Evidence map›Paper›PMID 42835801›Full record

ArticleInternational journal of general medicine2026

MRI-Based Intratumoral and Multi-Range Peritumoral Radiomics for Predicting Axillary Lymph Node Metastasis in Breast Cancer.

Bingchen Chu, Tongyun Zhan, Xing Liu, Haiying Huang

Abstract read
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Article in International journal of general medicine, 2026. 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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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Bingchen ChuDepartment of Radiology, The First Affiliated Hospital of Anhui University of Science and Technology, Huainan, Anhui, 232000, People's Republic of China.
Tongyun ZhanDepartment of Radiology, The First Affiliated Hospital of Anhui University of Science and Technology, Huainan, Anhui, 232000, People's Republic of China.
Xing LiuDepartment of Radiology, The First Affiliated Hospital of Anhui University of Science and Technology, Huainan, Anhui, 232000, People's Republic of China.
Haiying HuangDepartment of Radiology, The First Affiliated Hospital of Anhui University of Science and Technology, Huainan, Anhui, 232000, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: To determine the best-performing peritumoral boundary among tested distances and evaluate the predictive value of preoperative multiparametric MRI radiomics for axillary lymph node metastasis (ALNM) in breast cancer. Methods: Clinical and imaging data of 273 patients from two centers were retrospectively analyzed. ROIs were manually delineated on Fs-T2WI and DCE-MRI images, and peritumoral regions were generated by expanding the tumor ROI by 3 mm, 5 mm, and 7 mm. Patients from Center 1 (n = 201) were randomly divided into training and internal validation set (7:3), while Center 2 (n = 72) served as the external validation set. LASSO regression and an SVM classifier were used to construct predictive models. A nomogram integrating the best-performing radiomic model with clinical parameters was developed. Model performance was assessed using AUC and DCA. Results: Maximum tumor diameter was identified as an independent risk factor for ALNM (OR = 4.042, 95% CI: 2.488-6.532; Conclusion: Among the tested distances, a 5 mm peritumoral margin provided the best predictive performance for ALNM. The multiparametric MRI radiomic model effectively predicts ALNM and offers a valuable non-invasive tool for preoperative assessment.

Indexed as

axillary lymph node metastasisbreast cancermagnetic resonance imagingperitumoral regionradiomics

Identifiers

PMID42835801
PMCPMC13637695

What Socratic holds

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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.