Machine learning and microbiome analysis for early detection of pancreatic cancer.

Aim: To develop machine learning (ML) models integrating clinical and microbial predictors for early pancreatic cancer (PC) detection. Background: 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. Methods: 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. Results: 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. Conclusions: 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.