The clinical value of radiomics models based on multi-parameter MRI features in evaluating the different expression status of HER2 in breast cancer.

IF 1.1 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Tingting Liu, Jialu Lin, Jiulou Zhang, Jianjuan Lou, Qigui Zou, Siqi Wang, Cong Wang, Yanni Jiang
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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.

背景:准确的乳腺癌术前无创评估对于个性化治疗和预后分层至关重要:目的:评估基于多参数磁共振成像(MRI)的放射组学模型在区分浸润性乳腺癌HER2表达状态方面的有效性:我们对2018年1月至2019年12月期间400名乳腺癌患者的基线MRI扫描和临床数据进行了回顾性分析。使用 ITK-SNAP 软件对涡轮反转恢复幅度(TIRM)、动态对比增强磁共振成像 2 期(DCE2)、动态对比增强磁共振成像 4 期(DCE4)、扩散加权成像(DWI)和表观扩散系数(ADC)序列获得的最大肿瘤图像上的二维感兴趣区进行手动分割。提取特征并进行降维筛选。建立逻辑回归模型来预测 HER2 表达状态:在区分 HER2 表达与非 HER2 表达方面,DCE2 模型优于其他单参数模型,曲线下面积(AUC)分别为 0.91(训练)和 0.88(测试)。带有 DCE 特征的组合模型的性能明显提高(P ≤ 0.001)。多参数模型的 AUC 最高,分别为 0.93(训练)和 0.91(测试)。在区分 HER2-低和 HER2-零时,TIRM 模型在单参数模型中表现最佳,AUC 分别为 0.80(训练)和 0.72(测试)。多参数模型进一步提高了预测效果,其AUC为0.83(测试):结论:基于多参数磁共振成像特征的放射组学模型在评估浸润性乳腺癌的HER2表达状态方面具有很强的临床实用性,尤其是在识别HER2-表达缺失和HER2-低表达亚型方面。
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来源期刊
Acta radiologica
Acta radiologica 医学-核医学
CiteScore
2.70
自引率
0.00%
发文量
170
审稿时长
3-8 weeks
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.
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