Intraoperative Cytological Diagnosis of Brain Tumors: A Preliminary Study Using Deep Learning Model.

BACKGROUND
Intraoperative pathological diagnosis of central nervous system (CNS) tumors is essential in neuro-oncology to plan the patient management. Frozen section slides and cytological preparations provide architectural and cellular details analyzed by the pathologists to reach an intraoperative diagnosis. With the progress in artificial intelligence and machine learning fields, AI systems have significant potential in providing highly accurate real-time diagnosis in cytopathology.


OBJECTIVE
To investigate the efficiency of machine learning models in intraoperative cytological diagnosis of CNS tumors.


MATERIALS AND METHODS
We trained a deep neural network to classify 4 major brain biopsied lesions for intraoperative tissue diagnosis. Overall, 205 medical images were obtained from squash smear slides of histologically correlated cases, with 18 high-grade and 11 low-grade gliomas, 17 metastatic carcinomas, and 9 non-neoplastic pathological brain tissues. The neural network model was trained and evaluated using 5-fold cross-validation.


RESULTS
The model achieved 95% and 97% diagnostic accuracy on the patch-level classification and patient-level classification tasks, respectively.


CONCLUSIONS
We conclude that deep learning-based classification of cytological preparations may be a promising complementary method for rapid and accurate intraoperative diagnosis of CNS tumors.