ArticleMedical physics2015
Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy.
Article in Medical physics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Time-Dependent DCE-MRI Radiomics to Predict Response to Neoadjuvant Therapy in Breast Cancer: A Multicenter Study with External Validation.Diagnostics (Basel, Switzerland) · 2026Article
- Dual-input spatio-temporal transformer model: Predicting the efficacy of NACT in breast cancer based on DCE-MRI images.iScience · 2026Article
- Improving diffuse optical tomography imaging quality using APU-Net: an attention-based physical U-Net model.Journal of biomedical optics · 2024Article
- Review
- Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction.Frontiers in oncology · 2022Review
- Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence.La Radiologia medica · 2022Review
- Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.Current genomics · 2021Review
- Machine learning model with physical constraints for diffuse optical tomography.Biomedical optics express · 2021Article
- AI-Enhanced Diagnosis of Challenging Lesions in Breast MRI: A Methodology and Application Primer.Journal of magnetic resonance imaging : JMRI · 2021Review
- Clinicopathologic breast cancer characteristics: predictions using global textural features of the ipsilateral breast mammogram.Radiological physics and technology · 2021Article
- A novel CNN algorithm for pathological complete response prediction using an I-SPY TRIAL breast MRI database.Magnetic resonance imaging · 2020Article
- Machine learning in breast MRI.Journal of magnetic resonance imaging : JMRI · 2020Review
- Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy.Breast (Edinburgh, Scotland) · 2020Review
- A robust method for segmenting pectoral muscle in mediolateral oblique (MLO) mammograms.International journal of computer assisted radiology and surgery · 2019Article
- Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.Investigative radiology · 2019Article
- 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.Breast cancer research and treatment · 2019Article
- Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter-reader variability in annotating tumors.Medical physics · 2018Article
- Appearance Constrained Semi-Automatic Segmentation from DCE-MRI is Reproducible and Feasible for Breast Cancer Radiomics: A Feasibility Study.Scientific reports · 2018Article
- A computer-aided diagnosis (CAD) scheme for pretreatment prediction of pathological response to neoadjuvant therapy using dynamic contrast-enhanced MRI texture features.The British journal of radiology · 2017Observational
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Authors and funding
6 authors.
Funding
Abstract
purposeTo identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy.
methodsThe authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method.
resultsIn the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC=0.85±0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96±0.03 (p<0.01).
conclusionsThis study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy.
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