Novel machine learning algorithm can identify patients at risk of poor overall survival following curative resection for colorectal liver metastases.

ABSTRACT

BACKGROUND/PURPOSE: The primary cause of mortality in colorectal cancer is metastatic disease. We investigated the ability of a machine learning (ML) algorithm to stratify overall survival (OS) of patients undergoing curative resection for colorectal liver metastases (CRLM). METHODS: Patients undergoing curative liver resection for CRLM between 2010-2021 at the University Hospital RWTH Aachen were eligible for this retrospective study. Patients with recurrent metastases, incomplete resections, or early deaths, were excluded. A gradient-boosted decision tree (GBDT) model identified patients at risk of poor OS, based on clinicopathological characteristics. Differences in survival were compared with Kaplan-Meier analysis and the log-rank test. RESULTS: A total of 487 patients were split into training (n = 389, 80%) and test cohorts (n = 98, 20%). Of the latter, 20 (20%) were identified by the GBDT model as high-risk and showed significantly reduced OS (23 months vs 52 months, P = .005) and increased hazard ratio (2.434, 95%CI 1.280-4.627, P = .007). The strongest predictors were preoperative serum carcinoembryonic antigen (CEA), age, diameter of the largest metastasis, number of metastases, body mass index, and primary tumor grading. CONCLUSION: A GBDT model can identify high-risk patients regarding OS after curative resection of CRLM. Closer follow-up and aggressive systemic treatment strategies may be beneficial to these patients.