Novel technologies for gastrointestinal cancer detection: a systematic review and meta-analysis of diagnostic accuracy.

INTRODUCTION: Gastrointestinal (GI) cancers account for a quarter of all cancers and one-third of cancer-related deaths worldwide. Novel diagnostic approaches are crucial to enhance early detection and improve patient outcomes. This review systematically identified and evaluated novel GI cancer detection approaches. METHODS: A systematic review was conducted using PubMed, EMBASE, Scopus, and the Cochrane Library on December 11, 2024, and updated on June 2, 2025. Cohort studies, published in English, that evaluated a novel diagnostic approach for incident GI cancer in a clinical setting, included ≥300 participants, and reported diagnostic accuracy metrics were eligible for inclusion. Two reviewers independently screened studies and assessed risk of bias using the QUADAS-2 tool. RESULTS: Of 9,604 studies screened, 72 were included: by site, 51 on colorectal, one on upper GI, one on esophageal, seven on gastric, nine on pancreatic, and five on hepatocellular carcinoma (HCC). Stool DNA testing in patients with a positive FIT showed the highest performance for colorectal cancer (AUC 0.94-0.98). The best performing technologies across other cancer types were Q-Cancer for pancreatic (AUC 0.89-0.92), the GALADUS model for HCC in cirrhotic patients (AUC 0.94), and an artificial intelligence model for esophageal cancer (AUC 0.87). DISCUSSION: This review highlights several promising novel diagnostic approaches for GI cancer detection. External validation, population-level studies, and health economic evaluation are crucial to establish clinical utility across different settings and populations. Clinicians and policymakers must weigh the added value of novel technologies against current practice, regulatory requirements, clinical understanding, interpretation of results, and public trust.