Personalized colorectal cancer risk assessment through explainable AI and Gut microbiome profiling
Abstract
ABSTRACT The clinical adenoma – carcinoma progression represents a well-established framework for understanding colorectal cancer (CRC) development, although the molecular mechanisms underlying this transition remain only partially understood. Increasing evidence suggests the gut microbiome (GM) as a key modulator of colorectal carcinogenesis, positioning microbial profiling as a promising avenue for noninvasive risk stratification and early detection. In this study, Machine Learning (ML) classifiers integrated with eXplainable Artificial Intelligence (XAI) techniques were employed to identify microbiome-derived biomarkers predictive of CRC and adenomatous lesions. The models were trained on 16S rRNA sequencing data from 453 patients and evaluated through cross-validation, achieving AU-ROC and AU-PRC scores of 0.71 and 0.67, respectively. External validation on an independent Italian cohort ($n = 43$n=43) yielded AU-ROC and AU-PRC scores of 0.70 and 0.89, respectively. XAI-based interpretation revealed consistent microbial signatures across datasets. In detail, taxa belonging to the Fusobacterium and Peptostreptococcus genera were associated with increased CRC risk, whereas the Eubacterium eligens group was identified as a robust negative predictor. Beyond classification, patient-level explanations enabled by XAI facilitated the identification of adenoma subgroups exhibiting microbiome profiles converging toward those of CRC, suggesting the presence of transitional microbial…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 453 participants or records.
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