Radiomics-driven prediction of pathologic complete response in non-mass breast cancer using post-neoadjuvant chemotherapy preoperative dynamic contrast-enhanced magnetic resonance imaging

Accurate early prediction of pCR could help identify patients most likely to benefit from NAC and avoid ineffective treatment in non-responders. The developed radiomics model offers an interpretable and reproducible tool; upon successful external validation, it has the potential to support personalized treatment planning in patients with NML-type breast cancer.

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Publication Details

Journal
Diagnostic and Interventional Radiology
Published
2026-03-09
DOI
https://doi.org/10.4274/dir.2026.263773
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

Radiomics-driven prediction of pathologic complete response in non-mass breast cancer using post-neoadjuvant chemotherapy preoperative dynamic contrast-enhanced magnetic resonance imaging

Cheng Liu, Tao Ai, Yang Liu, O. Moroz et al.
Diagnostic and Interventional Radiology
Radiomics and Machine Learning in Medical Imaging
article

Radiomics-driven prediction of pathologic complete response in non-mass breast cancer using post-neoadjuvant chemotherapy preoperative dynamic contrast-enhanced magnetic resonance imaging

Cheng Liu, Tao Ai, Yang Liu, O. Moroz, XingRui Li, Qian Yin
article en

Abstract

Accurate early prediction of pCR could help identify patients most likely to benefit from NAC and avoid ineffective treatment in non-responders. The developed radiomics model offers an interpretable and reproducible tool; upon successful external validation, it has the potential to support personalized treatment planning in patients with NML-type breast cancer.

Diagnostic and Interventional Radiology
Central South University (CN), Xiangya Hospital Central South University (CN), Tongji Hospital (CN), Huazhong University of Science and Technology (CN)
Good health and well-being
Openalex Percentile: Top 30%
Radiomics and Machine Learning in Medical Imaging
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