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Background: Accurate diagnosis of skull base tumors is essential forproviding personalized surgical treatment strategies. Intraoperative diagnosiscan be challenging due to tumor diversity and lack of intraoperative pathologyresources.  Objective: To develop an independent and parallel intraoperative pathologyworkflow that can provide rapid and accurate skull base tumor diagnoses usinglabel-free optical imaging and artificial intelligence.  Method: We used a fiber laser-based, label-free, non-consumptive,high-resolution microscopy method ($<$ 60 sec per 1 $\times$ 1 mm$^\text{2}$),called stimulated Raman histology (SRH), to image a consecutive, multicentercohort of skull base tumor patients. SRH images were then used to train aconvolutional neural network (CNN) model using three representation learningstrategies: cross-entropy, self-supervised contrastive learning, and supervisedcontrastive learning. Our trained CNN models were tested on a held-out,multicenter SRH dataset.  Results: SRH was able to image the diagnostic features of both benign andmalignant skull base tumors. Of the three representation learning strategies,supervised contrastive learning most effectively learned the distinctive anddiagnostic SRH image features for each of the skull base tumor types. In ourmulticenter testing set, cross-entropy achieved an overall diagnostic accuracyof 91.5%, self-supervised contrastive learning 83.9%, and supervisedcontrastive learning 96.6%. Our trained model was able to identify tumor-normalmargins and detect regions of microscopic tumor infiltration in whole-slide SRHimages.  Conclusion: SRH with trained artificial intelligence models can provide rapidand accurate intraoperative analysis of skull base tumor specimens to informsurgical decision-making.

Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence