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This review synthesizes recent progress in image-based analysis for colon cancer morphology and staging, covering modalities such as colonoscopy, CT, MRI, EUS, and histopathology, and the incorporation of AI/ML for detection, staging, and planning. A systematic literature review of peer-reviewed studies from databases including PubMed and Scopus evaluates performance, highlighting improved lesion sensitivity, staging precision, and predictive capabilities. Persistent issues include interobserver variability, dataset bias, regulatory concerns, and the need for standardized validation protocols.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/advancements-in-image-based-analyses-for-morphology-and-staging-of-colon-cancer-a-comprehensive-review/372678/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/advancements-in-image-based-analyses-for-morphology-and-staging-of-colon-cancer-a-comprehensive-review/372678.png","ImageObject",300,407,{"name":92,"@type":93},"Elsa","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-24","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Which imaging modalities are covered for colon cancer morphology and staging?","Question",{"text":112,"@type":113},"The review covers colonoscopy, computed tomography (CT), magnetic resonance imaging (MRI), endoscopic ultrasound (EUS), and histopathological analysis.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How do AI-assisted imaging techniques affect lesion detection and staging?",{"text":117,"@type":113},"AI-assisted methods improve lesion detection sensitivity and enhance staging accuracy compared with conventional radiology approaches.",{"name":119,"@type":110,"acceptedAnswer":120},"What challenges limit broader clinical deployment of AI in colon cancer imaging?",{"text":121,"@type":113},"Key challenges include interobserver variability, dataset biases, regulatory concerns, and the need for standardized AI validation protocols.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},372678,1790262512,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":26},137455077381,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","Wiley  \nBioMed Research International  \nVolume 2025, Article ID 9214337, 24 pages [https://doi.org/10.1155/bmri/9214337](https://doi.org/10.1155/bmri/9214337)  \nReview Article  \nAdvancements in Image-Based Analyses for Morphology and Staging of Colon Cancer: A Comprehensive Review  \nSamuel Arthur Ameyaw , 1 Derrick Adu Afari, 1 and John Boateng 2,3  \n1Department of Computer Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana  \n2Department of Clinical Microbiology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana 3Kumasi Centre for Collaborative Research, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana  \nCorrespondence should be addressed to John Boateng; [jboateng111@st.knust.edu.gh](jboateng111@st.knust.edu.gh)  \nReceived 18 June 2024; Revised 26 July 2025; Accepted 29 July 2025  \nAcademic Editor: Marcello Maida  \nCopyright © 2025 Samuel Arthur Ameyaw et al. BioMed Research International published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nColon 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 latestage 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 IEEEXplore. 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 AIassisted 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.  \n1. Introduction  \nColorectal cancer (CRC), which includes both colon and rectal cancer, is a leading cause of morbidity and mortality worldwide [1, 2] . It is the third most commonly diagnosed cancer and the second leading cause of cancer-related deaths globally, accounting for approximately 9.4% of all cancerrelated fatalities in 2020 [3, 4] . According to recent projections, the global incidence of CRC is expected to surpass 3.2 million new cases annually by 2040, with the highest  \nincreases occurring in low- and middle-income countries due to aging populati","cbCainxJGFjonkpg","https://ap.wps.com/l/cbCainxJGFjonkpg","pdf",1085362,24,"English","# Introduction\n## Image Modalities in Colon Cancer Assessment\n## AI and Machine Learning in Detection and Staging\n## Clinical Impact and Predictive Performance\n## Challenges and Future Directions","[{\"question\":\"Which imaging modalities are covered for colon cancer morphology and staging?\",\"answer\":\"The review covers colonoscopy, computed tomography (CT), magnetic resonance imaging (MRI), endoscopic ultrasound (EUS), and histopathological analysis.\"},{\"question\":\"How do AI-assisted imaging techniques affect lesion detection and staging?\",\"answer\":\"AI-assisted methods improve lesion detection sensitivity and enhance staging accuracy compared with conventional radiology approaches.\"},{\"question\":\"What challenges limit broader clinical deployment of AI in colon cancer imaging?\",\"answer\":\"Key challenges include interobserver variability, dataset biases, regulatory concerns, and the need for standardized AI validation protocols.\"}]","Advancements in Image-Based Analyses for Morphology and Staging of Colon Cancer: A Comprehensive Review | PDF",1790195103]