ArticleBreast cancer research : BCR2017
Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI.
Article in Breast cancer research : BCR, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 397 papers, 5 of them syntheses that pooled it.
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Who cites it
397 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Pooled it
- Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.BMC cancer · 2024Pooled it
- Diagnostic performance of DCE-MRI radiomics in predicting axillary lymph node metastasis in breast cancer patients: A meta-analysis.PloS one · 2024Pooled it
- Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review.BMC bioinformatics · 2023Pooled it
- Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy.Seminars in cancer biology · 2022Pooled it
- Neoadjuvant cabozantinib for locally advanced nonmetastatic clear cell renal cell carcinoma: a phase 2 trial.Nature cancer · 2025Trial
- Texture-Based Radiomic SD-OCT Features Associated With Response to Anti-VEGF Therapy in a Phase III Neovascular AMD Clinical Trial.Translational vision science & technology · 2024Trial
- High-temporal resolution DCE-MRI improves assessment of intra- and peri-breast lesions categorized as BI-RADS 4.BMC medical imaging · 2023Trial
- A Computed Tomography Radiomics Approach for Assessing the T Cell-Inflamed Gene Expression Profile in Hepatocellular Carcinoma and Its Association With Immunotherapy Response.Health care science · 2026Article
- Pixels to Prognosis: Translating Synthetic MRI Radiomics into Clinical Practice for Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer.Radiology. Imaging cancer · 2026Article
- Intratumoral and peritumoral-based radiomics for assessment of lymphovascular invasion in invasive breast cancer: model development and validation.Translational cancer research · 2026Article
- An interpretable biparametric MRI habitat radiomics model for predicting bone metastasis in prostate cancer: a dual-center study.Translational andrology and urology · 2026Article
- Mammography-Based Radiomics for Prediction of Nodal Status and Disease Burden in Breast Cancer: A Temporally Validated Study.Cancers · 2026Article
- Differentiating breast cancer lung metastasis from primary lung cancer using clinical and computed tomography radiomics features.Translational cancer research · 2026Article
- Integrating MRI radiomics and deep learning for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study.BMC medical imaging · 2026Article
- 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
- Comparative analysis of tumor and mesorectum radiomics in predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Personalizing adjuvant radiotherapy decisions in breast cancer: an externally test MRI radiomics approach for recurrence risk stratification.Radiation oncology (London, England) · 2026Article
- A biology-informed radiomics model for prognostication of hepatocellular carcinoma based on AKR1B10 expression.European journal of radiology open · 2026Article
- Radiologic exposomics: imaging the environmental imprint on cancer for precision oncology.La Radiologia medica · 2026Review
337 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
Abstract
backgroundIn this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC).
methodsA total of 117 patients who had received NAC were retrospectively analyzed. Within the intratumoral and peritumoral regions of T1-weighted contrast-enhanced MRI scans, a total of 99 radiomic textural features were computed at multiple phases. Feature selection was used to identify a set of top pCR-associated features from within a training set (n = 78), which were then used to train multiple machine learning classifiers to predict the likelihood of pCR for a given patient. Classifiers were then independently tested on 39 patients. Experiments were repeated separately among hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR
resultsAmong all patients, a combined intratumoral and peritumoral radiomic feature set yielded a maximum AUC of 0.78 ± 0.030 within the training set and 0.74 within the independent testing set using a diagonal linear discriminant analysis (DLDA) classifier. Receptor status-specific feature discovery and classification enabled improved prediction of pCR, yielding maximum AUCs of 0.83 ± 0.025 within the HR
conclusionsThrough a combined intratumoral and peritumoral radiomics approach, we could successfully predict pCR to NAC from pretreatment breast DCE-MRI, both with and without a priori knowledge of receptor status. Further, our findings suggest that the radiomic features most predictive of response vary across different receptor subtypes.
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