ArticleScientific reports2024
Prediction of early clinical response to neoadjuvant chemotherapy in Triple-negative breast cancer: Incorporating Radiomics through breast MRI.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A Randomized, Phase II Clinical Trial of FLT-PET and FDG-PET for Early Response Assessment of Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Trial
- Article
- Development and validation of a highly accurate multigene gene expression biomarker to predict chemotherapy response in primary triple-negative breast cancer.Breast cancer research and treatment · 2026Article
- Pre-Treatment Breast MRI Features and ADC Values as Predictors of Pathologic Complete Response in Breast Cancer: A Molecular Subtype-Based Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- A delta radiomics model based on ultrasound images predicts response to neoadjuvant therapy in triple negative breast cancer.Journal of applied clinical medical physics · 2025Article
- Radiomics for Detecting Metaplastic Histology in Triple-Negative Breast Cancer: A Step Towards Personalized Therapy.Bioengineering (Basel, Switzerland) · 2025Article
- Prediction of axillary lymph node metastasis in triple negative breast cancer using MRI radiomics and clinical features.Scientific reports · 2025Article
- Application of Machine Learning to Breast MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Correlation Between Pathological and MRI Radiological Tumor Responses to Neoadjuvant Chemotherapy in Non-Luminal Breast Cancer: A Single Institution Experience.Cancer reports (Hoboken, N.J.) · 2025Article
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- Article
- DCE-MRI radiomics of primary breast lesions combined with ipsilateral axillary lymph nodes for predicting efficacy of NAT.BMC cancer · 2025Article
- Prediction of lymphovascular invasion in invasive breast cancer based on clinical-MRI radiomics features.BMC medical imaging · 2024Article
- GRACE-ViT: Grouped Recalibration With Adaptive Contextual Emphasis for Breast Cancer Neoadjuvant Chemotherapy Response Prediction.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
This study assessed pretreatment breast MRI coupled with machine learning for predicting early clinical responses to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC), focusing on identifying non-responders. A retrospective analysis of 135 TNBC patients (107 responders, 28 non-responders) treated with NAC from January 2015 to October 2022 was conducted. Non-responders were defined according to RECIST guidelines. Data included clinicopathologic factors and clinical MRI findings, with radiomics features from contrast-enhanced T1-weighted images, to train a stacking ensemble of 13 machine learning models. For subgroup analysis, propensity score matching was conducted to adjust for clinical disparities in NAC response. The efficacy of the models was evaluated using the area under the receiver-operating-characteristic curve (AUROC) before and after matching. The model combining clinicopathologic factors and clinical MRI findings achieved an AUROC of 0.752 (95% CI 0.644-0.860) for predicting non-responders, while radiomics-based models showed 0.749 (95% CI 0.614-0.884). An integrated model of radiomics, clinicopathologic factors, and clinical MRI findings reached an AUROC of 0.802 (95% CI 0.699-0.905). After propensity score matching, the hierarchical order of key radiomics features remained consistent. Our study demonstrated the potential of using machine learning models based on pretreatment MRI to non-invasively predict TNBC non-responders to NAC.
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