Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
Ultrasound Med Biol. 2020 May;46(5):1133-1141. doi: 10.1016/j.ultrasmedbio.2020.01.015. Epub 2020 Feb 24.
A radiomics-based classifier to distinguish phyllodes tumor and fibroadenoma on gray-scale breast ultrasonography was developed and validated. A total of 93 radiomics features were extracted from representative transverse plane ultrasound images of 182 fibroepithelial lesions initially diagnosed by core needle biopsy. High-throughput radiomics features were selected using the intra-class correlation coefficient between two radiologist readers and the Least Absolute Shrinkage and Selection Operator regression through 10-fold cross-validation. When applied to the validation set, the radiomics classifier for the differentiation of phyllodes tumors and benign/fibroadenomas achieved an area under the receiver operating characteristic curve of 0.765 (95% confidence interval [CI]: 0.597-0.888) with an accuracy of 0.703 (sensitivity: 0.857; specificity: 0.5). Our radiomics signature-based classifier may help predict phyllodes tumors among fibroepithelial lesions on breast ultrasonography.
基于放射组学的分类器可区分灰阶乳腺超声上的叶状肿瘤和纤维腺瘤，并对此进行了开发和验证。从最初通过核心针活检诊断为纤维上皮病变的 182 个病变的代表性横切面超声图像中提取了 93 个放射组学特征。通过 10 倍交叉验证，使用两位放射科医生之间的组内相关系数和最小绝对收缩和选择算子回归来选择高通量放射组学特征。当应用于验证集时，用于区分叶状肿瘤和良性/纤维腺瘤的放射组学分类器的受试者工作特征曲线下面积为 0.765（95%置信区间 [CI]：0.597-0.888），准确率为 0.703（敏感度：0.857；特异性：0.5）。我们的基于放射组学特征的分类器可能有助于预测乳腺超声上纤维上皮病变中的叶状肿瘤。
