ArticleScientific data2025
A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Separating Ranking Signal, Incremental Feature Value, and Evaluation-Induced Optimism in Breast MRI Treatment-Effect Analysis: Foundation Model and Radiomic Representations.Journal of imaging informatics in medicine · 2026Article
- MDIFN: a deep learning-based multi-dimensional interaction fusion network for placental MRI segmentation.Quantitative imaging in medicine and surgery · 2026Article
- Can AI Detect What Is Not Injected? Evaluation of Lesion Detection in Virtual Contrast-Enhanced Breast MRI Using a Large-Scale AI Model Trained on GBCA-Enhanced Data.Tomography (Ann Arbor, Mich.) · 2026Article
- Large-scale multi-sequence pretraining for generalizable MRI analysis in versatile clinical applications.Nature biomedical engineering · 2026Article
- Bidirectional Perceptual Multimodal Interaction Network Based on Contrastive Learning for Breast Cancer pCR Prediction.Tomography (Ann Arbor, Mich.) · 2026Article
- Fréchet radiomic distance (FRD): A versatile metric for comparing medical imaging datasets.Medical image analysis · 2026Article
- A Simple and Robust MRI Radiomics Feature for Predicting Pathological Complete Response: A Proof-of-Concept Study in Breast Cancer Neoadjuvant Chemotherapy.Cancer reports (Hoboken, N.J.) · 2026Article
- A hybrid ST-ViT-driven multimodal architecture combining spatiotemporal MRI patterns and radiomic features for enhanced prediction of pCR in neoadjuvant breast cancer therapy.Biology direct · 2026Article
- Domain Shift in Breast DCE-MRI Tumor Segmentation: A Balanced LoCoCV Study on the MAMA-MIA Dataset.Diagnostics (Basel, Switzerland) · 2026Article
- Radiomics and Deep Learning: Bridging Breast Cancer Imaging Phenotypes and Genomic Heterogeneity.Breast cancer (Dove Medical Press) · 2026Review
- Enhancing breast magnetic resonance imaging segmentation with a federated semi-supervised approach.Scientific reports · 2025Article
- Explainable Radiomics-Based Model for Automatic Image Quality Assessment in Breast Cancer DCE MRI Data.Journal of imaging · 2025Article
- Federated nnU-Net for privacy-preserving medical image segmentation.Scientific reports · 2025Article
- Segmentation variability and radiomics stability for predicting triple-negative breast cancer subtype using magnetic resonance imaging.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Noninvasive prediction of microsatellite instability in stage II/III rectal cancer using dynamic contrast-enhanced magnetic resonance imaging radiomics.World journal of gastrointestinal oncology · 2025Article
- FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare.BMJ (Clinical research ed.) · 2025Article
Corrections and comments
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Authors and funding
33 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a multicenter dataset of 1506 pre-treatment T1-weighted dynamic contrast-enhanced MRI cases, including expert annotations of primary tumors and non-mass-enhanced regions. The dataset integrates imaging data from four collections in The Cancer Imaging Archive (TCIA), where only 163 cases with expert segmentations were initially available. To facilitate the annotation process, a deep learning model was trained to produce preliminary segmentations for the remaining cases. These were subsequently corrected and verified by 16 breast cancer experts (averaging 9 years of experience), creating a fully annotated dataset. Additionally, the dataset includes 49 harmonized clinical and demographic variables, as well as pre-trained weights for a baseline nnU-Net model trained on the annotated data. This resource addresses a critical gap in publicly available breast cancer datasets, enabling the development, validation, and benchmarking of advanced deep learning models, thus driving progress in breast cancer diagnostics, treatment response prediction, and personalized care.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.