02T06:52:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-18T16:03:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268825804451212469789396814565109385377","date":"2025-05-07T12:11:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317995955166195977999561165945511793938","date":"2025-04-23T12:38:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"293769371364264260402643667125950155081","date":"2025-04-14T11:21:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-14T11:00:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-14T10:59:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-07T07:25:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-04T11:00:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-04T02:17:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d1440a07-059b-4b42-8233-52ae8544eedd","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46768017,"name":"Biological sciences/Cancer/Cancer models"},{"id":46768019,"name":"Biological sciences/Cancer/Head and neck cancer"},{"id":46768021,"name":"Health sciences/Medical research"},{"id":46768022,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-07-28T16:03:56+00:00","versionOfRecord":{"articleIdentity":"rs-6372710","link":"https://doi.org/10.1038/s41598-025-10355-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-24 15:58:09","publishedOnDateReadable":"July 24th, 2025"},"versionCreatedAt":"2025-04-10 08:30:48","video":"","vorDoi":"10.1038/s41598-025-10355-w","vorDoiUrl":"https://doi.org/10.1038/s41598-025-10355-w","workflowStages":[]},"version":"v1","identity":"rs-6372710","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6372710","identity":"rs-6372710","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}