■评估深度学习模型的诊断效率,以在普通计算机断层扫描(CT)图像上区分恶性和良性腮腺肿瘤。
■回顾性分析283例腮腺肿瘤患者的CT图像。其中,根据病理结果,良性150例,恶性133例。总共切除了917个感兴趣的腮腺肿瘤区域(456个良性和461个恶性)。三个深度学习网络(ResNet50,VGG16_bn,和DenseNet169)用于诊断(大约3:1用于训练和测试)。诊断效率(准确性,灵敏度,特异性,和曲线下面积[AUC])根据917张图像计算并比较了三个网络。为了模拟人类诊断的过程,在网络结束时建立了投票模型,283例肿瘤被分类为良性或恶性.同时,由两名放射科医师(A和B)对917张肿瘤图像进行分类,并由放射科医师对原始CT图像进行分类。计算了三个深度学习网络模型(投票后)和两个放射科医师的诊断效率。
■对于917张CT图像,ResNet50对腮腺恶性肿瘤的诊断具有较高的准确性和敏感性;灵敏度,特异性,AUC为90.8%,91.3%,90.4%,和0.96。对于283个肿瘤,准确性,灵敏度,ResNet50的特异性(投票后)为92.3%,93.5%和91.2%,分别。
■ResNet50在普通CT图像上区分腮腺恶性肿瘤和良性肿瘤方面表现出很高的敏感性;这使其成为筛查腮腺恶性肿瘤的有前途的辅助诊断方法。
UNASSIGNED: Evaluating the diagnostic efficiency of deep-learning models to distinguish malignant from benign parotid tumors on plain computed tomography (CT) images.
UNASSIGNED: The CT images of 283 patients with parotid tumors were enrolled and analyzed retrospectively. Of them, 150 were benign and 133 were malignant according to pathology results. A total of 917 regions of interest of parotid tumors were cropped (456 benign and 461 malignant). Three deep-learning networks (ResNet50, VGG16_bn, and DenseNet169) were used for diagnosis (approximately 3:1 for training and testing). The diagnostic efficiencies (accuracy, sensitivity, specificity, and area under the curve [AUC]) of three networks were calculated and compared based on the 917 images. To simulate the process of human diagnosis, a voting model was developed at the end of the networks and the 283 tumors were classified as benign or malignant. Meanwhile, 917 tumor images were classified by two radiologists (A and B) and original CT images were classified by radiologist B. The diagnostic efficiencies of the three deep-learning network models (after voting) and the two radiologists were calculated.
UNASSIGNED: For the 917 CT images, ResNet50 presented high accuracy and sensitivity for diagnosing malignant parotid tumors; the accuracy, sensitivity, specificity, and AUC were 90.8%, 91.3%, 90.4%, and 0.96, respectively. For the 283 tumors, the accuracy, sensitivity, and specificity of ResNet50 (after voting) were 92.3%, 93.5% and 91.2%, respectively.
UNASSIGNED: ResNet50 presented high sensitivity in distinguishing malignant from benign parotid tumors on plain CT images; this made it a promising auxiliary diagnostic method to screen malignant parotid tumors.