ArticleBreast cancer research and treatment2019
Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.
Article in Breast cancer research and treatment, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers, 2 of them syntheses that pooled it.
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Who cites it
95 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- The Diagnostic Performance of Minimally Invasive Biopsy in Predicting Breast Pathological Complete Response After Neoadjuvant Systemic Therapy in Breast Cancer: A Meta-Analysis.Frontiers in oncology · 2020Pooled it
- Longitudinal Breast MRI for Early Treatment-Response Modeling: A Comparative Study of Handcrafted Radiomics and Frozen Deep Image Embeddings for pCR Prediction.Diagnostics (Basel, Switzerland) · 2026Article
- Personalizing adjuvant radiotherapy decisions in breast cancer: an externally test MRI radiomics approach for recurrence risk stratification.Radiation oncology (London, England) · 2026Article
- Early cardiotoxicity of radiotherapy in breast cancer patients after chemotherapy: the early warning sign.Journal of the Egyptian National Cancer Institute · 2026Observational
- Time-Dependent DCE-MRI Radiomics to Predict Response to Neoadjuvant Therapy in Breast Cancer: A Multicenter Study with External Validation.Diagnostics (Basel, Switzerland) · 2026Article
- Pretreatment MRI radiomics for predicting pathological Miller-Payne grading in breast cancer following neoadjuvant chemotherapy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Preoperative Breast MRI and Histopathology in Breast Cancer: Concordance, Challenges and Emerging Role of CEM and mpMRI.Diagnostics (Basel, Switzerland) · 2025Review
- Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Predicting breast cancer response to neoadjuvant therapy by integrating radiomic and deep-learning features from early-and-peak phases of DCE-MRI.BMC cancer · 2025Article
- Integrating multiparametric MRI radiomics and clinical models to assess sensitivity to neoadjuvant chemotherapy in breast cancer: A multicenter study.Journal of applied clinical medical physics · 2025Article
- Prediction of Breast Cancer Response to Neoadjuvant Therapy with Machine Learning: A Clinical, MRI-Qualitative, and Radiomics Approach.Life (Basel, Switzerland) · 2025Article
- Machine learning model for predicting tertiary lymphoid structures and treatment response in triple-negative breast cancer.NPJ precision oncology · 2025Article
- Article
- Predictive value of tumoral and peritumoral radiomic features in neoadjuvant chemotherapy response for breast cancer: a retrospective study.La Radiologia medica · 2025Article
- DCE-MRI radiomics of primary breast lesions combined with ipsilateral axillary lymph nodes for predicting efficacy of NAT.BMC cancer · 2025Article
- Article
- MRI-based radiomics nomogram for predicting CD8-positive tumor-infiltrating lymphocytes levels in HER2-positive breast cancer.Frontiers in oncology · 2025Article
- TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs.Medical image analysis · 2025Article
- Combining Biology-based and MRI Data-driven Modeling to Predict Response to Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer.Radiology. Artificial intelligence · 2025Article
35 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
Abstract
purposeTo determine whether a multivariate machine learning-based model using computer-extracted features of pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer patients.
methodsInstitutional review board approval was obtained for this retrospective study of 288 breast cancer patients at our institution who received NAT and had a pre-treatment breast MRI. A comprehensive set of 529 radiomic features was extracted from each patient's pre-treatment MRI. The patients were divided into equal groups to form a training set and an independent test set. Two multivariate machine learning models (logistic regression and a support vector machine) based on imaging features were trained to predict pCR in (a) all patients with NAT, (b) patients with neoadjuvant chemotherapy (NACT), and (c) triple-negative or human epidermal growth factor receptor 2-positive (TN/HER2+) patients who had NAT. The multivariate models were tested using the independent test set, and the area under the receiver operating characteristics (ROC) curve (AUC) was calculated.
resultsOut of the 288 patients, 64 achieved pCR. The AUC values for predicting pCR in TN/HER+ patients who received NAT were significant (0.707, 95% CI 0.582-0.833, p < 0.002).
conclusionsThe multivariate models based on pre-treatment MRI features were able to predict pCR in TN/HER2+ patients.
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