Alert button
Picture for John G. Golfinos

John G. Golfinos

Alert button

Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging

Mar 23, 2023
Todd C. Hollon, Cheng Jiang, Asadur Chowdury, Mustafa Nasir-Moin, Akhil Kondepudi, Alexander Aabedi, Arjun Adapa, Wajd Al-Holou, Jason Heth, Oren Sagher, Pedro Lowenstein, Maria Castro, Lisa Irina Wadiura, Georg Widhalm, Volker Neuschmelting, David Reinecke, Niklas von Spreckelsen, Mitchel S. Berger, Shawn L. Hervey-Jumper, John G. Golfinos, Matija Snuderl, Sandra Camelo-Piragua, Christian Freudiger, Honglak Lee, Daniel A. Orringer

Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for patients with brain tumors is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. In this study, we developed DeepGlioma, a rapid ($< 90$ seconds), artificial-intelligence-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas. DeepGlioma is trained using a multimodal dataset that includes stimulated Raman histology (SRH); a rapid, label-free, non-consumptive, optical imaging method; and large-scale, public genomic data. In a prospective, multicenter, international testing cohort of patients with diffuse glioma ($n=153$) who underwent real-time SRH imaging, we demonstrate that DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy (IDH mutation, 1p19q co-deletion and ATRX mutation), achieving a mean molecular classification accuracy of $93.3\pm 1.6\%$. Our results represent how artificial intelligence and optical histology can be used to provide a rapid and scalable adjunct to wet lab methods for the molecular screening of patients with diffuse glioma.

* Paper published in Nature Medicine 
Viaarxiv icon

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

Aug 08, 2021
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

Figure 1 for Contrastive Representation Learning for Rapid Intraoperative Diagnosis of Skull Base Tumors Imaged Using Stimulated Raman Histology
Figure 2 for Contrastive Representation Learning for Rapid Intraoperative Diagnosis of Skull Base Tumors Imaged Using Stimulated Raman Histology
Figure 3 for Contrastive Representation Learning for Rapid Intraoperative Diagnosis of Skull Base Tumors Imaged Using Stimulated Raman Histology
Figure 4 for Contrastive Representation Learning for Rapid Intraoperative Diagnosis of Skull Base Tumors Imaged Using Stimulated Raman Histology

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.

Viaarxiv icon