Abstract
BACKGROUND: Colorectal cancer liver metastasis (CRLM) is a significant contributor to cancer-related illness and death. Neoadjuvant chemotherapy (NAC) is an essential treatment approach; however, optimal patient selection remains a challenge. This study aimed to develop a machine learning-based predictive model using hematological biomarkers to assess the efficacy of NAC in patients with CRLM. METHODS: We retrospectively analyzed the clinical data of 214 CRLM patients treated with the XELOX regimen. Blood characteristics before and after NAC, as well as the ratios of these biomarkers, were integrated into the machine learning models. Logistic regression, decision trees (DTs), random forest (RF), support vector machine (SVM), and AdaBoost were used for predictive modeling. The performance of the models was evaluated using the AUROC, F1-score, and external validation. RESULTS: The DT (AUROC: 0.915, F1-score: 0.621) and RF (AUROC: 0.999, F1-score: 0.857) models demonstrated the best predictive performance in the training cohort. The model incorporating the ratio of post-treatment to pre-treatment gamma-glutamyl transferase (rGGT) and carcinoembryonic antigen (rCEA) formed the GCR index, which achieved an AUROC of 0.853 in the external validation. The GCR index showed strong clinical relevance, predicting better chemotherapy responses in patients with lower rCEA and higher rGGT levels. CONCLUSIONS: The GCR index serves as a predictive biomarker for the efficacy of NAC in CRLM, providing a valuable clinical reference for the prognostic assessment of these patients.