Exploration of Medicine

Table 5. CRC screening tests using blood-based markers with AI integration and cited performance.

Blood test name or vendor	Analytes	Target	AI algorithm + input Data	Performance metrics	References
mSEPT9	ctDNA	CRC specific	Random forest (cfDNA methylation data)	AUC 0.82–0.89; Sens 69–77%; Spec 88–92%	[148, 149]
Freenome	ctDNA + protein	CRC specific	Ensemble deep learning (multi-omics: cfDNA, protein)	AUC 0.94; Sens 79–91%; Spec 91–96%	[150, 151]
CancerSEEK	ctDNA + protein	Multi-cancer	Random forest + logistic regression (multi-analyte)	AUC 0.94; Sens 69% (CRC); Spec 99%	[152, 153]
Guardant	ctDNA	CRC specific	Targeted NGS + ML classifier (proprietary)	Sens ~91%; Spec ~94%	[154, 155]
Grail (Galleri)	ctDNA	Multi-cancer	Deep neural networks (cfDNA methylation)	Sens 67% (CRC); Spec 99.5%; PPV 88.7%; early-stage sens ~27.5%	[156–165]
Clinical Genomics	ctDNA	CRC specific	Likely logistic regression/SVM on methylation	Sens 81%; Spec 91% (early trials)	[166–169]

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CRC: colorectal cancer; AI: artificial intelligence; mSEPT9: methylated SEPT9; ctDNA: circulating tumor DNA; cfDNA: cell-free DNA; Sens: sensitivity; Spec: specificity; ML: machine learning.

Declarations

Acknowledgments

AI-Assisted Work Statement: During the preparation of this work, the authors used MS PowerPoint and ChatGPT to put together the text and icons to generate the graphical abstract. After using MS PowerPoint and ChatGPT to generate the image, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Author contributions

TDP: Conceptualization. DTPL: Writing—original draft, Conceptualization. Both authors read, edited, and approved the submitted version.

Conflicts of interest

Dr. Tuan D. Pham, who is the Editorial Board Member of Exploration of Medicine, had no involvement in the decision-making or review process of this manuscript. The other author declares that he has no conflicts of interest.

Ethical approval

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