Department of Radiology, The University of Chicago, Chicago, IL 60637, United States of America; Department of Physics, Wheaton College, Wheaton, IL 60187, United States of America.
Department of Radiology, The University of Chicago, Chicago, IL 60637, United States of America.
Magn Reson Imaging. 2021 Oct;82:111-121. doi: 10.1016/j.mri.2021.06.021. Epub 2021 Jun 24.
Radiomic features extracted from breast lesion images have shown potential in diagnosis and prognosis of breast cancer. As medical centers transition from 1.5 T to 3.0 T magnetic resonance (MR) imaging, it is beneficial to identify potentially robust radiomic features across field strengths because images acquired at different field strengths could be used in machine learning models. Dynamic contrast-enhanced MR images of benign breast lesions and hormone receptor positive/HER2-negative (HR+/HER2-) breast cancers were acquired retrospectively, yielding 612 unique cases: 150 and 99 benign lesions imaged at 1.5 T and 3.0 T, and 223 and 140 HR+/HER2- cancerous lesions imaged at 1.5 T and 3.0 T, respectively. In addition, an independent set of seven lesions imaged at both field strengths, three benign lesions and four HR+/HER2- cancers, was analyzed separately. Lesions were automatically segmented using a 4D fuzzy c-means method; thirty-eight radiomic features were extracted. Feature value distributions were compared by cancer status and imaging field strength using the Kolmogorov-Smirnov test. Features that did not demonstrate a statistically significant difference were considered to be potentially robust. The area under the receiver operating characteristic curve (AUC), for the task of classifying lesions as benign or HR+/HER2- cancer, was determined for each feature at each field strength. Three features were found to be both potentially robust across field strength and of high classification performance, i.e., AUCs statistically greater than 0.5 in the classification task: one shape feature (irregularity), one texture feature (sum average) and one enhancement variance kinetics features (enhancement variance increasing rate). In the demonstration set of lesions imaged at both field strengths, two of the three potentially robust features showed qualitative agreement across field strength. These findings may contribute to the development of computer-aided diagnosis models that are robust across field strength for this classification task.
从乳腺病变图像中提取的放射组学特征已显示出在乳腺癌的诊断和预后中的潜力。随着医疗中心从 1.5T 向 3.0T 磁共振（MR）成像过渡，识别跨场强的潜在稳健放射组学特征是有益的，因为在不同场强下获得的图像可用于机器学习模型中。回顾性地获取了良性乳腺病变和激素受体阳性/HER2 阴性（HR+/HER2-）乳腺癌的动态对比增强 MR 图像，共获得 612 个独特病例：150 个和 99 个良性病变分别在 1.5T 和 3.0T 下成像，223 个和 140 个 HR+/HER2-癌性病变分别在 1.5T 和 3.0T 下成像。此外，还分别分析了另外一组在两种场强下成像的七个病变，包括三个良性病变和四个 HR+/HER2-癌症。使用 4D 模糊 c-均值方法自动对病变进行分割；提取了 38 个放射组学特征。使用 Kolmogorov-Smirnov 检验比较了癌症状态和成像场强下的特征值分布。未表现出统计学差异的特征被认为是潜在稳健的。在每个场强下，针对良性或 HR+/HER2-癌症分类任务，确定了每个特征的接收器操作特征曲线（AUC）下面积。发现三个特征在跨场强方面具有稳健性且分类性能较高，即在分类任务中 AUC 统计上大于 0.5：一个形状特征（不规则性）、一个纹理特征（总和平均值）和一个增强变异性动力学特征（增强变异性增长率）。在两个场强下成像的病变演示集中，三个潜在稳健特征中的两个在跨场强方面表现出定性一致性。这些发现可能有助于开发针对该分类任务的稳健跨场强计算机辅助诊断模型。
