Novel machine learning algorithm can identify patients at risk of poor overall survival following curative resection for colorectal liver metastases
Iakovos Amygdalos, Gustav Müller‐Franzes, Jan Bednarsch, Zoltán Czigány, Tom Florian Ulmer, Philipp Bruners, Christiane Kühl, Ulf P. Neumann, Daniel Truhn, Sven Arke Lang
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.