Nomograms for Predicting Overall and Cancer-Specific Survival Among Second Primary Endometrial Cancer in Primary Colorectal Carcinoma Patients

用于预测原发性结直肠癌患者继发性子宫内膜癌总体生存率和癌症特异性生存率的列线图

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Abstract

BACKGROUND: Endometrial cancer (EC) is one of the most frequent gynecologic cancers, approximately 20% of patients are regarded as high-risk with poor prognosis. However, more details of patients with second primary endometrial cancer (SPEC) after colorectal cancer (CRC) remain poorly understood. We therefore proposed to construct two nomograms to predict 3- and 5-year overall survival (OS) and cancer-specific survival (CSS) rates to facilitate clinical application. METHODS: A total of 1631 participants were identified in the SEER database from 1973 to 2020. We constructed and validated the nomograms for predicting OS and CSS. The receiver operating characteristic curves, calibration plot, decision curve analysis, C-index, net reclassification improvement, and integrated discrimination improvement were applied to evaluate the predictive performance. Finally, the Prognostic index was calculated and used for risk stratification of Kaplan-Meier survival analysis based on different treatment options. RESULTS: Nomograms of OS and CSS were formulated based on the independent prognostic factors utilizing the training set. The 3- and 5- years of OS nomogram demonstrated good discrimination (AUC = 0.840 and 0.829, respectively), well-calibrated power, and excellent clinical effectiveness. Our nomograms of predicting OS and CSS had a concordance index of 0.801 and 0.866 compared with 0.676 and 0.746 for the AJCC staging system, and more importantly, demonstrated a better forecast accuracy. Chemoradiotherapy displayed a significant survival benefit in the high-risk groups, but proceeding to surgery plus chemotherapy showed a favorable survival for the low groups based on all patients. CONCLUSION: We developed and internally validated multivariable models that predict OS and CSS risk of SPEC in patients with a CRC to help clinicians make applicable clinical decisions for patients.

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