Banzato Tommaso, Bernardini Marco, Cherubini Giunio B, Zotti Alessandro
Department of Animal Medicine, Production and Health, University of Padua, Viale dell'Università 16, AGRIPOLIS, Legnaro, 35020, Padua, Italy.
Portoni Rossi Veterinary Hospital, Via Roma 57, Zola Predosa, 40069, Bologna, Italy.
BMC Vet Res. 2018 Oct 22;14(1):317. doi: 10.1186/s12917-018-1638-2.
Distinguishing between meningeal-based and intra-axial lesions by means of magnetic resonance (MR) imaging findings may occasionally be challenging. Meningiomas and gliomas account for most of the total primary brain neoplasms in dogs, and differentiating between these two forms is mandatory in choosing the correct therapy. The aims of the present study are: 1) to determine the accuracy of a deep convolutional neural network (CNN, GoogleNet) in discriminating between meningiomas and gliomas in pre- and post-contrast T1 images and T2 images; 2) to develop an image classifier, based on the combination of CNN and MRI sequence displaying the highest accuracy, to predict whether a lesion is a meningioma or a glioma.
Eighty cases with a final diagnosis of meningioma (n = 56) and glioma (n = 24) from two different institutions were included in the study. A pre-trained CNN was retrained on our data through a process called transfer learning. To evaluate CNN accuracy in the different imaging sequences, the dataset was divided into a training, a validation and a test set. The accuracy of the CNN was calculated on the test set. The combination between post-contrast T1 images and CNN was chosen in developing the image classifier (trCNN). Ten images from challenging cases were excluded from the database in order to test trCNN accuracy; the trCNN was trained on the remainder of the dataset of post-contrast T1 images, and correctly classified all the selected images. To compensate for the imbalance between meningiomas and gliomas in the dataset, the Matthews correlation coefficient (MCC) was also calculated. The trCNN showed an accuracy of 94% (MCC = 0.88) on post-contrast T1 images, 91% (MCC = 0.81) on pre-contrast T1-images and 90% (MCC = 0.8) on T2 images.
The developed trCNN could be a reliable tool in distinguishing between different meningiomas and gliomas from MR images.
通过磁共振（MR）成像结果区分脑膜病变和脑实质内病变有时具有挑战性。脑膜瘤和胶质瘤占犬原发性脑肿瘤总数的大部分，在选择正确治疗方法时必须区分这两种类型。本研究的目的是：1）确定深度卷积神经网络（CNN，谷歌网络）在鉴别对比剂增强前后T1图像和T2图像中的脑膜瘤和胶质瘤时的准确性；2）基于CNN与显示最高准确性的MRI序列的组合开发一种图像分类器，以预测病变是脑膜瘤还是胶质瘤。
本研究纳入了来自两个不同机构的80例最终诊断为脑膜瘤（n = 56）和胶质瘤（n = 24）的病例。通过称为迁移学习的过程在我们的数据上对预训练的CNN进行再训练。为了评估CNN在不同成像序列中的准确性，将数据集分为训练集、验证集和测试集。在测试集上计算CNN的准确性。在开发图像分类器（trCNN）时选择了对比剂增强后T1图像和CNN的组合。为了测试trCNN的准确性，从数据库中排除了10例具有挑战性病例的图像；trCNN在对比剂增强后T1图像数据集的其余部分上进行训练，并正确分类了所有选定的图像。为了弥补数据集中脑膜瘤和胶质瘤之间的不平衡，还计算了马修斯相关系数（MCC）。trCNN在对比剂增强后T1图像上的准确率为94%（MCC = 0.88），在对比剂增强前T1图像上为91%（MCC = 0.81），在T2图像上为90%（MCC = 0.8）。
所开发的trCNN可能是一种从MR图像中区分不同脑膜瘤和胶质瘤的可靠工具。
