基于磁共振成像的机器学习放射组学用于预测乳腺浸润性导管癌的 HER2 表达状态

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Hong-Jian Luo , Jia-Liang Ren , Li mei Guo , Jin liang Niu , Xiao-Li Song
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引用次数: 0

摘要

背景人表皮生长因子受体 2(HER2)是一种肿瘤生物标志物,对浸润性导管乳腺癌(IDC)的预后和治疗具有重要意义。本研究旨在探讨基于多序列磁共振成像(MRI)的机器学习放射组学模型在对 IDC 患者的 HER2 表达状态(包括 HER2 阳性、HER2 低和 HER2 完全阴性(HER2-0))进行分类时的有效性。方法共招募了402名经手术病理确诊的IDC女性患者,随后将其分为训练组(250人,中心I)和验证组(152人,中心II)。放射组学特征从术前核磁共振成像中提取。关键特征选择采用模拟退火算法。进行了两项分类任务:任务 1:HER2 阳性与 HER2 阴性(HER2-低和 HER2-零)的分类;任务 2:HER2-低与 HER2-零的分类。通过逻辑回归、随机森林(RF)和支持向量机建立了放射组学模型。结果从多序列磁共振成像中总共提取了 4506 个放射组学特征。成功建立了预测 HER2 表达状态的放射组学模型。在三种分类算法中,RF在HER2-阳性与HER2-阴性以及HER2-低与HER2-零的分类中性能最高,AUC值分别为0.777和0.731。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MRI-based machine learning radiomics for prediction of HER2 expression status in breast invasive ductal carcinoma

Background

Human epidermal growth factor receptor 2 (HER2) is a tumor biomarker with significant prognostic and therapeutic implications for invasive ductal breast carcinoma (IDC).

Objective

This study aimed to explore the effectiveness of a multisequence magnetic resonance imaging (MRI)-based machine learning radiomics model in classifying the expression status of HER2, including HER2-positive, HER2-low, and HER2 completely negative (HER2-zero), among patients with IDC.

Methods

A total of 402 female patients with IDC confirmed through surgical pathology were enrolled and subsequently divided into a training group (n = 250, center I) and a validation group (n = 152, center II). Radiomics features were extracted from the preoperative MRI. A simulated annealing algorithm was used for key feature selection. Two classification tasks were performed: task 1, the classification of HER2-positive vs. HER2-negative (HER2-low and HER2-zero), and task 2, the classification of HER2-low vs. HER2-zero. Logistic regression, random forest (RF), and support vector machine were conducted to establish radiomics models. The performance of the models was evaluated using the area under the curve (AUC) of the operating characteristics (ROC).

Results

In total, 4506 radiomics features were extracted from multisequence MRI. A radiomics model for prediction of expression state of HER2 was successfully developed. Among the three classification algorithms, RF achieved the highest performance in classifying HER2-positive from HER2-negative and HER2-low from HER2-zero, with AUC values of 0.777 and 0.731, respectively.

Conclusions

Machine learning-based MRI radiomics may aid in the non-invasive prediction of the different expression status of HER2 in IDC.

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来源期刊
European Journal of Radiology Open
European Journal of Radiology Open Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
4.10
自引率
5.00%
发文量
55
审稿时长
51 days
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