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.
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