A Prediction Model for Metachronous Peritoneal Carcinomatosis in Patients with Stage T4 Colon Cancer after Curative Resection

Abstract

Simple Summary Metachronous peritoneal carcinomatosis (mPC) has a significantly worse overall survival than other metastases in colorectal cancer. Exploring the peritoneal cavity is the only effective way to detect early-stage mPC lesions for curative treatment. The operation cannot be justified in all patients but is possible in patients with higher mPC risk. pT1–3 have a significantly lower risk of developing mPC than pT4. Therefore, we focused on analyzing patients with pT4 colon cancer without distant metastasis, developed a prediction model and used data-driven analysis to select patients with a high risk of developing mPC. Abstract (1) Background: The aim of this study was to develop a prediction model for assessing individual mPC risk in patients with pT4 colon cancer. Methods: A total of 2003 patients with pT4 colon cancer undergoing R0 resection were categorized into the training or testing set. Based on the training set, 2044 Cox prediction models were developed. Next, models with the maximal C-index and minimal prediction error were selected. The final model was then validated based on the testing set using a time-dependent area under the curve and Brier score, and a scoring system was developed. Patients were stratified into the high- or low-risk group by their risk score, with the cut-off points determined by a classification and regression tree (CART). (2) Results: The five candidate predictors were tumor location, preoperative carcinoembryonic antigen value, histologic type, T stage and nodal stage. Based on the CART, patients were categorized into the low-risk or high-risk groups. The model has high predictive accuracy (prediction error ≤5%) and good discrimination ability (area under the curve >0.7). (3) Conclusions: The prediction model quantifies individual risk and is feasible for selecting patients with pT4 colon cancer who are at high risk of developing mPC.