Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
Sci Rep. 2020 Dec 9;10(1):21566. doi: 10.1038/s41598-020-78681-9.
We aimed to predict molecular subtypes of breast cancer using radiomics signatures extracted from synthetic mammography reconstructed from digital breast tomosynthesis (DBT). A total of 365 patients with invasive breast cancer with three different molecular subtypes (luminal A + B, luminal; HER2-positive, HER2; triple-negative, TN) were assigned to the training set and temporally independent validation cohort. A total of 129 radiomics features were extracted from synthetic mammograms. The radiomics signature was built using the elastic-net approach. Clinical features included patient age, lesion size and image features assessed by radiologists. In the validation cohort, the radiomics signature yielded an AUC of 0.838, 0.556, and 0.645 for the TN, HER2 and luminal subtypes, respectively. In a multivariate analysis, the radiomics signature was the only independent predictor of the molecular subtype. The combination of the radiomics signature and clinical features showed significantly higher AUC values than clinical features only for distinguishing the TN subtype. In conclusion, the radiomics signature showed high performance for distinguishing TN breast cancer. Radiomics signatures may serve as biomarkers for TN breast cancer and may help to determine the direction of treatment for these patients.
我们旨在使用从数字乳腺断层合成重建的合成乳房 X 线摄影术（DBT）中提取的放射组学特征来预测乳腺癌的分子亚型。共有 365 名浸润性乳腺癌患者，分为三种不同的分子亚型（luminal A+B、luminal；HER2 阳性、HER2；三阴性、TN），被分配到训练集和时间独立验证队列中。从合成乳房 X 光片中提取了 129 个放射组学特征。使用弹性网络方法构建放射组学特征。临床特征包括患者年龄、病变大小和放射科医生评估的图像特征。在验证队列中，放射组学特征对 TN、HER2 和 luminal 亚型的 AUC 分别为 0.838、0.556 和 0.645。在多变量分析中，放射组学特征是分子亚型的唯一独立预测因子。放射组学特征与临床特征的结合在区分 TN 亚型方面的 AUC 值明显高于仅临床特征。总之，放射组学特征在区分 TN 乳腺癌方面表现出较高的性能。放射组学特征可以作为 TN 乳腺癌的生物标志物，并有助于确定这些患者的治疗方向。
