Towards democratizing AI in MR-based prostate cancer diagnosis: 3.0 to 1.5 Tesla

Andrew Grebenisan; Alireza Sedghi; Alexandre Menard; Jason Izard; Robert Siemens; Parvin Mousavi

doi:10.1117/12.2549413

16 March 2020 Towards democratizing AI in MR-based prostate cancer diagnosis: 3.0 to 1.5 Tesla

Andrew Grebenisan, Alireza Sedghi, Alexandre Menard, Jason Izard, Robert Siemens, Parvin Mousavi

Proceedings Volume 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 113150R (2020) https://doi.org/10.1117/12.2549413
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

In the Western world, nearly 1 in 14 men will be diagnosed with prostate cancer in their lifetimes. The gold standard for detection of PCa is histopathology analysis of biopsy cores taken using trans-rectal ultrasound (TRUS) guidance, a procedure that has a false negative rate of 30 − 45% and serious side effects. Multiparametric MRI (MP-MRI) is quickly becoming part of the standard of care to detect PCa lesions. According to recent multi-centre studies, it has the potential to decrease false positives for PCa detection and reduce the need for biopsy. At the same time, deep learning approaches for aiding radiologists in PCa diagnosis have been on the rise. Most of the solutions in the literature benefit from abundant high-quality data, which limits their translation to clinical settings. For PCa in particular, close to 83% of clinical MRI systems in Canada are 1.5 T, and many centres may not have the high throughput volume of patients required for building locally accurate machine learning models. In this paper, we present preliminary results from a deep learning framework built using publicly available 3.0 T MP-MRI data and re-purposed for 1.5 T clinical data. We achieve areas under the receiver operating curve of up to 0.76 and provide visualization of the most informative areas of the images for the deep models. Our proposed approach has the potential to allow local hospitals to use pre-built AI models fine-tuned for their own cases, taking advantage of externally available large data sets.

Conference Presentation

Citation Download Citation

Andrew Grebenisan, Alireza Sedghi, Alexandre Menard, Jason Izard, Robert Siemens, and Parvin Mousavi "Towards democratizing AI in MR-based prostate cancer diagnosis: 3.0 to 1.5 Tesla", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113150R (16 March 2020); https://doi.org/10.1117/12.2549413

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