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KMID : 1144120230130010031
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Biomedical Engineering Letters
2023 Volume.13 No. 1 p.31 ~ p.40
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Deep learning prediction of non-perfused volume without contrast agents during prostate ablation therapy
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Cameron Wright
Pietari Makela
Alexandre Bigot
Mikael Anttinen
Peter J. Bostrom
Roberto Blanco Sequeiros
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Abstract
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The non-perfused volume (NPV) is an important indicator of treatment success immediately after prostate ablation. However,
visualization of the NPV frst requires an injection of MRI contrast agents into the bloodstream, which has many downsides.
Purpose of this study was to develop a deep learning model capable of predicting the NPV immediately after prostate ablation
therapy without the need for MRI contrast agents. A modifed 2D deep learning UNet model was developed to predict the
post-treatment NPV. MRI imaging data from 95 patients who had previously undergone prostate ablation therapy for treatment of localized prostate cancer were used to train, validate, and test the model. Model inputs were T1/T2-weighted and
thermometry MRI images, which were always acquired without any MRI contrast agents and prior to the fnal NPV image
on treatment-day. Model output was the predicted NPV. Model accuracy was assessed using the Dice-Similarity Coefcient
(DSC) by comparing the predicted to ground truth NPV. A radiologist also performed a qualitative assessment of NPV.
Mean (std) DSC score for predicted NPV was 85%±8.1% compared to ground truth. Model performance was signifcantly
better for slices with larger prostate radii (>24 mm) and for whole-gland rather than partial ablation slices. The predicted
NPV was indistinguishable from ground truth for 31% of images. Feasibility of predicting NPV using a UNet model without
MRI contrast agents was clearly established. If developed further, this could improve patient treatment outcomes and could
obviate the need for contrast agents altogether.
Trial Registration Numbers Three studies were used to populate the data: NCT02766543, NCT03814252 and NCT03350529.
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KEYWORD
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High intensity focused ultrasound, Deep learning, Contrast-enhanced MRI, UNet model, Clinical trialsthermal ablation, Control systems engineering, Treatment optimization
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