Gabelloni Michela, Faggioni Lorenzo, Attanasio Simona, Vani Vanina, Goddi Antonio, Colantonio Sara, Germanese Danila, Caudai Claudia, Bruschini Luca, Scarano Mariella, Seccia Veronica, Neri Emanuele
Diagnostic and Interventional Radiology, Department of Translational Research, University of Pisa, Via Roma, 67, 56126 Pisa, Italy.
Institute of Information Science and Technologies "A. Faedo" of the National Research Council of Italy (ISTI-CNR), 56124 Pisa, Italy.
Diagnostics (Basel). 2020 Nov 3;10(11):900. doi: 10.3390/diagnostics10110900.
Our purpose is to evaluate the performance of magnetic resonance (MR) radiomics analysis for differentiating between malignant and benign parotid neoplasms and, among the latter, between pleomorphic adenomas and Warthin tumors. We retrospectively evaluated 75 T2-weighted images of parotid gland lesions, of which 61 were benign tumors (32 pleomorphic adenomas, 23 Warthin tumors and 6 oncocytomas) and 14 were malignant tumors. A receiver operating characteristics (ROC) curve analysis was performed to find the threshold values for the most discriminative features and determine their sensitivity, specificity and area under the ROC curve (AUROC). The most discriminative features were used to train a support vector machine classifier. The best classification performance was obtained by comparing a pleomorphic adenoma with a Warthin tumor (yielding sensitivity, specificity and a diagnostic accuracy as high as 0.8695, 0.9062 and 0.8909, respectively) and a pleomorphic adenoma with malignant tumors (sensitivity, specificity and a diagnostic accuracy of 0.6666, 0.8709 and 0.8043, respectively). Radiomics analysis of parotid tumors on conventional T2-weighted MR images allows the discrimination of pleomorphic adenomas from Warthin tumors and malignant tumors with a high sensitivity, specificity and diagnostic accuracy.
我们的目的是评估磁共振（MR）影像组学分析在鉴别腮腺良恶性肿瘤以及在良性肿瘤中鉴别多形性腺瘤和沃辛瘤的性能。我们回顾性评估了75例腮腺病变的T2加权图像，其中61例为良性肿瘤（32例多形性腺瘤、23例沃辛瘤和6例嗜酸细胞瘤），14例为恶性肿瘤。进行了受试者操作特征（ROC）曲线分析，以找出最具鉴别力特征的阈值，并确定其敏感性、特异性和ROC曲线下面积（AUROC）。使用最具鉴别力的特征训练支持向量机分类器。通过比较多形性腺瘤与沃辛瘤（敏感性、特异性和诊断准确性分别高达0.8695、0.9062和0.8909）以及多形性腺瘤与恶性肿瘤（敏感性、特异性和诊断准确性分别为0.6666、0.8709和0.8043），获得了最佳分类性能。对常规T2加权MR图像上的腮腺肿瘤进行影像组学分析，能够以较高的敏感性、特异性和诊断准确性鉴别多形性腺瘤与沃辛瘤以及恶性肿瘤。
