Favati Benedetta, Borgheresi Rita, Giannelli Marco, Marini Carolina, Vani Vanina, Marfisi Daniela, Linsalata Stefania, Moretti Monica, Mazzotta Dionisia, Neri Emanuele
Department of Translational Research, University of Pisa, 56126 Pisa, Italy.
Unit of Medical Physics, Azienda Ospedaliero-Universitaria Pisana, Via Roma 67, 56126 Pisa, Italy.
Diagnostics (Basel). 2022 Mar 22;12(4):771. doi: 10.3390/diagnostics12040771.
A fair amount of microcalcifications sent for biopsy are false positives. The study investigates whether quantitative radiomic features extracted from digital breast tomosynthesis (DBT) can be an additional and useful tool to discriminate between benign and malignant BI-RADS category 4 microcalcification.
This retrospective study included 252 female patients with BI-RADS category 4 microcalcifications. The patients were divided into two groups according to micro-histopathology: 126 patients with benign lesions and 126 patients with certain or possible malignancies. A total of 91 radiomic features were extracted for each patient, and the 12 most representative features were selected by using the agglomerative hierarchical clustering method. The binary classification task of the two groups was carried out by using four different machine-learning algorithms (i.e., linear support vector machine (SVM), radial basis function (RBF) SVM, logistic regression (LR), and random forest (RF)). Accuracy, sensitivity, sensibility, and the area under the curve (AUC) were calculated for each of them.
The best performance was achieved using the RF classifier (AUC = 0.59, 95% confidence interval 0.57-0.60; sensitivity = 0.56, 95% CI 0.54-0.58; specificity = 0.61, 95% CI 0.59-0.63; accuracy = 0.58, 95% CI 0.57-0.59).
DBT-based radiomic analysis seems to have only limited potential in discriminating benign from malignant microcalcifications.
送去活检的大量微钙化灶为假阳性。本研究调查从数字乳腺断层合成（DBT）中提取的定量影像组学特征是否可作为区分BI-RADS 4类微钙化灶良性与恶性的一种额外且有用的工具。
这项回顾性研究纳入了252例具有BI-RADS 4类微钙化灶的女性患者。根据微观组织病理学将患者分为两组：126例良性病变患者和126例确定或可能为恶性肿瘤的患者。为每位患者提取了总共91个影像组学特征，并使用凝聚层次聚类方法选择了12个最具代表性的特征。使用四种不同的机器学习算法（即线性支持向量机（SVM）、径向基函数（RBF）SVM、逻辑回归（LR）和随机森林（RF））对两组进行二元分类任务。计算每种算法的准确率、敏感性、特异性和曲线下面积（AUC）。
使用RF分类器获得了最佳性能（AUC = 0.59，95%置信区间0.57 - 0.60；敏感性 = 0.56，95% CI 0.54 - 0.58；特异性 = 0.61，95% CI 0.59 - 0.63；准确率 = 0.58，95% CI 0.57 - 0.59）。
基于DBT的影像组学分析在区分微钙化灶良性与恶性方面似乎潜力有限。
