Gastroenteropancreatic neuroendocrine tumors in 2025: From molecular profiling to artificial intelligence-driven therapy

Gastroenteropancreatic neuroendocrine tumors represent a biologically complex and clinically heterogeneous group of neoplasms with a steadily increasing global incidence. Advances in molecular profiling have identified distinct genetic landscapes across tumor subtypes, with alterations in multiple endocrine neoplasia type 1, death domain-associated protein, X-linked mental retardation and alpha-thalassemia syndrome protein, tuberous sclerosis complex 2, and phosphatase and tensin homolog commonly defining well-differentiated neuroendocrine tumors, while tumor protein P53, retinoblastoma 1, Kirsten rat sarcoma viral oncogene homolog, and B-Raf protooncogene mutations are characteristic of poorly differentiated neuroendocrine carcinomas. Although tumor differentiation remains a fundamental clinical framework, it does not fully capture the extensive intertumoral and intratumoral heterogeneity that drives variable clinical behavior and therapeutic response. In this context, the integration of multi-omics data with radiomics and artificial intelligence is reshaping diagnostic and prognostic paradigms, enabling more objective assessment of proliferative indices such as Ki67 and improving risk stratification. Advances in functional imaging, including ⁶⁸Ga-DOTATATE positron emission tomography/computed tomography, together with emerging biomarkers such as circulating tumor DNA and the NETest, are further enhancing the precision of disease monitoring. Moreover, digital pathology and machine learning approaches show promise in overcoming sampling bias and interobserver variability. This narrative review synthesizes recent insights into molecular pathogenesis, diagnostic innovations, and artificial intelligence-driven therapeutic perspectives, highlighting the transition toward an integrative, data-driven model of gastroenteropancreatic neuroendocrine tumor management that bridges biological complexity with personalized clinical decision-making and optimized patient outcomes.