Evidence map›Paper›PMID 41590373›Full record

ArticleCurrent oncology (Toronto, Ont.)2026

Machine Learning Model Based on Multiparametric MRI for Distinguishing HER2 Expression Level in Breast Cancer.

Yongxin Chen, Weifeng Liu, Wenjie Tang, Qingcong Kong, Siyi Chen, Shuang Liu, Liwen Pan, Yuan Guo, Xinqing Jiang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Current oncology (Toronto, Ont.), 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. Article
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.

Yongxin ChenThe First School of Clinical Medicine, Jinan University, Guangzhou 510632, China.
Weifeng LiuDepartment of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou 510180, China.
Wenjie TangDepartment of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou 510180, China.
Qingcong KongDepartment of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.
Siyi ChenDepartment of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou 510180, China.
Shuang LiuDepartment of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou 510180, China.
Liwen PanDepartment of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou 510180, China.
Yuan GuoDepartment of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou 510180, China.
Xinqing JiangThe First School of Clinical Medicine, Jinan University, Guangzhou 510632, China.

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2022A1515110792Basic and Applied Basic Research Foundation of Guangdong Province 2023A1515220097Basic and Applied Basic Research Foundation of Guangdong Province 2024A1515010653National Natural Science Foundation of China 82271938National Natural Science Foundation of China 82302314Science and technology Projects in Guangzhou 2023A04J1275Science and technology Projects in Guangzhou 2024A03J1030Science and technology Projects in Guangzhou 2025A03J4162Science and technology Projects in Guangzhou 2025A03J4163
6 · The paper itself

Abstract

This study aimed to develop machine learning models based on conventional MRI features to classify HER2 expression levels in invasive breast cancer and explore their association with disease-free survival (DFS). A total of 678 patients from two centers were included, with Center 1 divided into training and internal test sets and Center 2 serving as an external test set. Random Forest models were trained to distinguish HER2-positive vs. HER2-negative (Task 1) and HER2-low vs. HER2-zero tumors (Task 2) using BI-RADS-based MRI features. SHapley Additive exPlanations were applied to rank feature importance, assist feature selection, and enhance model interpretability. DFS was analyzed using Kaplan-Meier curves and log-rank tests. In Task 1, key features included tumor size, axillary lymph nodes, fibroglandular tissue, peritumoral edema, and multifocal, achieving AUCs of 0.75 and 0.73 in the internal and external test sets, respectively. In Task 2, tumor size, peritumoral edema, and multifocal yielded AUCs of 0.73 and 0.72, respectively. Higher task-specific model scores were associated with shorter DFS in Task 1 (

Indexed as

Breast NeoplasmsErb-b2 Receptor Tyrosine KinasesMachine LearningMultiparametric Magnetic Resonance ImagingClassification AlgorithmsFemaleHumansMiddle AgedPredictive Learning ModelsRandom ForestERBB2 protein, humanErb-b2 Receptor Tyrosine Kinasesbreast cancerhuman epidermal growth factor receptor 2machine learningmultiparametric MRISHapley Additive exPlanation

Identifiers

PMID41590373
PMCPMC12839683

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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.