Evidence map›Paper›PMID 41331937›Full record

ArticleEuropean journal of medical research2025

A population-based analysis of a risk stratification system for predicting radiotherapy benefits in invasive breast carcinoma of no special type with medullary pattern.

Jian Zhang, Lizhao Wang, Heyan Chen, Yu Yan, Guanqun Ge, Ke Wang, Jianjun He

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Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Jian Zhang *Department of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, Shannxi, China.
Lizhao Wang *Department of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, Shannxi, China.
Heyan Chen *Department of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, Shannxi, China.
Yu YanDepartment of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, Shannxi, China.
Guanqun GeDepartment of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, Shannxi, China.
Ke WangDepartment of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, Shannxi, China. xjtu_wet@163.com.
Jianjun HeDepartment of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, Shannxi, China. chinahjj09@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInvasive breast carcinoma of no special type (IBC-NST) with medullary pattern is a rare histological subtype, accounting for 3-5% of breast malignancies. Its distinct clinicopathological features and uncertain benefit from radiotherapy (RT) pose unique therapeutic challenges. Current guidelines extrapolate treatment protocols from invasive ductal carcinoma, yet prognostic heterogeneity and the absence of validated biomarkers underscore the need for precision stratification tools to guide RT decisions.

methodsUsing data from the SEER database (2010-2018), we conducted univariate and multivariate Cox regression analyses to develop a prognostic stratification model and stratified the whole cohort into different risk groups to determine the optimal candidates to benefit from radiotherapy. The accuracy of the nomogram was evaluated by discrimination and calibration evaluation.

resultsAmong 667 eligible patients, 535 were allocated to the training set and 132 to the validation set (8:2 ratio). Five independent prognostic factors were identified: age, T stage, N stage, molecular subtype, and chemotherapy status. These were incorporated into a nomogram predicting 3- and 5-year overall survival (OS). Using an optimal cutoff, patients were stratified into low- and high-risk groups. Radiotherapy significantly improved OS in low-risk patients compared to those not receiving RT (P = 0.017), but not in high-risk patients (P = 0.47). The model demonstrated strong predictive performance, with 3- and 5-year AUC values of 0.777 and 0.775 in the training set, and 0.747 and 0.712 in the validation set. Calibration curves indicated close agreement between predicted and observed outcomes.

conclusionWe developed and validated a prognostic nomogram for IBC-NST with medullary pattern that accurately stratifies patients by risk. Our findings indicate that radiotherapy is associated with a survival benefit primarily in low-risk patients, offering a practical tool to optimize RT personalization and avoid overtreatment in high-risk individuals.

Indexed as

Breast NeoplasmsCarcinoma, Ductal, BreastAdultAgedFemaleHumansMiddle AgedNomogramsPrognosisRisk AssessmentSEER ProgramInvasive breast carcinomaMedullary patternNomogramOverall survivalRadiotherapyRisk stratification

Identifiers

PMID41331937
PMCPMC12777346

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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.