REVIEW article
Front. Med.
Sec. Nuclear Medicine
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1643850
This article is part of the Research TopicRecent developments in artificial intelligence and radiomicsView all 7 articles
Research progress in artificial intelligence for brain metastases
Provisionally accepted
- 1Shandong First Medical University - Tai'an Campus, Tai'an, China
- 2Shandong First Medical University, Jinan, China
- 3Shandong Provincial Hospital, Jinan, China
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As artificial intelligence (AI) continues to evolve, its integration into medical practice is becoming increasingly prominent, particularly in the field of neuro-oncology. This review examines the application of AI—specifically machine learning (ML) and deep learning (DL)—in the imaging evaluation of brain metastases (BM). A systematic search of PubMed was conducted to identify relevant studies published within the past five years. The retrieved literature was categorized and analyzed according to three key clinical tasks: segmentation, differential diagnosis, and prognostic prediction.We first outline the capabilities of AI in the automatic detection and segmentation of BM using advanced imaging techniques. Subsequently, we synthesize evidence on how AI aids in distinguishing BM from other intracranial structures and lesions. Finally, we discuss the emerging role of AI in predicting disease prognosis and the development of new metastatic abnormalities.Current evidence suggests that AI not only enhances diagnostic efficiency and reproducibility but also provides clinically meaningful insights that support personalized treatment planning. Importantly, the integration of AI into neuro-oncological imaging remains at a nascent stage, indicating substantial potential for future growth and refinement in both technical performance and clinical applicability.
Keywords: brain metastases, MRI, deep learning, machine learning, CT
Received: 09 Jun 2025; Accepted: 05 Sep 2025.
Copyright: © 2025 Wang, Wang, Liang and Li. 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: Tong Li, Shandong Provincial Hospital, Jinan, China
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