Artificial Intelligence Neuropathologist - Automated CNS Tumor Pathological Diagnosis Based on Deep Learning

The aim of the study is to develop an automated pathological diagnosis system for CNS tumors based on deep learning technique. It is designed to firstly develop the best deep learning model for pathological diagnosis of CNS tumors, in order to improve the accuracy of pathological diagnosis. Then to be used clinically, reduce the workload and stress of neuropathologists and obtain the benefits for CNS tumor patients.

Different CNS tumors including meningioma, glioma, lymphoma and other various tumors have their own different treatment principles and plans. For example, high grade glioma requires operational resection and post-operational chemo-radiotherapy. However, operational resection is not significant for improving prognosis in lymphoma patients, systematic chemotherapy will be performed after specific diagnosis based on biopsy. Therefore, in this study, an automated CNS tumor pathological diagnosis system will be developed to classify the different type of those tumors.

At present, pathological diagnosis of CNS tumors is based on histopathological characteristics and molecular information after a systematic analyzed by pathologists. The accuracy of the diagnosis very much relies on the experience of the pathologists. However, to become a experienced and qualified pathologist requires years of training. Pathologists may give completely different diagnose outcome for the same patient. Thus, it is essential to develop a system that can assist pathologists.

Deep learning is one of the most advanced techniques of artificial intelligence. In particular, the ability of image recognition is extremely powerful. Therefore, we are able to develop a model for histopathological section images based on deep learning. WHO Classification of CNS Tumors 2016 has included molecular markers as the important part of diagnosis. Hence, there will be an additional model of molecular pathology to be added to the system.

Huashan Hospital has one of the largest CNS tumor biobank in China, which is the key part for deep learning, as it needs large amount of data. The case load of this study is able to show the representative and authoritative of those data.