The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China; Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, 510080, China.
School of Electronics and Information, Xi'an Polytechnic University, Xi'an, 710000, China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, 710126, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Eur J Radiol. 2019 Dec;121:108711. doi: 10.1016/j.ejrad.2019.108711. Epub 2019 Oct 20.
We developed and validated a radiomic model based on mammography and assessed its value for predicting the pathological diagnosis of Breast Imaging Reporting and Data System (BI-RADS) category 4 calcifications.
Patients with a total of 212 eligible calcifications were recruited (159 cases in the primary cohort and 53 cases in the validation cohort). In total, 8286 radiomic features were extracted from the craniocaudal (CC) and mediolateral oblique (MLO) images. Machine learning was used to select features and build a radiomic signature. The clinical risk factors were selected from the independent clinical factors through logistic regression analyses. The radiomic nomogram incorporated the radiomic signature and an independent clinical risk factor. The diagnostic performance of the radiomic model and the radiologists' empirical prediction model was evaluated by the area under the receiver operating characteristic curve (AUC). The differences between the various AUCs were compared with DeLong's test.
Six radiomic features and the menopausal state were included in the radiomic nomogram, which discriminated benign calcifications from malignant calcifications with an AUC of 0.80 in the validation cohort. The difference between the classification results of the radiomic nomogram and that of radiologists was significant (p < 0.05). Particularly for patients with calcifications that are negative on ultrasounds but can be detected by mammography (MG+/US- calcifications), the identification ability of the radiomic nomogram was very strong.
The mammography-based radiomic nomogram is a potential tool to distinguish benign calcifications from malignant calcifications.
我们开发并验证了一种基于乳腺 X 线摄影的放射组学模型，并评估其预测乳腺影像报告和数据系统（BI-RADS）类别 4 钙化的病理诊断的价值。
共招募了 212 例符合条件的钙化患者（主要队列 159 例，验证队列 53 例）。共从头尾位（CC）和内外斜位（MLO）图像中提取了 8286 个放射组学特征。通过机器学习选择特征并构建放射组学特征。从独立的临床因素中通过逻辑回归分析选择临床危险因素。放射组学列线图纳入放射组学特征和独立的临床危险因素。通过受试者工作特征曲线（AUC）下面积评估放射组学模型和放射科医生经验预测模型的诊断性能。使用 DeLong 检验比较了各个 AUC 之间的差异。
放射组学列线图中包含 6 个放射组学特征和绝经状态，在验证队列中区分良性钙化和恶性钙化的 AUC 为 0.80。放射组学列线图的分类结果与放射科医生的结果之间存在显著差异（p<0.05）。特别是对于超声阴性但乳腺 X 线摄影能检测到的钙化（MG+/US-钙化），放射组学列线图的识别能力非常强。
基于乳腺 X 线摄影的放射组学列线图是一种区分良性钙化和恶性钙化的潜在工具。
