Evidence map›Paper›PMID 42587611›Full record

ArticleDiagnostics (Basel, Switzerland)2026

A Practical Machine Learning Model for Predicting Neoadjuvant Response in HER2-Positive Breast Cancer.

María Azmat, Lucía Graña-López, Manuel Fernández-Delgado, Eva Cernadas, Marcelino Maneiro, Cristina Núñez

Abstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2026. 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

6 authors.

María AzmatCentro Singular de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS), Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain.
Lucía Graña-LópezRadiology Department, Hospital Lucus Augusti Lugo, 27003 Lugo, Spain.ORCID 0000-0002-4809-4208
Manuel Fernández-DelgadoCentro Singular de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS), Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain.ORCID 0000-0001-5483-9424
Eva CernadasCentro Singular de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS), Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain.ORCID 0000-0002-1562-2553
Marcelino ManeiroInstitute for Research in Global Health and Sustainable Development, iTERRA, Inorganic Chemistry Department, Faculty of Sciences, Campus Terra, University of Santiago de Compostela, 27002 Lugo, Spain.ORCID 0000-0003-1258-3517
Cristina NúñezInstitute for Research in Global Health and Sustainable Development, iTERRA, Inorganic Chemistry Department, Faculty of Sciences, Campus Terra, University of Santiago de Compostela, 27002 Lugo, Spain.

Funding

Instituto de Salud Carlos III PI22/00025Xunta de Galicia 2022-2025 GRC GI-1636 (2022-PG053)Xunta de Galicia ED431G-2023/04
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

breast MRIclinical prediction modelHER2-positive breast cancermachine learningneoadjuvant therapypathological complete response

Identifiers

PMID42587611
PMCPMC13465032

What OpenQuestion holds

Textmetadata
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.