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Garrucho, L.* ; Kushibar, K.* ; Reidel, C.A.* ; Joshi, S.* ; Osuala, R. ; Tsirikoglou, A.* ; Bobowicz, M.* ; Del Riego, J.* ; Catanese, A.* ; Gwoździewicz, K.* ; Cosaka, M.L.* ; Abo-Elhoda, P.M.* ; Tantawy, S.W.* ; Sakrana, S.S.* ; Shawky-Abdelfatah, N.O.* ; Salem, A.M.A.* ; Kozana, A.* ; Divjak, E.* ; Ivanac, G.* ; Nikiforaki, K.* ; Klontzas, M.E.* ; García-Dosdá, R.* ; Gulsun-Akpinar, M.* ; Lafcı, O.* ; Mann, R.M.* ; Martín-Isla, C.* ; Prior, F.* ; Marias, K.* ; Starmans, M.P.A.* ; Strand, F.* ; Diaz, O.* ; Igual, L.* ; Lekadir, K.*

A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations.

Sci. Data 12:453 (2025)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Neoadjuvant Chemotherapy; Information; Prediction
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 2052-4463
e-ISSN 2052-4463
Zeitschrift Scientific Data
Quellenangaben Band: 12, Heft: 1, Seiten: , Artikelnummer: 453 Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-507100-001
Förderungen Juan de la Cierva fellowship
Ministry of Science and Innovation of Spain
European Union
DOI 10.1038/s41597-025-04707-4
WOS ID 001449486500003
Scopus ID 105000828732
PubMed ID 40108146
Erfassungsdatum 2025-05-09
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