Advancements in Image‐Based Analyses for Morphology and Staging of Colon Cancer: A Comprehensive Review. - Ameyaw, Samuel Arthur; Afari, Derrick Adu; Boateng, John; Maida, Marcello (BioMed Research International, 2025)
Colon cancer remains a significant global health burden, accounting for approximately 10% of all cancer cases worldwide and ranking as the second leading cause of cancer‐related mortality. Despite advances in treatment, the 5‐year survival rate for late‐stage colorectal cancer remains as low as 14%, whereas early detection can improve survival to over 90%. This review explores recent advancements in image‐based analyses for the morphology and staging of colon cancer, focusing on key imaging modalities, including colonoscopy, computed tomography (CT), magnetic resonance imaging (MRI), endoscopic ultrasound (EUS), histopathological analysis, and the integration of artificial intelligence (AI) and machine learning (ML) algorithms. A systematic literature review was conducted using peer‐reviewed studies from databases such as PubMed, Scopus, and IEEE Xplore. Selection criteria included studies published within the past decade that evaluated imaging techniques for colon cancer detection, staging, and treatment planning. AI and ML applications in colon cancer imaging were also examined, with an emphasis on their diagnostic accuracy, staging precision, and impact on clinical decision‐making. Findings indicate that AI‐assisted imaging techniques enhance lesion detection sensitivity (88%–94%) and improve staging accuracy compared to conventional radiology methods. AI models have also demonstrated superior predictive capabilities in treatment response and prognosis, with deep learning–based algorithms achieving over 90% accuracy in 5‐year survival prediction. Despite these advancements, challenges persist, including interobserver variability, dataset biases, regulatory concerns, and the need for standardized AI validation protocols. Addressing these challenges requires interdisciplinary collaboration among clinicians, researchers, and policymakers to refine AI algorithms, develop standardized imaging protocols, and ensure equitable AI applications across diverse populations. By leveraging advancements in imaging and AI‐driven analysis, colon cancer diagnosis and management can be significantly improved, ultimately enhancing early detection rates, treatment personalization, and patient survival outcomes.