The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI

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.

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Publication Details

Journal
Magnetic Resonance in Medical Sciences
Published
2024-10-28
DOI
https://doi.org/10.2463/mrms.rev.2024-0056
Citations
2
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.81
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article

The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI

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2 citations
Magnetic Resonance in Medical Sciences
Radiomics and Machine Learning in Medical Imaging
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article

The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI

Yusuke Matsui, Shinji Naganawa, Ukihide Tateishi, Fuminari Tatsugami, Takahiro Tsuboyama, Rintaro Ito, Tomoyuki Fujioka, Shohei Fujita, Takeshi Nakaura, Yasutaka Fushimi, Noriyuki Fujima, Kenji Hirata, Masahiro Yanagawa, Daiju Ueda, Akira Yamada, Taiki Nozaki, Koji Kamagata, Mariko Kawamura
article en
2 citations

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.

Magnetic Resonance in Medical SciencesVol. 24(4)
Hiroshima University (JP), Tokyo Medical and Dental University (JP), Shinshu University (JP), Okayama University (JP), Keio University (JP), Hokkaido University (JP), Kyoto University (JP), Juntendo University (JP), Kumamoto University Hospital (JP), Hokkaido University Hospital (JP), Kumamoto Chuo Hospital (JP), Osaka Metropolitan University (JP), Nagoya University (JP), Kobe University (JP), The University of Tokyo (JP), The University of Osaka (JP)
Openalex Percentile: Top 25%
Radiomics and Machine Learning in Medical Imaging
0.81
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