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作者

Dongxiang Wang Wei Wang Tong Li Chenqi Liang Xia Zhao

作者单位

³Department of Nuclear Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China ¹Medical Imaging, Faculty of Radiology, Shandong First Medical University, Tai'an, Shandong, China ⁴Department of Radiology, Shandong University of Traditional Chinese Medicine Affiliated Hospital, Jinan, China ²Clinical Medicine, Faculty of Clinical and Basic Medicine, Shandong First Medical University, Jinan, Shandong, China

刊名

Frontiers in Medicine

年份

2025

卷号

Vol.12

关键词

brain metastases MRI deep learning machine learning CT

摘要

As artificial intelligence 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 and deep learning —in the imaging evaluation of brain metastases . A systematic search of PubMed was conducted to identify relevant studies published within the past 5 years. The retrieved literature was categorized and analyzed according to three key clinic...更多

As artificial intelligence 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 and deep learning —in the imaging evaluation of brain metastases . A systematic search of PubMed was conducted to identify relevant studies published within the past 5 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.收起

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期刊