Automatic detection of pituitary microadenoma from magnetic resonance imaging using deep learning algorithms

Li, Q.; Zhu, Y.; Chen, M.; Guo, R.; Hu, Q.; Deng, Z.; Deng, S.; Wen, H.; Gao, R.; Nie, Y.; Li, H.; Zhang, T.; Chen, J.; Shi, G.; Shen, J.; Cheung, W. W.; Guo, Y.; Chen, Y.

Pituitary microadenoma (PM) is often difficult to detect by MR imaging alone. We employed a computer-aided PM diagnosis (PM-CAD) system based on deep learning to assist radiologists in clinical workflow. We enrolled 1,228 participants and stratified into 3 non-overlapping cohorts for training, validation and testing purposes. Our PM-CAD system outperformed 6 existing established convolutional neural network models for detection of PM. In test dataset, diagnostic accuracy of PM-CAD system was comparable to radiologists with > 10 years of professional expertise (94% versus 95%). The diagnostic accuracy in internal and external dataset was 94% and 90%, respectively. Importantly, PM-CAD system detected the presence of PM that had been previously misdiagnosed by radiologists. This is the first report showing that PM-CAD system is a viable tool for detecting PM. Our results suggest that PM-CAD system is applicable to radiology departments, especially in primary health care institutions.