Machine learning and microbiome analysis for early detection of pancreatic cancer

To develop machine learning (ML) models integrating clinical and microbial predictors for early pancreatic cancer (PC) detection.

Pancreatic cancer is a leading cause of cancer-related mortality, with a 5-year survival rate of ~12%. Limited biomarkers and non-specific risk factors hinder early diagnosis. Emerging evidence links oral and gut microbiota, such as Fusobacterium nucleatum and Roseburia species, to PC risk, offering potential for non-invasive biomarkers.

We analyzed a retrospective cohort of 40 participants (20 PC cases, 20 controls). Clinical (e.g., age, WBC) and microbial (e.g., Fusobacterium nucleatum, Roseburia-to-Fusobacterium ratio [RI/FN]) predictors were evaluated using five ML classifiers (logistic regression, SVM, random forest, naïve Bayes, neural network) under Leave-Group-Out Cross-Validation (LGOCV; 80/20 split, 200 repetitions). Elastic-net regularization and stability selection identified key predictors. Performance metrics included AUC, sensitivity, specificity, PPV, NPV, and accuracy. Nomograms were developed for clinical utility.

Age (AUC 97.4%) and microbial markers (e.g., RI/FN ratio, AUC 100%) showed excellent discrimination. Multivariable models using age and RI/FN achieved excellent performance (AUC 98-100%). Nomograms provided interpretable risk estimates.

Integrating clinical and microbial predictors with ML offers a promising approach for non-invasive PC detection. The RI/FN ratio and age are robust biomarkers that warrant further validation in larger cohorts. However, the small sample size limits generalizability and warrants validation in larger cohorts.