Impact of Radiomic and Artificial Intelligence on Colorectal Cancer: A Narrative Review

Abstract

Colorectal cancer (CRC) remains one of the leading causes of cancer-related morbidity and mortality worldwide, representing a major public health challenge. Despite advances in screening strategies, surgical techniques, and systemic therapies, patient prognosis is often compromised by late diagnosis, tumor heterogeneity, and therapeutic resistance. In recent years, the integration of advanced imaging analytics and artificial intelligence (AI) has opened new avenues for precision oncology. Radiomics, defined as the high-throughput extraction of quantitative features from medical images, has emerged as a promising tool to capture intratumoral heterogeneity and predict clinical outcomes in a non-invasive manner. When combined with AI, particularly machine learning and deep learning approaches, radiomics enables the development of predictive and prognostic models that may support treatment personalization. This narrative review provides a comprehensive overview of CRC epidemiology and risk factors, summarizes current diagnostic and clinical management strategies, and focuses extensively on radiomics and AI applications in CRC, including workflow standardization, feature extraction, clinical applications, and challenges for implementation in daily practice.

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