Contrastive Representation Learning for Rapid Intraoperative Diagnosis of Skull Base Tumors Imaged Using Stimulated Raman Histology

• URL: http://arxiv.org/abs/2108.03555v1
• Date: Sun, 8 Aug 2021 02:49:29 GMT
• Title: Contrastive Representation Learning for Rapid Intraoperative Diagnosis of Skull Base Tumors Imaged Using Stimulated Raman Histology
• Authors: Cheng Jiang, Abhishek Bhattacharya, Joseph Linzey, Rushikesh Joshi, Sung Jik Cha, Sudharsan Srinivasan, Daniel Alber, Akhil Kondepudi, Esteban Urias, Balaji Pandian, Wajd Al-Holou, Steve Sullivan, B. Gregory Thompson, Jason Heth, Chris Freudiger, Siri Khalsa, Donato Pacione, John G. Golfinos, Sandra Camelo-Piragua, Daniel A. Orringer, Honglak Lee, Todd Hollon
• Abstract summary: Intraoperative diagnosis of skull base tumors can be challenging due to tumor diversity and lack of intraoperative pathology resources. We developed an independent and parallel intraoperative pathology workflow that can provide rapid and accurate skull base tumor diagnoses.
• Score: 26.194247664756553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: Accurate diagnosis of skull base tumors is essential for providing personalized surgical treatment strategies. Intraoperative diagnosis can be challenging due to tumor diversity and lack of intraoperative pathology resources. Objective: To develop an independent and parallel intraoperative pathology workflow that can provide rapid and accurate skull base tumor diagnoses using label-free optical imaging and artificial intelligence (AI). 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, multicenter cohort of skull base tumor patients. SRH images were then used to train a convolutional neural network (CNN) model using three representation learning strategies: cross-entropy, self-supervised contrastive learning, and supervised contrastive 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 and malignant skull base tumors. Of the three representation learning strategies, supervised contrastive learning most effectively learned the distinctive and diagnostic SRH image features for each of the skull base tumor types. In our multicenter testing set, cross-entropy achieved an overall diagnostic accuracy of 91.5%, self-supervised contrastive learning 83.9%, and supervised contrastive learning 96.6%. Our trained model was able to identify tumor-normal margins and detect regions of microscopic tumor infiltration in whole-slide SRH images. Conclusion: SRH with AI models trained using contrastive representation learning can provide rapid and accurate intraoperative diagnosis of skull base tumors.

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