Prediction of cancer-specific survival in elderly patients with colorectal cancer after surgery based on competing risk model and random survival forest model
Authors: Zhang Qian , Xu Rongxuan , Zhen Wenchong , Bai Xueting , Li Zihan , Zhang Yixin , Wu Wei , Yao Zhihan , Li Xiaofeng 
Published: November 2024

Background
The poorer prognosis in older patients with colorectal cancer (CRC) after surgery may be related to the cancer or treatment, or death from other causes. Competing risk events have an impact on the results of the survival studies. But many survival studies do not apply competing risk analysis. We analyzed the prognosis of elderly patients with CRC after surgery, taking into account the competing risk events.
Methods
Older patients with CRC between 2010 and 2015 were selected from the Surveillance, Epidemiology and End Results (SEER) database. The competing risk model and RSF model were used to predict CSS in elderly patients with CRC after surgery. Predictors included age, sex, race, marital status, grade, TNM stage, T stage, N stage, histological type, primary site, carcinoembryonic antigen (CEA), perineural invasion, tumor deposits, tumor size, chemotherapy and radiotherapy. The effectiveness of the competing risk model and RSF model was evaluated from three aspects: accuracy, calibration and clinical benefit. In addition, we also visualized the competing risk model with a nomogram and compared it with the nomogram of the Cox model.
Results
In the competing risk analysis, the results of univariate and multivariate analysis showed that age, race, marital status, grade, TNM stage, T stage, N stage, CEA, perineural invasion, tumor deposits, and chemotherapy were the influencing factors for postoperative CSS in elderly patients with CRC. In the construction of the RSF model, the importance of variables showed that TNM stage of tumor had significant contribution, followed by N stage, T stage, chemotherapy and tumor deposits.
The 1-year, 3-year and 5-year concordance index (C-index), receiver operating characteristic curve (ROC) and area under curve (AUC) of the competing risk model and the RSF model show that the models have good predictive power. The 1-year, 3-year and 5-year C-index was 0.818, 0.813 and 0.798 in the train set, and 0.824, 0.813 and 0.799 in the test set of the competing risk model. The 1-year,3-year and 5-year AUC was 0.833 (95%CI 0.822-0.844), 0.844 (95%CI 0.836,0.852) and 0.832 (95%CI 0.824,0.839) in the train set, and 0.840 (95%CI 0.824,0.856), 0.842 (95%CI 0.830,0.854) and 0.832 (95%CI 0.821,0.844) in the test set of the competing risk model. The 1-year, 3-year, 5-year C-index was 0.942, 0.913 and 0.897 in the train set, and 0.835, 0.840 and 0.815 in the test set of the RSF model. The 1-year, 3-year and 5-year AUC was 0.860(95%CI 0.850,870),0.885(95%CI 0.878,0.891) and 0.882(95%CI 0.876,0.888) in the train set and 0.842(95%CI 0.826,0.859),0.854(95%CI 0.842,0.866) and 0.847(95%CI 0.835,0.858) in the test set of the RSF model. The C-index was 0.825,0.820 and 0.805 in the train set and 0.830,0.819 and 0.805 in the test set of the traditional Cox model for 1,3,5 years. The traditional Cox model overestimates the patient's mortality compared with the competing risk model. The C-index of the RSF model is higher than the competing risk model and the traditional Cox model. The calibration curve showed that the prediction results of CSS in elderly patients with CRC were in good agreement with the actual results. Decision curve analysis (DCA) showed that both models could lead to higher clinical benefits for patients.
Conclusions
Based on the SEER database, the competing risk model and the RSF model were used to predict CSS after CRC surgery in elderly patients, and both models performed well. In order to help clinicians make clinical decisions. It is hoped that it can help clinicians to screen high-risk elderly patients and guide the clinical decision making of postoperative prognosis.