Kovačević Lucija, Štajduhar Andrija, Stemberger Karlo, Korša Lea, Marušić Zlatko, Prutki Maja
Clinical Department of Diagnostic and Interventional Radiology, University Hospital Centre Zagreb, Kispaticeva 12, 10000 Zagreb, Croatia.
Department for Medical Statistics, Epidemiology and Medical Informatics School of Medicine, University of Zagreb, Salata 12, 10000 Zagreb, Croatia.
J Pers Med. 2023 Jul 18;13(7):1150. doi: 10.3390/jpm13071150.
This study aimed to explore the potential of multi-phase dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics for classifying breast cancer surrogate subtypes. This retrospective study analyzed 360 breast cancers from 319 patients who underwent pretreatment DCE-MRI between January 2015 and January 2019. The cohort consisted of 33 triple-negative, 26 human epidermal growth factor receptor 2 (HER2)-positive, 109 luminal A-like, 144 luminal B-like HER2-negative, and 48 luminal B-like HER2-positive lesions. A total of 1781 radiomic features were extracted from manually segmented breast cancers in each DCE-MRI sequence. The model was internally validated and selected using ten times repeated five-fold cross-validation on the primary cohort, with further evaluation using a validation cohort. The most successful models were logistic regression models applied to the third post-contrast subtraction images. These models exhibited the highest area under the curve (AUC) for discriminating between luminal A like vs. others (AUC: 0.78), luminal B-like HER2 negative vs. others (AUC: 0.57), luminal B-like HER2 positive vs. others (AUC: 0.60), HER2 positive vs. others (AUC: 0.81), and triple negative vs. others (AUC: 0.83). In conclusion, the radiomic features extracted from multi-phase DCE-MRI are promising for discriminating between breast cancer subtypes. The best-performing models relied on tissue changes observed during the mid-stage of the imaging process.
本研究旨在探索多期动态对比增强磁共振成像（DCE-MRI）影像组学在乳腺癌替代亚型分类中的潜力。这项回顾性研究分析了2015年1月至2019年1月期间接受预处理DCE-MRI检查的319例患者的360例乳腺癌。该队列包括33例三阴性、26例人表皮生长因子受体2（HER2）阳性、109例腔面A型、144例HER2阴性的腔面B型和48例HER2阳性的腔面B型病变。在每个DCE-MRI序列中，从手动分割的乳腺癌中提取了总共1781个影像组学特征。该模型在主要队列上使用十次重复的五折交叉验证进行内部验证和选择，并使用验证队列进行进一步评估。最成功的模型是应用于对比剂注射后第三幅减影图像的逻辑回归模型。这些模型在区分腔面A型与其他类型（曲线下面积[AUC]：0.78）、HER2阴性的腔面B型与其他类型（AUC：0.57）、HER2阳性的腔面B型与其他类型（AUC：0.60）、HER2阳性与其他类型（AUC：0.81）以及三阴性与其他类型（AUC：0.83）方面表现出最高的曲线下面积。总之，从多期DCE-MRI中提取的影像组学特征在区分乳腺癌亚型方面具有前景。表现最佳的模型依赖于在成像过程中期观察到的组织变化。
