Luo Hong-Jian, Ren Jia-Liang, Mei Guo Li, Liang Niu Jin, Song Xiao-Li
Department of Radiology, The Third Affiliated Hospital of Zunyi Medical University (The First People's Hospital of Zunyi), Zuiyi, Guizhou province, China.
GE HealthCare, Beijing, China.
Eur J Radiol Open. 2024 Jul 19;13:100592. doi: 10.1016/j.ejro.2024.100592. eCollection 2024 Dec.
Human epidermal growth factor receptor 2 (HER2) is a tumor biomarker with significant prognostic and therapeutic implications for invasive ductal breast carcinoma (IDC).
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.
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).
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.
Machine learning-based MRI radiomics may aid in the non-invasive prediction of the different expression status of HER2 in IDC.
人表皮生长因子受体2(HER2)是一种肿瘤生物标志物,对浸润性导管癌(IDC)具有重要的预后和治疗意义。
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HER2表达状态进行分类中的有效性,包括HER2阳性、HER2低表达和HER2完全阴性(HER2零表达)。
共纳入402例经手术病理确诊的IDC女性患者,随后分为训练组(n = 250,中心I)和验证组(n = 152,中心II)。从术前MRI中提取放射组学特征。采用模拟退火算法进行关键特征选择。进行了两项分类任务:任务1,HER2阳性与HER2阴性(HER2低表达和HER2零表达)的分类;任务2,HER2低表达与HER2零表达的分类。采用逻辑回归、随机森林(RF)和支持向量机建立放射组学模型。使用操作特征曲线(ROC)下的面积(AUC)评估模型的性能。
从多序列MRI中总共提取了4506个放射组学特征。成功开发了用于预测HER2表达状态的放射组学模型。在三种分类算法中,RF在区分HER2阳性与HER2阴性以及HER2低表达与HER2零表达方面表现最佳,AUC值分别为0.777和0.731。
基于机器学习的MRI放射组学可能有助于对IDC中HER2的不同表达状态进行无创预测。