Construction and validation of a prognostic nomogram for predicting cancer-specific survival in patients with intermediate and advanced colon cancer after receiving surgery and chemotherapy

• Published in: Journal of cancer research and clinical oncology 2023-11, Vol.149 (14), p.12821-12834
• Main Authors: Shi, Yiheng, Wu, Xiaoting, Qu, Wanxi, Tian, Jiahao, Pang, Xunlei, Fan, Haohan, Fei, Sujuan, Miao, Bei
• Format: Article
• Language: English
• Subjects: Calibration; Cancer Research; Chemotherapy; Colon cancer; Colorectal cancer; colorectal neoplasms; death; drug therapy; Epidemiology; Hematology; Internal Medicine; Medicine; Medicine & Public Health; model validation; monitoring; nomogram; Nomograms; Oncology; Patients; prediction; Prediction models; risk; Risk factors; Surgery; Survival; survival rate
• ISSN: 0171-5216; 1432-1335
• DOI: 10.1007/s00432-023-05154-7

Background Existing predictive models often focus solely on overall survival (OS), neglecting the bias that other causes of death might introduce into survival rate predictions. To date, there is no strict predictive model established for cancer-specific survival (CSS) in patients with intermediate and advanced colon cancer after receiving surgery and chemotherapy. Methods We extracted the data from the Surveillance, Epidemiology, and End Results (SEER) database on patients with stage-III and -IV colon cancer treated with surgery and chemotherapy between 2010 and 2015. The cancer-specific survival (CSS) was assessed using a competitive risk model, and the associated risk factors were identified via univariate and multivariate analyses. A nomogram predicting 1-, 3-, and 5-year CSS was constructed. The c-index, area under the curve (AUC), and calibration curve were adopted to assess the predictive performance of the model. Additionally, the model was externally validated. Results A total of 18 risk factors were identified by univariate and multivariate analyses for constructing the nomogram. The AUC values of the nomogram for the 1-, 3-, and 5-year CSS prediction were 0.831, 0.842, and 0.848 in the training set; 0.842, 0.853, and 0.849 in the internal validation set; and 0.815, 0.823, and 0.839 in the external validation set. The C-index were 0.826 (se: 0.001), 0.836 (se: 0.002) and 0.763 (se: 0.013), respectively. Moreover, the calibration curve showed great calibration. Conclusion The model we have constructed is of great accuracy and reliability, and can help physicians develop treatment and follow-up strategies that are beneficial to the survival of the patients.