From the Department of Radiology, University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637.
Radiology. 2019 Apr;291(1):15-20. doi: 10.1148/radiol.2019181113. Epub 2019 Feb 12.
Background Previous studies have suggested that breast parenchymal texture features may reflect the biologic risk factors associated with breast cancer development. Therefore, combining the characteristics of normal parenchyma from the contralateral breast with radiomic features of breast tumors may improve the accuracy of digital mammography in the diagnosis of breast cancer. Purpose To determine whether the addition of radiomic analysis of contralateral breast parenchyma to the characterization of breast lesions with digital mammography improves lesion classification over that with radiomic tumor features alone. Materials and Methods This HIPAA-compliant, retrospective study included 182 patients (age range, 25-90 years; mean age, 55.9 years ± 14.9) who underwent mammography between June 2002 and July 2009. There were 106 malignant and 76 benign lesions. Automatic lesion segmentation and radiomic analysis were performed for each breast lesion. Radiomic texture analysis was applied in the normal regions of interest in the contralateral breast parenchyma to assess the mammographic parenchymal patterns. The classification performance of both individual features and the output from a Bayesian artificial neural network classifier was evaluated with the leave-one-patient-out method by using the area under the receiver operating characteristic curve (AUC) as the figure of merit in the task of differentiating between malignant and benign lesions. Results The performance of the combined lesion and parenchyma classifier in the differentiation between malignant and benign mammographic lesions was better than that with the lesion features alone (AUC = 0.84 ± 0.03 vs 0.79 ± 0.03, respectively; P = .047). Overall, six radiomic features-spiculation, margin sharpness, size, circularity from the tumor feature set, and skewness and power law beta from the parenchymal feature set-were selected more than 50% of the time during the feature selection process on the combined feature set. Conclusion Combining quantitative radiomic data from tumors with contralateral parenchyma characterizations may improve diagnostic accuracy for breast cancer. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Shaffer in this issue.
背景 先前的研究表明，乳腺实质纹理特征可能反映与乳腺癌发展相关的生物学危险因素。因此，将对侧乳腺的正常实质特征与乳腺肿瘤的放射组学特征相结合，可能会提高数字乳腺摄影在乳腺癌诊断中的准确性。目的 确定在数字乳腺摄影中，通过对侧乳腺实质的放射组学分析来描述乳腺病变特征是否比单独使用肿瘤放射组学特征更能提高病变分类的准确性。材料与方法 本 HIPAA 合规、回顾性研究纳入了 182 例（年龄 25-90 岁，平均年龄 55.9 岁±14.9 岁）于 2002 年 6 月至 2009 年 7 月期间接受乳腺 X 线摄影检查的患者。其中 106 例为恶性病变，76 例为良性病变。对每个乳腺病变进行自动病变分割和放射组学分析。对侧乳腺实质的感兴趣区进行放射组学纹理分析，以评估乳腺实质模式。采用受试者工作特征曲线下面积（AUC）作为评价指标，采用留一患者法评估个体特征和贝叶斯人工神经网络分类器输出的分类性能，用于区分良恶性病变。结果 联合病变和实质分类器在区分良恶性乳腺 X 线摄影病变方面的性能优于单独使用病变特征（AUC=0.84±0.03 与 0.79±0.03，P=0.047）。总体而言，在联合特征集的特征选择过程中，有 6 个放射组学特征（肿瘤特征集中的毛刺、边界锐度、大小、圆形度和实质特征集中的偏度和幂律β）的选择频率超过 50%。结论 将肿瘤的定量放射组学数据与对侧实质特征相结合，可能会提高乳腺癌的诊断准确性。
