Gland surgery
August 25, 2026 · via Europe PMC
Development and validation of a SEER-derived prognostic nomogram for non-metastatic Asian breast cancer patients.
Xu F, Zhang YL, Cheng JM, Sun HW, Du SS, Hua X.
AI summary — for doctors, generated from the abstract only
A SEER-derived nomogram incorporating age, grade, marital status, T and N stage, subtype, radiotherapy, and chemotherapy predicted overall survival in non-metastatic Asian American breast cancer patients better than TNM staging.
What was studied
Retrospective SEER analysis of 38,269 Asian American women with non-metastatic breast cancer (2000-2021), split 70/30 into training and validation cohorts to develop a Cox-based prognostic nomogram.
Key findings
- Independent predictors: age, grade, marital status, T stage, N stage, subtype, radiotherapy, chemotherapy
- C-index 0.794 (training) and 0.811 (validation), outperforming TNM staging
- Good calibration and clinical applicability on decision curve analysis
Clinical relevance
This tool may support individualized prognostic counseling in Asian American breast cancer patients, but it needs external validation and treatment variables should not be read as causal effects.
Who should care
Limitations
Retrospective registry data without detailed treatment, comorbidity, or Asian subgroup information; internal validation only.
A population-specific survival calculator for Asian American women with breast cancer outperforms stage alone but needs outside testing.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Background
Breast cancer (BC) is the most prevalent cancer among American women. Asian Americans are confronting a rapidly increasing incidence, facing unique challenges within the healthcare system that contribute to disparities in cancer outcomes. This study aimed to develop and validate a prognostic nomogram for predicting long-term survival in Asian BC patients utilizing the Surveillance, Epidemiology, and End Results (SEER) database.
Methods
Asian female BC patients diagnosed between 2000 and 2021 were identified from the SEER database. Following the application of inclusion and exclusion criteria, 38,269 patients were included and randomly divided into training (70%) and validation (30%) cohorts. Univariate and multivariate Cox regression analyses were conducted to identify independent prognostic factors, which were incorporated into a nomogram. The model's discriminative ability, calibration, and clinical utility were evaluated.
Results
Independent predictors of overall survival included age, tumor grade, marital status, T stage, N stage, subtype, radiotherapy, and chemotherapy. The nomogram demonstrated strong discriminative performance, with concordance indices of 0.794 and 0.811 in the training and validation cohorts, respectively, demonstrating better discriminative performance than the traditional tumor-node-metastasis (TNM) staging system. Calibration plots and decision curve analysis confirmed excellent calibration and clinical applicability.
Conclusions
This study presents a prognostic nomogram developed and validated exclusively in a large cohort of non-metastatic Asian American BC patients from the SEER database, incorporating modern molecular subtypes to improve prognostic stratification for this understudied population. By integrating readily available clinicopathological factors, the nomogram provides personalized survival forecasts, serving as a valuable tool for individualized management in this unique population. Further external validation and refinements are warranted to enhance its clinical utility.
DOI
10.21037/gs-2026-0203
PMID
42725256
PMCID
PMC13561593
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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