2017 Researcher Phedias Diamandis

“The brAIn Project: biological rendering through Artificial Intelligence and neural networks”

Phedias Diamandis
Neuropathologist / Clinician Scientist / Assistant Professor, University Health Network / Princess Margaret Cancer Centre

Project Summary:

Phedias Diamandis - 2017 Research Grant Recipient

Brain tumours represent a diverse group of diseases with highly variable therapies and outcomes. A key way to predict how a tumour will behave is by analyzing its specific morphologic features under the microscope. The human eye, however, cannot reliably detect subtle differences. This may explain why patients with the same diagnoses can experience dramatically different outcomes. Although new “molecular” technologies can reliably differentiate between tumour types, molecular testing is oftentimes unavailable and doctors must still rely on microscopic findings to make important clinical decisions.

Recently, advances in “Artificial Intelligence (AI)” allow computers to excel at analyzing images to identify extremely subtle differences. Dr. Diamandis’ team aims to take advantage of AI, and train computers to differentiate between brain tumour types.

Training involves exposing computers to a large series of images with known clinical outcomes (survival, therapy response) to allow AI to “discover” microscopic patterns associated with these specific clinical events. Because computers can process larger amounts of information than humans, it is expected they will be better able to predict tumour behavior from their microscopic appearance. This AI-assisted approach will hopefully allow improved differentiation of brain tumour types and offer more accurate predictors of outcome and treatment response to patients.

Project Update:

Brain tumours represent a diverse group of diseases with highly variable therapies and outcomes. A key way to predict how a tumour will behave is by analyzing its specific morphologic features under the microscope. This “diagnostic” exercise, however, is somewhat qualitative and prone to human interpretive errors. Although new “molecular” technologies are improving this, molecular testing is still largely guiding by initial microscopic interpretations and oftentimes unavailable at remote centres or in timeframes doctors need to make important clinical decisions.

Recently, advances in “Artificial Intelligence" (AI) now allow computers to excel at image analysis. Dr. Diamandis’ team is showing that AI can be effectively used to train computers to objectively and quantitatively differentiate brain tumours. Such AI-based tools are poised to revolutionize brain tumour care by providing doctors and their patients with accurate, cost-effective and timely diagnoses to guide optimal care. Importantly, these AI tools can be easily shared across the internet, allowing patients even at remote cancer centres to also benefit from this technology. This technology will help ensure that all patient, irrespective of their location will have access to expert-level diagnostic interpretations.

One limitation that has prevented the implementation of AI into clinical practice so far is computer-based errors. The initial paper (Fuast et al, BMC Bioinformatics 2018) made possible by funding from the Brain Tumour Foundation of Canada Research Grant outlines the blueprint of how to substantially reduce the errors made by AI diagnostic tools. This important innovation will allow researchers to safely integrate artificial intelligence into the diagnostic workflow to better understand the benefits. Dr. Diamandis' team now plans to continue expanding the capabilities of their AI-tool to also recognize additional, less common, brain tumour types that non-expert pathologists may misinterpret. While they are still adding additional cases and brain tumour types, their tool is already very good at telling humans when it needs helps. This key feature will allow Dr. Diamandis' team to begin implementing their tool into routine clinical practice to see if it can provide faster diagnoses read by both humans and robots. This "double read" will help provide assurance to patients that their diagnosis is accurate and that they are getting the proper treatment for their specific disease. For developing countries, this tool will hopefully provide high-level diagnostic services similar to the standard-of-care at larger academic centres. This could help provide more equal access to expert-level brain tumour diagnostics for patient globally. 

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