Meeting Banner
Abstract #2149

Deep learning-assisted ablative margin analysis for MR-guided prostate focal cryoablation: feasibility and initial retrospective validation

Daan N. Schouten1, Cees H. Slump2, Jurgen J. Fütterer1,2, Joyce G.R. Bomers1, and Christiaan G. Overduin1
1Medical Imaging, Radboudumc, Nijmegen, Netherlands, 2Robotics and Mechatronics, University of Twente, Enschede, Netherlands


MR-guided focal cryoablation is an emerging treatment option for localized prostate cancer, however local recurrence due to incomplete ablation is not uncommon. Ablation completeness is typically assessed on intraprocedural imaging by side-by-side comparison, but a volumetric approach is lacking. We present a deep learning-assisted algorithm for near real-time ablative margin monitoring during cryoablation procedures. Retrospective validation in 27 patients after MR-guided prostate cryoablation demonstrated significantly smaller minimal ablative margin and percentual tumour coverage for patients with versus without local recurrence. Prospective use may aid physicians in reducing the risk of local recurrence during prostate cryoablation procedures.

This abstract and the presentation materials are available to members only; a login is required.

Join Here