Brain tumors represent a diverse group of neoplasms with highly variable therapies and clinical outcomes. Early personalized clinical management and initiation of precision-based molecular studies still heavily relies on morphologic interpretation of hematoxylin and eosin (H&E)-stained slides. Unfortunately, due to its qualitative nature, histopathological classification is prone to well-recognized inter-observer variability. To overcome this limitation, we developed an objective morphology-based brain tumor classifier using a deep convolutional neural network (CNN). Our CNN is trained on a dataset of over one million pathologist- and molecularly-annotated image patches from H&E slides spanning over 20 common brain tumor classes. Importantly, our tool is fully automated, compatible with standard pathology workflows and provides prompt whole-slide annotation and lesion classification in under 5 minutes. The performance of our CNN-based tumor classifier is highly concordant with board-certified pathologists and confirmatory immunohistochemical stains. Testing reveals an area under the receiver operator characteristic (AUC) of >0.95 for multiple classification tasks, including lesion localizing and differentiating among different brain tumor classes. In certain scenarios, it also offers objective predictions of actionable molecular alterations (IDH mutations and 1p19q co-deletions). Lastly, we use cloud-computing to provide our classifier as a web-based tool capable of rendering timely second opinions and quality assurance to remote cancer centers requiring additional subspecialized neuropathological expertise. This study demonstrates the efficacy of utilizing artificial intelligence to create an autonomous histologic brain tumor classifier. Acutely, our compact tool aims to provide prompt, intra-operative information to help tailor surgical resections and personalized therapies. In the sub-acute setting, our CNN can provide objective triaging of molecular tests to help reduce diagnostic work-up times, costs and subjective interpretative errors. Our classifier thus has immediate translational potential as a rapid, precise and cost-effective tool to help guide personalized care in neuro-oncology.
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