A machine learning-based prognostic stratification of locoregional interventional therapies for patients with colorectal cancer liver metastases: a real-world study.

Abstract

Background: Colorectal cancer liver metastases (CRLM) represent a major cause of mortality in advanced colorectal cancer, with intra-arterial interventional therapy (IAIT) playing an increasingly important role in multidisciplinary management. This study aims to develop a machine learning (ML)-based prognostic model to predict survival outcomes in unresectable colorectal cancer liver metastases (uCRLM) patients undergoing IAIT treatment, enabling improved risk assessment.

Design: A retrospective study.

Objectives: This study aims to explore the effect of IAIT on the survival of patients with uCRLM.

Methods: Retrospective data were obtained from patients with CRLM who visited Luhe Hospital and Peking University Cancer Hospital from January 2018 to January 2023. The study population was divided into two groups: one group received IAIT sequence by systemic standard of care (SOC) therapy group (ISOC; n = 340), while the other group received systemic SOC therapy alone (n = 234). To reduce potential selection bias between the two groups, propensity score matching (PSM) was employed. The primary outcome measured was overall survival (OS). A prognostic model for IAIT was then constructed using five supervised ML models. The performance of the model was assessed by calculating the area under the receiver operating characteristic curve (AUC) and decision curve analysis. Kaplan-Meier analysis was used to reveal the OS risk stratification of the ML. To assess the prognostic nature of our models, we will include interaction terms between treatment modalities and key prognostic factors, followed by likelihood ratio tests to evaluate their significance.

Results: After PSM 1:1, 574 patients were divided into two groups. The median OS of patients who received ISOC was significantly higher than those who received systemic SOC therapy alone (40 vs 25 months, p = 0.036). Among the five ML models, the Random Survival Forest model demonstrated the most robust prognostic performance with 1-year, 2-year, and 3-year AUCs of 0.899 (95% confidence interval (CI): 0.858-0.939), 0.903 (95% CI: 0.864-0.943), and 0.873 (95% CI, 0.828-0.919). In the external validation cohort, the AUCs for 1, 2, and 3 years were 0.665 (95% CI: 0.455-0.875), 0.737 (95% CI: 0.636-0.837), and 0.730 (95% CI: 0.640-0.821), respectively. Kaplan-Meier curve analysis confirmed the model's prognostic power for the ISOC treatment strategy. We tested for interaction effects between treatment modalities (e.g., ISOC vs SOC) and the ML model's risk strata, but no significant interaction was observed (P for interaction p > 0.05).

Conclusion: In this study, ISOC significantly improved the prognosis of patients. The ML model provides accurate prognostic stratification for uCRLM patients, which may aid in risk-based clinical decision-making.