DBT-2026, a de-identified publicly available dataset of digital breast tomosynthesis exams with ground truth biopsies
Abstract
Abstract Digital breast tomosynthesis (DBT) is a powerful imaging modality that allows for improved lesion visibility, characterization, and localization compared to conventional two-dimensional digital mammography. DBT has been increasingly adopted in screening and diagnostic settings globally, particularly for women with dense breast tissue where tissue overlap presents a significant diagnostic challenge. Here we describe DBT-2026, a real world imaging dataset with 558 DBT exams from 558 patients with breast imaging reporting and data system (BI-RADS) scores of 0, 1, or 2. Each case contains one DBT examination in combination with expert annotations and free-text radiology reports that describe the radiological findings, produced in routine clinical practice. To protect patient privacy, all images and reports have been de-identified. The dataset is made freely available to researchers for non-commercial projects to facilitate and encourage research in breast cancer imaging.
Fields of Study & Subjects
Digital Radiography and Breast Imaging
AI in cancer detection
Radiomics and Machine Learning in Medical Imaging
Publication details
- Journal / source
- medRxiv
- Publication date
- 4 March 2026
- Language
- EN
- Document type
- Preprint