CT-based radiomics improves survival prediction in colorectal liver metastases: beyond clinical scores.

Surgery with perioperative chemotherapy offers a potentially curative treatment for colorectal liver metastases (CRLM). Selection of candidates for resection relies on survival prediction, but available prognostic factors have limited reliability. This study evaluated the potential of preoperative CT-based radiomics to predict overall survival, focusing on the impact of the CT-surgery interval and peritumoral tissue analysis. All consecutive patients undergoing resection for CRLM (2010-2020) with contrast-enhanced CT performed ≤ 60 days before surgery and at least one CRLM ≥ 10 mm were considered. Manual tumor segmentation (Tumor-VOI) and automatic 5-mm peritumoral expansion (Margin-VOI) were performed on portal phase images. From each VOI, 110 IBSI-compliant radiomic features were extracted. Three prediction models were developed: Clinical, Clinical+Tumor-radiomics, Clinical+Tumor/Margin-radiomics. Features selection was performed using Boruta algorithm, followed by Random Forest classification with 10-fold cross-validation. Model performance was evaluated in the entire cohort and in patients with CT-surgery interval ≤ 30 days. 306 patients were included (mean age 63 years; 187 men). Five-year survival was 40.9% (mean follow-up 34 months). At internal validation, the clinical model achieved C-index = 0.629. Radiomics provided modest improvement in the entire cohort, with greater impact in the 212 patients with a CT-surgery interval ≤ 30 days: the Clinical+Tumor-radiomics model reached C-index = 0.691, increasing to 0.717 with Margin-VOI features. Clinical-radiomic models outperformed established scores (Fong, GAME, RAS-mutation clinical scores; C-indices range = 0.502-0.593). Radiomic features of CRLM and peritumoral tissue extracted from preoperative CT improve survival prediction beyond conventional clinical scores. A CT-surgery interval of ≤ 30 days appears essential to optimize model performance.