What question did this study set out to answer?

To assess the diagnostic accuracy of MRI-based radiomics paired with machine learning and deep learning algorithms for classifying molecular subtypes of breast cancer.

December 26, 2024Journal of Radiation Research and Applied SciencesOpen Access

Diagnosing breast cancer subtypes using MRI radiomics and machine learning: A systematic review

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ZWZhenyue WangHeilongjiang University of Chinese Medicine

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Implication

Systematic review demonstrates superior accuracy of deep learning MRI radiomics for classifying breast cancer subtypes, highlighting potential for noninvasive precision oncology.

Key Points

To assess the diagnostic accuracy of MRI-based radiomics paired with machine learning and deep learning algorithms for classifying molecular subtypes of breast cancer.
Systematically searched PubMed, Scopus, and Web of Science through October 11, 2024, for peer-reviewed studies applying supervised machine learning to MRI radiomics (e.g., DCE-MRI, DWI) for breast cancer subtype classification.
Extracted radiomic feature data and diagnostic performance metrics across 42 eligible studies, analyzing pooled performance and generalizability.
Deep learning models consistently outperformed traditional machine learning classifiers, achieving an AUC of 0.93 compared to 0.85 for support vector machines in identifying triple-negative breast cancer.
Texture features emerged as the most critical predictors, contributing 40% to the overall diagnostic accuracy of the models.
Incorporating multiparametric MRI sequences significantly enhanced overall classification sensitivity, specificity, and accuracy across all molecular subtypes.

Cite This Study

Zhenyue Wang (2024) studied this question.

synapsesocial.com/papers/6a820a983922239fd406cd66 https://doi.org/10.1016/j.jrras.2024.101260

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Implication

Systematic review demonstrates superior accuracy of deep learning MRI radiomics for classifying breast cancer subtypes, highlighting potential for noninvasive precision oncology.

Diagnosing breast cancer subtypes using MRI radiomics and machine learning: A systematic review

Discussion

Member takes

Implication

Key Points

Cite This Study

Diagnosing breast cancer subtypes using MRI radiomics and machine learning: A systematic review

Discussion

Member takes

Implication

Key Points

Cite This Study

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