Prediction of HER-2 expression status in breast cancer based on multi-parameter MRI intratumoral and peritumoral radiomics

IF 2.1 4区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Magnetic resonance imaging Pub Date : 2025-06-01 DOI:10.1016/j.mri.2025.110434

Mingtai Cao , Xinyi Liu , Airu Yang , Yuan Xu , Qian Zhang , Yuntai Cao

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引用次数: 0

Abstract

Background

This study aims to explore the value of multiparametric magnetic resonance imaging (MRI) techniques—dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging (DWI), and T2-weighted fat-suppressed imaging (T2WI)—in predicting human epidermal growth factor receptor 2 (HER-2) status in breast cancer by integrating intratumoral and peritumoral radiomics features to establish a multiparametric MRI intratumoral and peritumoral radiomics model.

Methods

A retrospective cohort of 266 female breast cancer patients was analyzed. Patients from Center 1 (n = 199) were divided into a training set (n = 140) and internal validation set (n = 59; 7:3 ratio), while Center 2 (n = 67) provided the external test set. Using 3D Slicer, tumor boundaries were manually segmented on T2WI, DWI, and DCE-MRI to define intratumoral volumes of interest (VOIs). These VOIs were expanded by 3 mm to generate peritumoral regions (VOI_Peri3mm). Radiomics features were extracted from both regions, optimized via feature selection, and used to train eight random forest (RF) models. Performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

Results

The multiparametric MRI intratumoral and peritumoral radiomics model (DWI_Peri3 + T2WI_Peri3 + DCE_Peri3_RF) demonstrated optimal HER-2 prediction, achieving area under the curve (AUC) values of 0.822 (95 % CI:0.755–0.889), 0.823 (0.714–0.932), and 0.813 (0.712–0.914) in the training, internal validation, and external test sets, respectively. It significantly outperformed single-parameter or single-region models and maintained cross-cohort consistency.

Conclusion

The intratumoral-peritumoral radiomics fusion model integrating DWI, T2WI, and DCE-MRI provides high diagnostic accuracy for HER-2 assessment, offering non-invasive biomarkers and enhancing precision in breast cancer management.

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基于多参数MRI肿瘤内和肿瘤周围放射组学预测HER-2在乳腺癌中的表达状况。

背景：本研究旨在探讨多参数磁共振成像（MRI）技术——动态对比增强MRI （DCE-MRI）、弥散加权成像（DWI）和t2加权脂肪抑制成像（T2WI）——通过整合瘤内和瘤周放射组学特征，建立多参数MRI瘤内和瘤周放射组学模型，预测乳腺癌中人表皮生长因子受体2 （HER-2）状态的价值。方法：对266例女性乳腺癌患者进行回顾性队列分析。中心1的患者（n = 199）分为训练集（n = 140）和内部验证集(n = 59；7:3比例)，中心2 （n = 67）提供外部测试集。使用3D切片机，在T2WI， DWI和DCE-MRI上手动分割肿瘤边界，以确定肿瘤内感兴趣的体积（VOIs）。这些voi被扩大3 mm以产生肿瘤周围区域（VOI_Peri3mm）。从两个区域提取放射组学特征，通过特征选择进行优化，并用于训练8个随机森林（RF）模型。采用受试者工作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）对治疗效果进行评价。结果：多参数MRI瘤内和瘤周放射组学模型（DWI_Peri3 + T2WI_Peri3 + DCE_Peri3_RF）对HER-2的预测效果最佳，在训练集、内部验证集和外部测试集的曲线下面积（AUC）分别为0.822（95 % CI:0.755-0.889）、0.823（0.714-0.932）和0.813（0.712-0.914）。它明显优于单参数或单地区模型，并保持了跨队列的一致性。结论：融合DWI、T2WI和DCE-MRI的瘤内-瘤周放射组学融合模型对HER-2的评估具有较高的诊断准确性，提供了无创的生物标志物，提高了乳腺癌治疗的准确性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Magnetic resonance imaging 医学-核医学

CiteScore

4.70

自引率

4.00%

发文量

194

审稿时长

83 days

期刊介绍： Magnetic Resonance Imaging (MRI) is the first international multidisciplinary journal encompassing physical, life, and clinical science investigations as they relate to the development and use of magnetic resonance imaging. MRI is dedicated to both basic research, technological innovation and applications, providing a single forum for communication among radiologists, physicists, chemists, biochemists, biologists, engineers, internists, pathologists, physiologists, computer scientists, and mathematicians.