ArticleCancers2021
MRI-Based Radiomics Analysis for the Pretreatment Prediction of Pathologic Complete Tumor Response to Neoadjuvant Systemic Therapy in Breast Cancer Patients: A Multicenter Study.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Prediction of pathological complete response to neoadjuvant therapy in breast cancer integrating intratumoral and peritumoral delta radiomics with clinical features: a study based on multiparametric MRI.BMC medical imaging · 2026Article
- Deep learning-based radiomics does not improve residual cancer burden prediction post-chemotherapy in LIMA breast MRI trial.European radiology · 2026Article
- MRI-based texture analysis for breast cancer subtype classification in a multi-ethnic population.Magma (New York, N.Y.) · 2026Article
- Integrated conventional ultrasound and radiomics model for predicting major pathological response to neoadjuvant therapy in triple-negative breast cancer.BMC medical imaging · 2025Article
- Early prediction of neoadjuvant chemotherapy efficacy among patients with triple-negative breast cancer using an ultrasound-based radiomics nomogram.Diagnostic and interventional radiology (Ankara, Turkey) · 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
- A Priori Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Using Deep Features from Pre-Treatment MRI and CT.Cancers · 2025Article
- A Priori Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy Using CT Radiomics.Cancers · 2025Article
- Refined prognostication of pathological complete response in breast cancer using radiomic features and optimized InceptionV3 with DCE-MRI.Scientific reports · 2025Article
- Article
- Tumor Morphology for Prediction of Poor Responses Early in Neoadjuvant Chemotherapy for Breast Cancer: A Multicenter Retrospective Study.Tomography (Ann Arbor, Mich.) · 2024Article
- Baseline [European journal of nuclear medicine and molecular imaging · 2024Article
- Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity.Breast cancer research : BCR · 2024Article
- The residual cancer burden index as a valid prognostic indicator in breast cancer after neoadjuvant chemotherapy.BMC cancer · 2024Article
- Imaging-proteomic analysis for prediction of neoadjuvant chemotherapy responses in patients with breast cancer.Cancer medicine · 2023Article
- Machine Learning Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy for ER+HER2- Breast Cancer: Integrating Tumoral and Peritumoral MRI Radiomic Features.Diagnostics (Basel, Switzerland) · 2023Article
- External validation of an MR-based radiomic model predictive of locoregional control in oropharyngeal cancer.European radiology · 2023Article
- Thoughtful Response on "MRI-based Texture Analysis for Preoperative Prediction ofJournal of multidisciplinary healthcare · 2023Article
- The value, diagnostic efficacy and clinical significance of functional magnetic resonance imaging in evaluating the efficacy of neoadjuvant chemotherapy in patients with triple negative breast cancer.Frontiers in oncology · 2023Article
- Article
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13 authors.
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Abstract
This retrospective study investigated the value of pretreatment contrast-enhanced Magnetic Resonance Imaging (MRI)-based radiomics for the prediction of pathologic complete tumor response to neoadjuvant systemic therapy in breast cancer patients. A total of 292 breast cancer patients, with 320 tumors, who were treated with neo-adjuvant systemic therapy and underwent a pretreatment MRI exam were enrolled. As the data were collected in two different hospitals with five different MRI scanners and varying acquisition protocols, three different strategies to split training and validation datasets were used. Radiomics, clinical, and combined models were developed using random forest classifiers in each strategy. The analysis of radiomics features had no added value in predicting pathologic complete tumor response to neoadjuvant systemic therapy in breast cancer patients compared with the clinical models, nor did the combined models perform significantly better than the clinical models. Further, the radiomics features selected for the models and their performance differed with and within the different strategies. Due to previous and current work, we tentatively attribute the lack of improvement in clinical models following the addition of radiomics to the effects of variations in acquisition and reconstruction parameters. The lack of reproducibility data (i.e., test-retest or similar) meant that this effect could not be analyzed. These results indicate the need for reproducibility studies to preselect reproducible features in order to properly assess the potential of radiomics.
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