Work

Artificial intelligence-driven approaches in pituitary neuroendocrine tumors: integrating endocrine-metabolic profiling for enhanced diagnostics and therapeutics

Year: 2025

Type: article

Abstract: Pituitary neuroendocrine tumors (PitNETs) pose diagnostic and therapeutic challenges due to their heterogeneity and complex endocrine-metabolic interactions. Artificial intelligence (AI) enhances PitNET management through improved classification, outcome prediction, and personalized treatment. However, current AI models face limitations, including small, single-center datasets and insufficient integration of multi-omics or autoimmune-associated biomarkers. Future advancements require multicenter standardized databases, explainable AI frameworks, and multimodal data fusion. By decoding endocrine-metabolic dysregulation and its link to tumor behavior, AI-driven precision medicine can optimize PitNET care. This review highlights AI's potential in PitNETs while addressing key challenges and future directions for clinical translation.

Source: Frontiers in Endocrinology

DOI: 10.3389/fendo.2025.1618412

Authors: Aiping Zheng, Dan Tang, Huijuan He, Xinyu Liang

Institutions: Sichuan University, Wenzhou Medical University, West China Hospital of Sichuan University, Southwest Medical University, Affiliated Hospital of Southwest Medical University

Language: English

Topic: Pituitary Gland Disorders and Treatments

Subfield: Endocrinology, Diabetes and Metabolism

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,801