Multiparametric Radiomics for Characterization and Outcome Prediction in Colorectal Cancer: The Central Role of Diagnostic Imaging

Background/Objectives: Colorectal carcinoma (CRC) remains a leading cause of cancer-related morbidity and mortality worldwide, with increasing incidence in younger populations. Despite advances in imaging, conventional approaches remain limited by subjective interpretation and insufficient characterization of tumor heterogeneity. Radiomics, particularly in a multiparametric framework integrating computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), has emerged as a promising tool to enhance diagnostic and prognostic performance. This review aims to critically evaluate methodological strategies for optimizing multiparametric radiomics in CRC. Methods: A narrative review was conducted based on a literature search of PubMed/MEDLINE, Scopus, and Web of Science up to March 2026. Studies focusing on CT-, MRI-, and PET-based radiomics in CRC were included. Key methodological aspects analyzed included imaging acquisition and standardization, tumor segmentation techniques, radiomic feature extraction, feature selection methods (e.g., LASSO and PCA), and model validation approaches. Results: Multiparametric radiomics models integrating CT, MRI, and PET consistently demonstrated superior diagnostic accuracy compared to single-modality approaches, particularly in T-staging and lymph node involvement prediction. PET-derived metabolic features further enhanced characterization of tumor biology and improved prognostic stratification, including prediction of progression-free survival (PFS) and overall survival (OS). However, methodological heterogeneity, small sample sizes, and variability in imaging protocols and segmentation practices remain significant limitations affecting reproducibility and generalizability. Conclusions: Multiparametric radiomics represents a powerful advancement in precision oncology for CRC, enabling improved tumor characterization, risk stratification, and personalized treatment planning. Standardization, multicentric validation, and integration with artificial intelligence are essential for successful clinical translation.