Pathological Diagnosis of Adult Craniopharyngioma on MR Images: An Automated End-to-End Approach Based on Deep Neural Networks Requiring No Manual Segmentation

• Published in: Journal of clinical medicine Vol. 11; no. 24; p. 7481
• Main Authors: Teng, Yuen, Ran, Xiaoping, Chen, Boran, Chen, Chaoyue, Xu, Jianguo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 16.12.2022; MDPI
• Subjects: Artificial intelligence; Automation; Brain cancer; Classification; Clinical medicine; Deep learning; Magnetic resonance imaging; Mutation; Neural networks; Parkinson's disease; Patients; Radiomics; Tumors; United States > US; China; pathological diagnosis; computer-aided diagnosis; MRI; convolutional neural network; craniopharyngioma
• ISSN: 2077-0383; 2077-0383
• DOI: 10.3390/jcm11247481

The goal of this study was to develop end-to-end convolutional neural network (CNN) models that can noninvasively discriminate papillary craniopharyngioma (PCP) from adamantinomatous craniopharyngioma (ACP) on MR images requiring no manual segmentation. A total of 97 patients diagnosed with ACP or PCP were included. Pretreatment contrast-enhanced T1-weighted images were collected and used as the input of the CNNs. Six models were established based on six networks, including VGG16, ResNet18, ResNet50, ResNet101, DenseNet121, and DenseNet169. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to assess the performances of these deep neural networks. A five-fold cross-validation was applied to evaluate the performances of the models. The six networks yielded feasible performances, with area under the receiver operating characteristic curves (AUCs) of at least 0.78 for classification. The model based on Resnet50 achieved the highest AUC of 0.838 ± 0.062, with an accuracy of 0.757 ± 0.052, a sensitivity of 0.608 ± 0.198, and a specificity of 0.845 ± 0.034, respectively. Moreover, the results also indicated that the CNN method had a competitive performance compared to the radiomics-based method, which required manual segmentation for feature extraction and further feature selection. MRI-based deep neural networks can noninvasively differentiate ACP from PCP to facilitate the personalized assessment of craniopharyngiomas.