The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI.
Authors
Affiliations (15)
Affiliations (15)
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan.
- Department of Radiology, University of Tokyo, Tokyo, Japan.
- Department of Artificial Intelligence, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Osaka, Japan.
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan.
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan.
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
- Medical Data Science Course, Shinshu University School of Medicine, Matsumoto, Nagano, Japan.
- Department of Diagnostic Radiology, Hiroshima University, Hiroshima, Hiroshima, Japan.
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan.
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Okayama, Japan.
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan.
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan.
- Department of Central Radiology, Kumamoto University Hospital, Kumamoto, Kumamoto, Japan.
Abstract
The integration of deep learning (DL) in breast MRI has revolutionized the field of medical imaging, notably enhancing diagnostic accuracy and efficiency. This review discusses the substantial influence of DL technologies across various facets of breast MRI, including image reconstruction, classification, object detection, segmentation, and prediction of clinical outcomes such as response to neoadjuvant chemotherapy and recurrence of breast cancer. Utilizing sophisticated models such as convolutional neural networks, recurrent neural networks, and generative adversarial networks, DL has improved image quality and precision, enabling more accurate differentiation between benign and malignant lesions and providing deeper insights into disease behavior and treatment responses. DL's predictive capabilities for patient-specific outcomes also suggest potential for more personalized treatment strategies. The advancements in DL are pioneering a new era in breast cancer diagnostics, promising more personalized and effective healthcare solutions. Nonetheless, the integration of this technology into clinical practice faces challenges, necessitating further research, validation, and development of legal and ethical frameworks to fully leverage its potential.