Evidence map›Paper›PMID 41315494›Full record

ArticleScientific data2025

Histological Hyperspectral Breast Cancer Recurrence Database (HistologyHSI-BC Recurrence).

Laura Quintana-Quintana, Esther Sauras-Colón, Alessio Fiorin, Javier Santana-Nunez, Samuel Ortega, Noèlia Gallardo-Borràs, Alba Fischer-Carles, Tábata Sánchez-Alcántara, Himar Fabelo, Laia Adalid-Llansa and 5 more

Abstract readDataset
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Laura Quintana-Quintana *Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain.ORCID 0000-0003-1154-6490
Esther Sauras-Colón *Department of Pathology, Hospital de Tortosa Verge de la Cinta, Institut Català de la Salut, Tortosa, Spain. esauras.ebre.ics@gencat.cat.ORCID 0000-0003-1649-938X
Alessio FiorinDepartment of Pathology, Hospital de Tortosa Verge de la Cinta, Institut Català de la Salut, Tortosa, Spain.
Javier Santana-NunezInstitute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain.
Samuel OrtegaInstitute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain.ORCID 0000-0002-7519-954X
Noèlia Gallardo-BorràsDepartment of Pathology, Hospital de Tortosa Verge de la Cinta, Institut Català de la Salut, Tortosa, Spain.
Alba Fischer-CarlesDepartment of Pathology, Hospital de Tortosa Verge de la Cinta, Institut Català de la Salut, Tortosa, Spain.
Tábata Sánchez-AlcántaraDepartment of Pathology, Hospital de Tortosa Verge de la Cinta, Institut Català de la Salut, Tortosa, Spain.
Himar FabeloInstitute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain.ORCID 0000-0002-9794-490X
Laia Adalid-LlansaDepartment of Pathology, Hospital de Tortosa Verge de la Cinta, Institut Català de la Salut, Tortosa, Spain.
Daniel Mata-CanoDepartment of Pathology, Hospital de Tortosa Verge de la Cinta, Institut Català de la Salut, Tortosa, Spain.
Ramon Bosch-PríncepDepartment of Pathology, Hospital de Tortosa Verge de la Cinta, Institut Català de la Salut, Tortosa, Spain.
Marylène LejeuneDepartment of Pathology, Hospital de Tortosa Verge de la Cinta, Institut Català de la Salut, Tortosa, Spain.
Gustavo M CallicoInstitute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain.ORCID 0000-0002-3784-5504
Carlos López-PabloDepartment of Pathology, Hospital de Tortosa Verge de la Cinta, Institut Català de la Salut, Tortosa, Spain.

Funding

Agencia Canaria de Investigación, Innovación y Sociedad de la Información (ACIISI) TESIS2021010084Fundación Científica Asociación Española Contra el Cáncer (Scientific Foundation, Spanish Association Against Cancer) PRDLP246561SANTGovernment of Catalonia | Agència de Gestió d'Ajuts Universitaris i de Recerca (Agency for Management of University and Research Grants) 2022DI057
6 · The paper itself

Abstract

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.

Indexed as

Breast NeoplasmsDatabases, FactualNeoplasm Recurrence, LocalFemaleHumans

Identifiers

PMID41315494
PMCPMC12663337

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.