Habitat Radiomics Based on MRI for Predicting Metachronous Liver Metastasis in Locally Advanced Rectal Cancer: a Two-center Study

Rationale and Objectives: This study aimed to explore the feasibility of using habitat radiomics based on magnetic resonance imaging (MRI) to predict metachronous liver metastasis (MLM) in locally advanced rectal cancer (LARC) patients. A nomogram was developed by integrating multiple factors to enhance predictive accuracy. Methods: Retrospective data from 385 LARC patients across two centers were gathered. The data from Center 1 were split into a training set of 203 patients and an internal validation set of 87 patients, while Center 2 provided an external test set of 95 patients. Kmeans clustering was used on T2-weighted images, and the region of interest was extended at different thicknesses. After feature extraction and selection, four machine-learning algorithms were utilized to build radiomics models. A nomogram was created by combining habitat radiomics, conventional radiomics, and clinical independent predictors. Model performance was evaluated by the AUC, and clinical utility was assessed through calibration curve and DCA. Results: Habitat radiomics outperformed other single models in predicting MLM, with AUCs of 0.926, 0.864, and 0.851 in respective sets. The integrated nomogram achieved even higher AUCs of 0.959, 0.925, and 0.889. DCA and calibration curve analysis showed its high net benefit and good calibration. Conclusion: MRI-based habitat radiomics can effectively predict MLM in LARC patients. The integrated nomogram has optimal predictive performance and improves model accuracy significantly.