REVIEW article
Front. Endocrinol.
Sec. Cancer Endocrinology
Volume 16 - 2025 | doi: 10.3389/fendo.2025.1618412
This article is part of the Research TopicRefining Precision Medicine through AI and Multi-omics IntegrationView all 5 articles
Artificial Intelligence-Driven Approaches in Pituitary Neuroendocrine Tumors: Integrating Endocrine-Metabolic Profiling for Enhanced Diagnostics and Therapeutics
Provisionally accepted
- 1West China School of Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- 2Southwest Medical University, Luzhou, Sichuan, China
- 3Quzhou City People's Hospital, Quzhou, China
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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.
Keywords: artificial intelligence, deep learning, Pituitary neuroendocrine tumors, diagnostics, therapeutics 1 Introduction
Received: 26 Apr 2025; Accepted: 15 Sep 2025.
Copyright: © 2025 Zheng, Tang, He and Liang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Huijuan He, zouyan189@163.com
Xinyu Liang, rainxl@foxmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.