ArticleDiagnostics (Basel, Switzerland)2023
Machine Learning Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy for ER+HER2- Breast Cancer: Integrating Tumoral and Peritumoral MRI Radiomic Features.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 9 citations in OpenAlex.
- Unlocking the potential of radiomics in predicting the response of neoadjuvant immunochemotherapy for operable locally advanced esophageal squamous cell carcinoma: a narrative review.Journal of thoracic disease · 2026Review
- A radiomics-deep learning nomogram integrating intratumoral and peritumoral DCE-MRI features for pCR prediction in breast cancer.Frontiers in oncology · 2026Article
- MRI-based artificial intelligence models for post-neoadjuvant surgery personalization in breast cancer: a narrative review of evidence from Western Pacific.The Lancet regional health. Western Pacific · 2025Review
- Artificial Intelligence and Breast Cancer Management: From Data to the Clinic.Cancer innovation · 2025Review
- Predicting relative efficacy of anthracyclines and taxanes in breast cancer neoadjuvant AC-T chemotherapy using longitudinal MRI radiomic model.Frontiers in oncology · 2025Article
- Optimized prediction of breast cancer tumor microenvironment using MRI-based intratumoral and peritumoral radiomics: a prospective study.Frontiers in oncology · 2025Article
- Prediction of Bone Marrow Metastases Using Computed Tomography (CT) Radiomics in Patients with Gastric Cancer: Uncovering Invisible Metastases.Diagnostics (Basel, Switzerland) · 2024Article
- Clinical-radiomics nomogram based on the fat-suppressed T2 sequence for differentiating luminal and non-luminal breast cancer.Frontiers in oncology · 2024Article
- Preoperative Prediction of Breast Cancer Histological Grade Using Intratumoral and Peritumoral Radiomics Features from T2WI and DWI MR Sequences.Breast cancer (Dove Medical Press) · 2024Article
- GRACE-ViT: Grouped Recalibration With Adaptive Contextual Emphasis for Breast Cancer Neoadjuvant Chemotherapy Response Prediction.Technology in cancer research & treatmentArticle
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundThis study aimed to predict pathologic complete response (pCR) in neoadjuvant chemotherapy for ER+HER2- locally advanced breast cancer (LABC), a subtype with limited treatment response.
methodsWe included 265 ER+HER2- LABC patients (2010-2020) with pre-treatment MRI, neoadjuvant chemotherapy, and confirmed pathology. Using data from January 2016, we divided them into training and validation cohorts. Volumes of interest (VOI) for the tumoral and peritumoral regions were segmented on preoperative MRI from three sequences: T1-weighted early and delayed contrast-enhanced sequences and T2-weighted fat-suppressed sequence (T2FS). We constructed seven machine learning models using tumoral, peritumoral, and combined texture features within and across the sequences, and evaluated their pCR prediction performance using AUC values.
resultsThe best single sequence model was SVM using a 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase (AUC = 0.9447). Among the combinations, the top-performing model was K-Nearest Neighbor, using 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase and 3 mm peritumoral VOI in T2FS (AUC = 0.9631).
conclusionsWe suggest that a combined machine learning model that integrates tumoral and peritumoral radiomic features across different MRI sequences can provide a more accurate pretreatment pCR prediction for neoadjuvant chemotherapy in ER+HER2- LABC.
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