{"title":"The clinical value of radiomics models based on multi-parameter MRI features in evaluating the different expression status of HER2 in breast cancer.","authors":"Tingting Liu, Jialu Lin, Jiulou Zhang, Jianjuan Lou, Qigui Zou, Siqi Wang, Cong Wang, Yanni Jiang","doi":"10.1177/02841851251319110","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Accurate preoperative non-invasive assessment of HER2 expression in breast cancer is crucial for personalized treatment and prognostic stratification.</p><p><strong>Purpose: </strong>To evaluate the effectiveness of radiomics models based on multi-parametric magnetic resonance imaging (MRI) in distinguishing HER2 expression status in invasive breast cancer.</p><p><strong>Material and methods: </strong>We conducted a retrospective analysis of baseline MRI scans and clinical data from 400 patients with breast cancer between January 2018 and December 2019. Two-dimensional regions of interest were manually segmented on the maximum tumor images obtained from turbo inversion recovery magnitude (TIRM), dynamic contrast-enhanced magnetic resonance imaging phase 2 (DCE2), dynamic contrast-enhanced magnetic resonance imaging phase 4 (DCE4), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) sequences using ITK-SNAP software. Features were extracted and screened for dimensionality reduction. Logistic regression models were developed to predict HER2 expression status.</p><p><strong>Results: </strong>In distinguishing HER2-overexpression from non-HER2-overexpression, the DCE2 model outperformed other single-parameter models, with areas under the curve (AUCs) of 0.91 (training) and 0.88 (test). Combination models with DCE features showed significantly improved performance (<i>P</i> ≤ 0.001). The multiparameter model achieved the highest AUCs of 0.93 (training) and 0.91 (test). In distinguishing HER2-low from HER2-zero, the TIRM model performed best among single-parameter models, with AUCs of 0.80 (training) and 0.72 (test). The multiparameter model further enhanced prediction, yielding an AUC of 0.83 (test).</p><p><strong>Conclusion: </strong>Radiomics models based on multi-parametric MRI features demonstrated strong clinical utility in assessing HER2 expression status in invasive breast cancer, particularly in identifying HER2-overexpression and HER2-low expression subtypes.</p>","PeriodicalId":7143,"journal":{"name":"Acta radiologica","volume":" ","pages":"2841851251319110"},"PeriodicalIF":1.1000,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta radiologica","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/02841851251319110","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Accurate preoperative non-invasive assessment of HER2 expression in breast cancer is crucial for personalized treatment and prognostic stratification.
Purpose: To evaluate the effectiveness of radiomics models based on multi-parametric magnetic resonance imaging (MRI) in distinguishing HER2 expression status in invasive breast cancer.
Material and methods: We conducted a retrospective analysis of baseline MRI scans and clinical data from 400 patients with breast cancer between January 2018 and December 2019. Two-dimensional regions of interest were manually segmented on the maximum tumor images obtained from turbo inversion recovery magnitude (TIRM), dynamic contrast-enhanced magnetic resonance imaging phase 2 (DCE2), dynamic contrast-enhanced magnetic resonance imaging phase 4 (DCE4), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) sequences using ITK-SNAP software. Features were extracted and screened for dimensionality reduction. Logistic regression models were developed to predict HER2 expression status.
Results: In distinguishing HER2-overexpression from non-HER2-overexpression, the DCE2 model outperformed other single-parameter models, with areas under the curve (AUCs) of 0.91 (training) and 0.88 (test). Combination models with DCE features showed significantly improved performance (P ≤ 0.001). The multiparameter model achieved the highest AUCs of 0.93 (training) and 0.91 (test). In distinguishing HER2-low from HER2-zero, the TIRM model performed best among single-parameter models, with AUCs of 0.80 (training) and 0.72 (test). The multiparameter model further enhanced prediction, yielding an AUC of 0.83 (test).
Conclusion: Radiomics models based on multi-parametric MRI features demonstrated strong clinical utility in assessing HER2 expression status in invasive breast cancer, particularly in identifying HER2-overexpression and HER2-low expression subtypes.
期刊介绍:
Acta Radiologica publishes articles on all aspects of radiology, from clinical radiology to experimental work. It is known for articles based on experimental work and contrast media research, giving priority to scientific original papers. The distinguished international editorial board also invite review articles, short communications and technical and instrumental notes.