Histological Hyperspectral Breast Cancer Recurrence Database (HistologyHSI-BC Recurrence)
Metastasis occurs in nearly 1 out of 3 breast cancer (BC) patients and significantly reduces survival rates, particularly in cases of distant metastases. As most distant metastases develop after diagnosis (i.e., recurrence) and remain incurable, there is a critical need for prognostic biomarkers to assess recurrence risk. Multimodal data analysis has emerged as a promising approach to integrate diverse information, offering a more comprehensive perspective. This study introduces the Histology HSI-BC (hyperspectral imaging - breast cancer) Recurrence Database, the first publicly accessible multimodal database designed to advance BC distant recurrence prediction. The database comprises 47 histopathological whole-slide images, 677 hyperspectral (HS) images, and clinical and demographic data from 47 BC patients, of whom 22 (47%) experienced distant recurrence over a 12-year follow-up. Histopathological slides were digitized using a whole-slide scanner and annotated by expert pathologists, while HS images were acquired with an HS camera coupled to a bright-field microscope. This database provides a promising resource for studying BC recurrence prediction and personalized treatment strategies by integrating the aforementioned multimodal data.
Authors
- Samuel Ortega (ORCID: https://orcid.org/0000-0002-7519-954X)
- D. Mata Cano (ORCID: https://orcid.org/0000-0001-7543-4856)
- Gustavo M. Callicó (ORCID: https://orcid.org/0000-0002-3784-5504)
- Ramón Bosch (ORCID: https://orcid.org/0000-0003-4104-5515)
- Himar Fabelo (ORCID: https://orcid.org/0000-0002-9794-490X)
- Esther Sauras-Colón (ORCID: https://orcid.org/0000-0003-1649-938X)
- Noèlia Gallardo (ORCID: https://orcid.org/0000-0001-8537-4586)
- Laura Quintana (ORCID: https://orcid.org/0000-0003-1154-6490)
- Marylène Lejeune (ORCID: https://orcid.org/0000-0001-8441-9404)
- Alessio Fiorin (ORCID: https://orcid.org/0009-0002-0315-1085)
- Javier Santana-Nuñez (ORCID: https://orcid.org/0009-0000-5029-5239)
- Carlos López (ORCID: https://orcid.org/0000-0003-1248-3065)
- Laia Adalid-Llansa (ORCID: https://orcid.org/0000-0001-7543-9921)
- Alba Fischer-Carles (ORCID: https://orcid.org/0009-0005-7733-015X)
- Tábata Sánchez-Alcántara
Institutions
- Universidad de Las Palmas de Gran Canaria (ES)
- Universitat Pompeu Fabra (ES)
- Hospital Universitario de Gran Canaria Doctor Negrín (ES)
- Institut de Recerca Biomèdica Catalunya Sud (ES)
- Nofima (NO)
- Fundación Canaria de Investigación Sanitaria (ES)
- Hospital Universitari de Tortosa Verge de la Cinta (ES)
- Universitat Rovira i Virgili (ES)
Publication Details
- Journal
- Scientific Data
- Published
- 2025-11-28
- DOI
- https://doi.org/10.1038/s41597-025-06157-4
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Institut d'Investigació Sanitària Pere Virgili
- European Commission
- Generalitat de Catalunya
- Ministerio de Ciencia e Innovación
- Agencia Canaria de Investigación, Innovación y Sociedad de la Información
- Fundación Española para la Ciencia y la Tecnología
- Xarxa de Bancs de Tumors de Catalunya
- Agència de Gestió d'Ajuts Universitaris i de Recerca