Objective.Colorectal liver metastases (CRLM) represent a major clinical challenge because outcomes after hepatic resection vary widely between patients. Preoperative risk stratification remains limited, and radiomics may provide non invasive imaging biomarkers to support prognosis. This study aimed to develop a CT based radiomics signature capable of generating risk scores for predicting overall survival (OS) in patients undergoing CRLM resection.Approach.Preoperative CT scans from 197 CRLM patients were retrospectively obtained from The Cancer Imaging Archive. A total of 851 radiomics features were extracted using 3D Slicer, along with 256 deep features from a 3D convolutional neural network. Data were randomly divided into training (70%) and testing (30%) sets. Feature selection included correlation-based filtering, univariate Cox regression, and multivariate Cox regression with least absolute shrinkage and selection operator (LASSO) regularization. A radiomics-based risk score (Rad score) was calculated to stratify patients into high- and low- risk groups using the median value. Model performance was compared with clinical variables using time-dependent receiver operating characteristic analysis.Main results.The eight feature signature was prognostic in both internal splits. In multivariable Cox models, the Rad score remained an independent predictor of OS in the training cohort (HR = 2.671, 95% CI 2.023-3.527,P-value < 0.001; Harrell C index = 0.738, 95% CI 0.678-0.793) and the testing cohort (HR = 3.036, 95% CI 1.421-6.486,P-value = 0.004; Harrell C index = 0.663, 95% CI 0.558-0.752). Kaplan Meier analysis showed shorter survival in the high risk group than the low risk group in training (median OS 56.5 versus 76.00 months; log rankP-value = 0.0000) and testing (61.45 versus 74.25 months; log rankP-value = 0.0103). In an external cohort of 105 patients, the Rad score also separated risk (Harrell C index = 0.614, 95% CI 0.534-0.697; median OS 15.7 versus 29.87 months; log rankP-value = 0.0015).Significance.A compact CT radiomics signature derived from preoperative imaging provided independent prognostic information for OS and enabled risk stratification in internal testing and external validation. Further validation in independent CRLM cohorts is required before clinical deployment.
Keywords: colorectal liver metastases; computed tomography; deep learning; overall survival prediction; radiomics; risk stratification.
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