Predicting the recurrence risk of liver metastasis from colorectal cancer: a study based on preoperative CT intratumoral and peritumoral radiomics features

Objective This study aims to explore the value of predicting the recurrence risk of colorectal cancer liver metastasis (CRLM) based on preoperative CT intratumoral and peritumoral radiomics features. Methods This study utilized retrospectively collected preoperative CT data of 201 CRLM patients, comprising 145 cases from the hospital one and 56 cases from an external hospital two. Liver metastases were precisely segmented via manual annotation. Subsequently, the intratumoral region of interest (ROI Intra ) was isotropically dilated to radii of 2 mm, 4 mm, and 6 mm, resulting in peri-tumoral ROIs (ROI Peri2mm , ROI Peri4mm and ROI Peri6mm ). We established the prediction models based on support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) algorithms. The area under the subject operating characteristic curve (AUC) was used to evaluate the predictive performance. Results Compared with SVM and RF, MLP demonstrated superior predictive performance for estimating the recurrence risk of CRLM patients. The best radiomics signatures for predicting the recurrence risk of CRLM were ROI Intra+Peri4mm model, and the AUCs of the ROI Intra model, ROI Intra+Peri2mm model, ROI Intra+Peri4mm model, and ROI Intra+Peri6mm model constructed by MLP are 0.758 (95% confidence interval (CI), 0.621 - 0.865), 0.815 (95% CI, 0.684 - 0.908), 0.855 (95% CI, 0.731 - 0.936), and 0.825 (95% CI, 0.696 - 0.915), respectively, in the external test set. Conclusion Preoperative CT-based radiomics features extracted from intra-tumoral (ROI Intra ) and peritumoral (ROI Intra+Peri2mm , ROI Intra+Peri4mm , and ROI Intra+Peri6mm ) regions can effectively predict recurrence risk in CRLM patients.