ArticleInternational journal of colorectal disease2025
AI-driven pre-screening for colorectal cancer using complete blood counts: toward broader population impact.
Article in International journal of colorectal disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Bridging the Precancerous Gap in Colorectal Cancer Through AI-Enhanced Liquid Biopsy, Endoscopy, and Digital Pathology.Cancers · 2026Review
- Colorectal Cancer Screening in Hereditary and Familial High-Risk Populations: Best Practices and Future Directions.International journal of cancer · 2026Review
- Development and Validation of an Interpretable Machine Learning Model Based on Peripheral Blood Biomarkers for Esophageal Cancer Risk Prediction.International journal of general medicine · 2026Article
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Authors and funding
12 authors.
Funding
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Abstract
purposeEarly colorectal cancer (CRC) detection is crucial for effective treatment; however, traditional screening methods face challenges. Colonoscopy, though highly effective, has limited availability, and fecal immunochemical tests (FIT) are more accessible and cost-effective but suffer from low adherence. Our retrospective study aimed to develop a transparent artificial-intelligence model leveraging routine CBC data as a cost-effective method for CRC detection.
methodsWe conducted a retrospective analysis of 28,450 individuals aged 45-75 who underwent colonoscopy within six months of a complete blood count (CBC) test. Among them, 439 (1.8%) had CRC, 2,955 (11.8%) had advanced adenomas, and 21,662 (86.5%) had benign findings on colonoscopy. The database was divided into training (70%) and testing (30%) sets. The model was developed using ridge regression.
resultsDescriptive analysis revealed significant differences between CRC cases and controls across most CBC markers, CBC-derived ratios, and age (P < 0.001), except for lymphocytes. The model, based on red cell distribution width (RDW), systemic inflammation response index (SIRI), hemoglobin, and age, achieved an AUC of 0.77 (95% CI: 0.75-0.77) for CRC, comparable to a deep learning model (TabPFN). Interpretability analysis revealed that older age, elevated RDW and SIRI, and low hemoglobin were associated with CRC. In a subgroup (7.25%) with FIT results, FIT showed higher sensitivity for CRC (88%) than the model (64%), but lower specificity (77% vs. 81%).
conclusionGiven CBC's widespread use and accessibility, this approach may be a scalable pre-screening tool to improve CRC risk stratification and optimize resource allocation, demonstrating how explainable AI may augment existing CRC screening programs.
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