Machine Learning for Predicting Colorectal Cancer and Advanced Colorectal Polyps: A Systematic Review and Meta-Analysis
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View abstract on PubMed
Summary
This summary is machine-generated.Machine learning models show promise in predicting colorectal cancer (CRC) and advanced colorectal polyps (ACP) risk, with pooled sensitivity of 0.832 and specificity of 0.802. Further standardization is needed for routine clinical application.
Area Of Science
- Medical Informatics
- Oncology
- Artificial Intelligence
Background
- Machine learning (ML) is increasingly vital in healthcare for colorectal cancer (CRC) detection and diagnosis.
- AI-assisted colonoscopies and predictive models are enhancing diagnostic capabilities.
Purpose Of The Study
- To evaluate the efficacy of ML models in predicting the risk for CRC and advanced colorectal polyps (ACP) before colonoscopy.
Main Methods
- Systematic literature review following PRISMA guidelines.
- Data extraction using the CHARMS checklist.
- Meta-analysis to assess model performance for CRC and adenoma prediction.
Main Results
- Included 14 studies with a median of 3618 patients.
- Demonstrated considerable heterogeneity in methodologies and outcomes (AUROC 0.6-1).
- Pooled sensitivity: 0.832 (95% CI: 0.755-0.889); Specificity: 0.802 (95% CI: 0.722-0.863); Overall AUROC: 0.883.
Conclusions
- ML plays a significant role in CRC and ACP diagnosis.
- Routine ML use could optimize colonoscopy referrals for high-risk patients.
- Variability necessitates standardized approaches and further research.
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