Artificial Intelligence for Colorectal Cancer Diagnosis: A Comprehensive Survey of Modalities, Frameworks, and Clinical Tasks
- 1Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA
- 2Department of Pathology and Laboratory Medicine, School of Medicine, University of Louisville, Louisville, KY 40292, USA
- 3Electrical, Computer, and Biomedical Engineering Department, College of Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
Abstract
The diagnosis of colorectal cancer encompasses nine complementary modalities: stool-based screening, blood-based biomarkers, cross-sectional imaging, endoscopic evaluation, histopathology, liquid biopsy, molecular and genetic profiling, microbiome analysis, and nanotechnology-based diagnostics. This narrative review synthesizes 160 references spanning seven task-specific artificial intelligence domains, including computer-aided detection, optical diagnosis, invasion depth prediction, computational pathology and radiogenomics, metastasis forecasting, neoadjuvant treatment response prediction, and surgical workflow analysis. We propose a conceptual multimodal pipeline comprising data ingestion, preprocessing and harmonization, feature representation, multimodal fusion, forecasting, explainability, and validation. A structured evidence grading framework classifies each clinical task by evidence level, consistency, and clinical readiness. We further catalog public datasets, challenge benchmarks, and five reporting standards: TRIPOD-AI, CONSORT-AI, SPIRIT-AI, DECIDE-AI, and CLAIM. Computer-aided detection and optical diagnosis demonstrate the highest maturity with Level 1 evidence from randomized controlled trials, while computational pathology, radiogenomics, and metastasis prediction remain at Level 2 with retrospective validation (internal or external). Microbiome screening, nanotechnology diagnostics, and surgical phase recognition are exploratory. The principal barriers to clinical adoption are domain shift, data scarcity, calibration, prospective validation, and workflow integration. We conclude that clinically useful artificial intelligence requires robust external validation, uncertainty quantification, explainable outputs, standardized reporting, and a structured translational pathway from retrospective evidence through regulatory clearance to post-deployment monitoring.
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BibTeX
@article{elmosalamy2026artificial,
title = {{Artificial Intelligence for Colorectal Cancer Diagnosis: A Comprehensive Survey of Modalities, Frameworks, and Clinical Tasks}},
author = {Bassant El-Mosalamy and Hossam Magdy Balaha and Sohayla El-Moursi and Dibson Gondim and Khaldon Lweesy and Jawad Yousaf and Mohammed Ghazal and Ayman El-Baz},
journal = {Cancers},
year = {2026},
month = nov,
doi = {10.3390/cancers18213522},
url = {https://www.mdpi.com/2072-6694/18/21/3522},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial Intelligence for Colorectal Cancer Diagnosis: A Comprehensive Survey of Modalities, Frameworks, and Clinical Tasks
AU - Bassant El-Mosalamy
AU - Hossam Magdy Balaha
AU - Sohayla El-Moursi
AU - Dibson Gondim
AU - Khaldon Lweesy
AU - Jawad Yousaf
AU - Mohammed Ghazal
AU - Ayman El-Baz
PY - 2026
DA - 2026/11/02
JO - Cancers
DO - 10.3390/cancers18213522
UR - https://www.mdpi.com/2072-6694/18/21/3522
AB - The diagnosis of colorectal cancer encompasses nine complementary modalities: stool-based screening, blood-based biomarkers, cross-sectional imaging, endoscopic evaluation, histopathology, liquid biopsy, molecular and genetic profiling, microbiome analysis, and nanotechnology-based diagnostics. This narrative review synthesizes 160 references spanning seven task-specific artificial intelligence domains, including computer-aided detection, optical diagnosis, invasion depth prediction, computational pathology and radiogenomics, metastasis forecasting, neoadjuvant treatment response prediction, and surgical workflow analysis. We propose a conceptual multimodal pipeline comprising data ingestion, preprocessing and harmonization, feature representation, multimodal fusion, forecasting, explainability, and validation. A structured evidence grading framework classifies each clinical task by evidence level, consistency, and clinical readiness. We further catalog public datasets, challenge benchmarks, and five reporting standards: TRIPOD-AI, CONSORT-AI, SPIRIT-AI, DECIDE-AI, and CLAIM. Computer-aided detection and optical diagnosis demonstrate the highest maturity with Level 1 evidence from randomized controlled trials, while computational pathology, radiogenomics, and metastasis prediction remain at Level 2 with retrospective validation (internal or external). Microbiome screening, nanotechnology diagnostics, and surgical phase recognition are exploratory. The principal barriers to clinical adoption are domain shift, data scarcity, calibration, prospective validation, and workflow integration. We conclude that clinically useful artificial intelligence requires robust external validation, uncertainty quantification, explainable outputs, standardized reporting, and a structured translational pathway from retrospective evidence through regulatory clearance to post-deployment monitoring.
SN - 2072-6694
ER -