MINI REVIEW article
Front. Neurol.
Sec. Artificial Intelligence in Neurology
Volume 16 - 2025 | doi: 10.3389/fneur.2025.1581422
This article is part of the Research TopicImaging to Guide Treatment in Brain TumorsView all 6 articles
Artificial Intelligence in the Task of Segmentation and Classification of Brain Metastases Images: Current Challenges and Future Opportunities
Provisionally accepted
Yiheng Hu1
Chao Gao2
Yiren Wang3*
Zhongjian Wen1
Cheng Yang1
Hairui Deng1
Shouying Chen1
Yunfei Li2
Haowen Pang2*
Ping Zhou2*
Bin Liao2*
Yan Luo2*- 1Southwest Medical University, Luzhou, Sichuan, China
- 2The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
- 3School of Nursing, Southwest Medical University, Luzhou, China
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Brain metastases (BM) are common complications of advanced cancer, posing significant diagnostic and therapeutic challenges for clinicians. Therefore, the ability to accurately detect, segment, and classify brain metastases is crucial. This review focuses on the application of artificial intelligence (AI) in brain metastasis imaging analysis, including classical machine learning and deep learning techniques. It also discusses the role of AI in brain metastasis detection and segmentation, the differential diagnosis of brain metastases from primary brain tumors such as glioblastoma, the identification of the source of brain metastases, and the differentiation between radiation necrosis and recurrent tumors after radiotherapy.Additionally, the advantages and limitations of various AI methods are discussed, with a focus on recent advancements and future research directions. AI-driven imaging analysis holds promise for improving the accuracy and efficiency of brain metastasis diagnosis, thereby enhancing treatment plans and patient prognosis.
Keywords: brain metastases, artificial intelligence, deep learning, machine learning, Radiotherapy, Diagnostic Imaging
Received: 22 Feb 2025; Accepted: 29 Aug 2025.
Copyright: © 2025 Hu, Gao, Wang, Wen, Yang, Deng, Chen, Li, Pang, Zhou, Liao and Luo. 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:
Yiren Wang, School of Nursing, Southwest Medical University, Luzhou, China
Haowen Pang, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
Ping Zhou, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
Bin Liao, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
Yan Luo, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
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