Salman A, Elewa A, Salman MA. Artificial intelligence in colorectal liver metastases: From detection and resectability to treatment response and recurrence prediction. Artif Intell Cancer 2026; 7(1): 124432 [DOI: 10.35713/aic.124432]
Corresponding Author of This Article
Ahmed Salman, FRACP, FRCP, Internal Medicine, Kasr Alainy School of Medicine, 1 Al-Saray Street, Al-Manial, Cairo 11562, Al Qāhirah, Egypt. awea844@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
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review-article
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Salman A, Elewa A, Salman MA. Artificial intelligence in colorectal liver metastases: From detection and resectability to treatment response and recurrence prediction. Artif Intell Cancer 2026; 7(1): 124432 [DOI: 10.35713/aic.124432]
Artificial intelligence in colorectal liver metastases: From detection and resectability to treatment response and recurrence prediction
Ahmed Salman, Ahmed Elewa, Mohamed AbdAlla Salman
Ahmed Salman, Internal Medicine, Kasr Alainy School of Medicine, Cairo 11562, Al Qāhirah, Egypt
Ahmed Elewa, General Surgery, National Hepatology and Tropical Medicine Liver Institute, Cairo 16A, Egypt
Mohamed AbdAlla Salman, General Surgery, Kasralainy School of Medicine, Cairo 11562, Egypt
Author contributions: Salman A contributed to the study conception, manuscript drafting, and critical revision; Elewa A contributed to manuscript revision and final approval of the manuscript; Salman MA drafted the final version of the manuscript. All authors have read and approved the final manuscript.
AI contribution statement: AI-based tools were used solely for language polishing and formatting assistance during manuscript preparation. No AI tool was used to generate research data, interpret results, formulate conclusions, or produce or select references. All AI-assisted content was critically reviewed and revised by the authors, who take full responsibility for the accuracy, originality, and integrity of the manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Ahmed Salman, FRACP, FRCP, Internal Medicine, Kasr Alainy School of Medicine, 1 Al-Saray Street, Al-Manial, Cairo 11562, Al Qāhirah, Egypt. awea844@gmail.com
Received: June 15, 2026 Revised: July 23, 2026 Accepted: July 30, 2026 Published online: September 8, 2026 Processing time: 79 Days and 17.7 Hours
Abstract
Colorectal cancer ranks among the commonest malignancies globally, and the liver is the main site of metastatic spread. Approximately 50% of patients develop colorectal liver metastases (CRLM), and its prognosis is strongly influenced by CRLM. The only practical route to cure is resection combined with systemic and locoregional therapy, but the decision-making is interlinked and guided only partially by qualitative imaging and standard clinicopathological tests. In this narrative review, we explore where artificial intelligence (AI) stands across the CRLM pathway: Lesion detection and characterization, deep-learning classification of histopathological growth patterns, automated volumetry and surgical planning, intraoperative guidance, prediction of response to systemic and ablative therapy, and estimation of recurrence and survival. AI performance is often equal to or better than expert performance for development cohorts and captures prognostic insight that is missed by qualitative reading. However, retrospective single-center design, small samples, inconsistent external validation, and fragile feature reproducibility hinder translation. Advances will rely on prospective multi-institutional testing, standardized reporting, privacy-preserving collaborative training, consideration of algorithmic bias and regulatory requirements, and integration of imaging, pathology, molecular, and clinical data. Nevertheless, AI has the potential to be an effective adjunct for interdisciplinary CRLM care.
Core Tip: Artificial intelligence is now applied across every phase of colorectal liver metastasis care, but its maturity is uneven. Automated detection and liver volumetry already match expert performance, while models predicting response, recurrence, and survival often flounder during external validation. But the question is no longer whether algorithms can learn clinically relevant patterns, but whether they survive beyond the cohort that produced them. Progress will involve prospective multi-institutional validation, reproducible features, and multimodal data integration that enhances, rather than replaces, multidisciplinary clinical judgment.
