Evidence map›Paper›PMID 40138607›Full record

ArticleJCO clinical cancer informatics2025

Risk Stratification for Sentinel Lymph Node Positivity in Older Women With Early-Stage Estrogen Receptor-Positive/Human Epidermal Growth Factor Receptor 2 Neu-Negative Invasive Breast Cancer.

Ton Wang, Drew Neish, Samantha M Thomas, Astrid Botty van den Bruele, Laura H Rosenberger, Akiko Chiba, Kendra J Modell Parrish, Maggie L DiNome, Lesly A Dossett, Charles D Scales and 3 more

Abstract read
In one paragraph

Article in JCO clinical cancer informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

13 authors.

Ton WangDepartment of Surgery, Duke University Medical Center, Durham, NC.ORCID 0000-0003-0437-3771
Drew NeishDuke Cancer Institute, Duke University, Durham, NC.
Samantha M ThomasDuke Cancer Institute, Duke University, Durham, NC.ORCID 0000-0002-1561-9303
Astrid Botty van den BrueleDepartment of Surgery, Duke University Medical Center, Durham, NC.ORCID 0000-0002-0621-980X
Laura H RosenbergerDepartment of Surgery, Duke University Medical Center, Durham, NC.ORCID 0000-0002-6829-6747
Akiko ChibaDepartment of Surgery, Duke University Medical Center, Durham, NC.ORCID 0000-0003-0303-2799
Kendra J Modell ParrishDepartment of Surgery, Duke University Medical Center, Durham, NC.
Maggie L DiNomeDepartment of Surgery, Duke University Medical Center, Durham, NC.ORCID 0000-0002-1926-292X
Lesly A DossettDepartment of Surgery, Michigan Medicine, Ann Arbor, MI.ORCID 0000-0001-8060-1073
Charles D ScalesDepartment of Urology, Duke University Medical Center, Durham, NC.ORCID 0000-0002-7887-0204
Leah L ZulligDepartment of Population Health Sciences, Duke University Medical Center, Durham, NC.ORCID 0000-0002-6638-409X
E Shelley HwangDepartment of Surgery, Duke University Medical Center, Durham, NC.ORCID 0000-0003-0204-2012
Jennifer K PlichtaDepartment of Surgery, Duke University Medical Center, Durham, NC.ORCID 0000-0002-7411-0558

Funding

Women's Cancer Research ProgramP30CA014236 · NCI · DUKE UNIVERSITY · PI JOHN N. NORTON · 1985 to 2026
$174.8M
Building Interdisciplinary Research Careers in Women's Health (K12) - Administrative SupplementK12AR084231 · NIAMS · DUKE UNIVERSITY · PI Cindy Amundsen · 2023 to 2026
$2.5M
NCI NIH HHS P30 CA014236NIAMS NIH HHS K12 AR084231
6 · The paper itself

Abstract

purposeGuidelines recommend omission of sentinel lymph node biopsy (SLNB) for axillary staging in select patients age 70 years and older with early-stage estrogen receptor-positive (ER+), human epidermal growth factor receptor 2 neu-negative (HER2-) invasive breast cancers (BCs). However, many women meeting criteria for SLNB omission continue to receive this procedure. This study aims to stratify patients into risk cohorts for nodal positivity that can be incorporated into deimplementation strategies to reduce low-value SLNB procedures.

methodsA retrospective cohort analysis using the National Cancer Database was performed on patients age 70 years and older with ER+/HER2-, cT1-2, cN0, cM0 BC who underwent breast surgery from 2018 to 2021. Patients who received neoadjuvant therapies were excluded. Recursive partitioning analysis (RPA) was used to develop two models to estimate nodal positivity: (1) a clinical model for preoperative use to decide whether to perform SLNB and (2) a pathologic model for postoperative use to guide adjuvant decisions in cases of SLNB omission.

resultsThe study included 68,867 patients who received SLNB; 13.4% had a tumor-involved lymph node. RPA on the basis of clinical covariates demonstrated <8% risk of nodal positivity for patients with cT1mi-cT1b and grade 1-2 tumors. RPA on the basis of pathologic covariates found <10% risk of nodal positivity for patients with pT1 tumors without lymphovascular invasion (LVI). Patients with cT2 or pT2 without LVI and nonductal/nonlobular histology had <5% risk of nodal positivity.

conclusionThis study demonstrates a low risk of nodal positivity for patients with cT1 or pT1 tumors. Our RPA-defined subgroups offer a novel approach to predict nodal positivity in patients age 70 years and older with early-stage, ER+/HER2- invasive BC that can be incorporated in deimplementation strategies to reduce low-value axillary surgery.

Indexed as

Breast NeoplasmsErb-b2 Receptor Tyrosine KinasesReceptors, EstrogenSentinel Lymph NodeSentinel Lymph Node BiopsyAgedAged, 80 and overFemaleHumansLymphatic MetastasisNeoplasm InvasivenessNeoplasm StagingRetrospective StudiesRisk AssessmentERBB2 protein, humanErb-b2 Receptor Tyrosine KinasesReceptors, Estrogen

Identifiers

PMID40138607
PMCPMC12087273

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Registered trials

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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.