Evidence map›Paper›PMID 42104431›Full record

Observational studyBreast cancer research : BCR2026

Leveraging immune and clinicopathological profiles with machine learning to predict axillary lymph node metastasis in breast cancer patients.

Alba Fischer-Carles, Alessio Fiorin, Carlos López Pablo, Laia Reverté, Esther Sauras-Colón, Noèlia Gallardo-Borràs, Marylène Lejeune, Mikel Relloso Ortiz de Uriarte, Tábata Sánchez-Alcántara, Maria Ballester-Navarro and 6 more

Abstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Breast cancer research : BCR, 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

16 authors.

Alba Fischer-CarlesOncological Pathology and Bioinformatics Research Group, Institut de Recerca Biomèdica Catalunya Sud (IRB CatSud), Tortosa, Spain.
Alessio FiorinOncological Pathology and Bioinformatics Research Group, Institut de Recerca Biomèdica Catalunya Sud (IRB CatSud), Tortosa, Spain.
Carlos López PabloOncological Pathology and Bioinformatics Research Group, Institut de Recerca Biomèdica Catalunya Sud (IRB CatSud), Tortosa, Spain. clopezp.ebre.ics@gencat.cat.
Laia RevertéOncological Pathology and Bioinformatics Research Group, Institut de Recerca Biomèdica Catalunya Sud (IRB CatSud), Tortosa, Spain.
Esther Sauras-ColónOncological Pathology and Bioinformatics Research Group, Institut de Recerca Biomèdica Catalunya Sud (IRB CatSud), Tortosa, Spain.
Noèlia Gallardo-BorràsOncological Pathology and Bioinformatics Research Group, Institut de Recerca Biomèdica Catalunya Sud (IRB CatSud), Tortosa, Spain.
Marylène LejeuneOncological Pathology and Bioinformatics Research Group, Institut de Recerca Biomèdica Catalunya Sud (IRB CatSud), Tortosa, Spain.
Mikel Relloso Ortiz de UriarteOncological Pathology and Bioinformatics Research Group, Institut de Recerca Biomèdica Catalunya Sud (IRB CatSud), Tortosa, Spain.
Tábata Sánchez-AlcántaraOncological Pathology and Bioinformatics Research Group, Institut de Recerca Biomèdica Catalunya Sud (IRB CatSud), Tortosa, Spain.
Maria Ballester-NavarroOncological Pathology and Bioinformatics Research Group, Institut de Recerca Biomèdica Catalunya Sud (IRB CatSud), Tortosa, Spain.
Ramon Bosch PríncepOncological Pathology and Bioinformatics Research Group, Institut de Recerca Biomèdica Catalunya Sud (IRB CatSud), Tortosa, Spain.
Laia Adalid-LlansaOncological Pathology and Bioinformatics Research Group, Institut de Recerca Biomèdica Catalunya Sud (IRB CatSud), Tortosa, Spain.
Elena GoydaOncological Pathology and Bioinformatics Research Group, Institut de Recerca Biomèdica Catalunya Sud (IRB CatSud), Tortosa, Spain.
Daniel Mata CanoOncological Pathology and Bioinformatics Research Group, Institut de Recerca Biomèdica Catalunya Sud (IRB CatSud), Tortosa, Spain.
Jérôme NoaillyDepartment of Engineering, BCN MedTech, Universitat Pompeu Fabra, Barcelona, Spain.
Gemma PiellaDepartment of Engineering, BCN MedTech, Universitat Pompeu Fabra, Barcelona, Spain.

Funding

HORIZON EUROPE Marie Sklodowska-Curie Actions 101073222Ministerio de Ciencia e Innovación TED2021-130081B-C22
6 · The paper itself

Abstract

backgroundBreast cancer is the leading cause of cancer-related death in women, with mortality increasing when tumor cells spread to nearby lymph nodes, particularly the axillary lymph nodes (ALNs). Although several studies predict patients with ALN metastasis at diagnosis (pdALN

methodsTwo datasets of luminal breast cancer patients diagnosed between 1995 and 2008 were used: Dataset 1 involved 83 women (42 pdALN

resultsModel 2 outperformed Models 1 and 3, despite the larger cohort on which Model 3 was developed. This highlights the crucial role of the immune response in breast cancer progression. Model 4 achieved a median ROC AUC of 0.84, a median accuracy of 0.76, and a median recall of 0.75. Remarkably, nine of the ten predictive features were immune populations. The intratumoral follicular dendritic cell marker CD21

conclusionsThis study not only identifies promising biomarker candidates but also highlights the importance of including mechanistic features, such as mediating inflammation, in breast cancer patient stratification.

Indexed as

Breast NeoplasmsLymphatic MetastasisLymph NodesMachine LearningAdultAgedAxillaBiomarkers, TumorClassification AlgorithmsFemaleHumansMiddle AgedPrediction AlgorithmsPredictive Learning ModelsPrognosisRandom ForestBiomarkers, TumorAxillary lymph node metastasisBreast cancerClinicopathological dataImmune responseMachine learning

Identifiers

PMID42104431
PMCPMC13321410

What OpenQuestion holds

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LicenceCC BY
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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.