Evidence map›Paper›PMID 41774882›Full record

ArticleJCO clinical cancer informatics2026

Early-Stage Breast Cancer in Women Younger Than 50 Years: Comparing American Joint Committee on Cancer Anatomic and Prognostic Stages With Partitioning Around Medoids Clusters in SEER Data.

Suvd Zulbayar, Jennifer Brooks, Arian Aminoleslami, Shana Kim, Renzo Jose Carlos Calderon Anyosa, Feifan Xiang, Julia Knight, Andrea Eisen, Geoffrey M Anderson

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

Article in JCO clinical cancer informatics, 2026. 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

9 authors.

Suvd ZulbayarInstitute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.
Jennifer BrooksDalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.ORCID 0000-0001-7574-4256
Arian AminoleslamiInstitute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-9221-1897
Shana KimDalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.ORCID 0009-0008-8816-5518
Renzo Jose Carlos Calderon AnyosaInstitute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.ORCID 0000-0003-4840-2191
Feifan XiangInstitute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.
Julia KnightDalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Andrea EisenFaculty of Health Sciences, McMaster University, Hamilton, ON, Canada.
Geoffrey M AndersonInstitute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.ORCID 0000-0003-0124-1343

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeEarly-stage breast cancer (ESBC) in women younger than 50 years often presents with tumor features, including grade and hormone receptor and human epidermal growth factor receptor 2 (HER2) status different from older women. Machine learning clustering techniques can reveal underlying patterns in the inter-relationships of these features and provide novel insights to inform and guide decision making by patients and providers.

methodsPartitioning around medoids (PAM) was applied to SEER data from 67,746 women age 18-49 years diagnosed with ESBC. PAM clustering based on tumor size (T), nodal status (N), grade, and receptor status identified 10 distinct clusters. The PAM clusters and American Joint Committee on Cancer (AJCC) anatomic and prognostic stages were compared in terms of their tumor features and their association with chemotherapy and survival.

resultsAJCC anatomic and prognostic stages are primarily defined by T and N. PAM clusters were primarily defined by receptor status and grade. PAM clusters align closely with luminal A, luminal B, triple-negative, or HER2-overexpressing treatment-related subtypes. PAM clusters better discriminated chemotherapy treatment, with C-statistic 0.839 (95% CI, 0.836 to 0.842), than either anatomic, with C-statistic 0.770 (95% CI, 0.767 to 0.773), or prognostic staging, with C-statistic 0.796 (95% CI, 0.794 to 0.800). PAM clusters were better predictors of 5-year overall survival, with C-statistic 0.733 (95% CI, 0.727 to 0.739), than anatomic stages, with C-statistic 0.721 (95% CI, 0.715 to 728), but not as predictive as prognostic stages, with C-statistic 0.759 (95% CI, 0.753 to 0.764).

conclusionData-driven PAM clusters provide novel insights into the inter-relationship of tumor features and their association with hormonal, targeted, and chemotherapy treatment and with survival outcomes in women younger than 50 years with ESBC. An online application was created so that the PAM clusters could be used as alternatives or in addition to traditional AJCC staging to inform and guide patients and providers.

Indexed as

Breast NeoplasmsAdolescentAdultClustering AlgorithmsErb-b2 Receptor Tyrosine KinasesFemaleHumansMiddle AgedNeoplasm StagingPrognosisSEER ProgramUnited StatesYoung AdultErb-b2 Receptor Tyrosine Kinases

Identifiers

PMID41774882
PMCPMC12959580

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.