Nomogram Prediction Model and Prognostic Comparison of Cervical Clear Cell Carcinoma and Cervical Endometrioid Adenocarcinoma: A SEER Database Study

BACKGROUND: Cervical clear cell adenocarcinoma (CCAC) and cervical endometrioid adenocarcinoma (CEAC) are rare and aggressive non-HPV-associated malignancies. Despite their histological similarities, these subtypes demonstrate distinct biological behaviors, presenting challenges in treatment and prognosis. OBJECTIVE: To develop and validate a multivariable prognostic model for CCAC and CEAC, utilizing the SEER database to enhance clinical decision-making. METHODS: A total of 775 CEAC and 421 CCAC cases were analyzed using a multivariable nomogram. Patients were randomly allocated to model-development (n = 838) and validation (n = 358) cohorts in a 7:3 ratio. The model's performance was evaluated through AUC, Brier score, and Calibration. Decision Curve Analysis (DCA) and Clinical Impact Curve (CIC) were assessed in both development and internal validation cohorts. RESULTS: The model exhibited excellent calibration and discrimination in predicting overall survival (OS). In the development cohort, the 12- and 24-month prediction models had AUCs of 0.894 (95% CI: 0.860-0.928) and 0.857 (95% CI: 0.821-0.892), respectively. In the internal validation cohort, the 12- and 24-month models achieved AUCs of 0.814 (95% CI: 0.788-0.840) and 0.798 (95% CI: 0.775-0.822), respectively. The model effectively stratified patients into low-, intermediate-, and high-risk groups, with significantly different median survival times (p < 0.0001). DCA and CIC further validated the model's clinical utility. CONCLUSION: We developed a robust nomogram for quantifying OS risk in CCAC and CEAC patients. This model provides clinicians with a tool for identifying high-risk patients and implementing timely interventions.