Evidence map›Paper›PMID 40427095›Full record

ArticleCancers2025

Predictors of High-Burden Residual Axillary Disease After Neoadjuvant Therapy in Breast Cancer.

Damiano Gentile, Jacopo Canzian, Erika Barbieri, Andrea Sagona, Simone Di Maria Grimaldi, Corrado Tinterri

Abstract read
In one paragraph

Article in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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.

Damiano GentileBreast Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy.ORCID 0000-0002-8861-5223
Jacopo CanzianDepartment of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20090 Milan, Italy.ORCID 0009-0008-0883-0582
Erika BarbieriBreast Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy.
Andrea SagonaBreast Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy.
Simone Di Maria GrimaldiBreast Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy.ORCID 0000-0002-1014-0141
Corrado TinterriBreast Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNeoadjuvant therapy (NAT) plays a crucial role in breast cancer (BC) management by enabling tumor and nodal downstaging. While axillary lymph node dissection (ALND) remains the standard for patients with residual nodal disease after NAT, its prognostic benefit is debated. Identifying predictors of high-burden residual axillary disease may guide treatment intensification and surgical de-escalation strategies.

methodsWe retrospectively analyzed 262 BC patients treated with NAT followed by ALND between 2006 and 2023. Patients were stratified into low- (ypN0-mi-1) and high-burden (ypN2-3) residual axillary disease groups. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of high-burden residual disease.

resultsHigh-burden residual axillary disease was observed in 35.9% of patients. Baseline cN+ status (OR = 7.697,

conclusionsIdentifying patients at risk of high-burden residual axillary disease is essential to optimize neoadjuvant strategies. Increasing axillary pathological complete response may reduce the need for ALND, minimizing surgical morbidity without compromising oncological outcomes.

Indexed as

axillary burden predictionaxillary lymph node dissectionbreast cancerneoadjuvant therapysentinel lymph node biopsy

Identifiers

PMID40427095
PMCPMC12110274

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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.