Preoperative longer MUL and shorter ILD, but also intraoperative FP independently improve continence recovery after RARP. The risk calculator could be used to identify patients at high risk of UI.

No abstract

In histopathology, whole-mount sections (WMS) can be utilized to capture entire cross-sections of excised tissue, thereby reducing the number of slides per case, preserving tissue context, and minimizing cutting artefacts. Additionally, the use of WMS allows for easier correlation of histology and pre-operative imaging. However, in digital pathology, WMS are not frequently acquired due to the limited availability of whole-slide scanners that can scan double-width glass slides. Moreover, whole-mounts may still be too large to fit on a single double-width glass slide, and archival material is typically stored as tissue fragments. In this work, we present PythoStitcher, an improved version of the AutoStitcher algorithm for automatically stitching multiple tissue fragments and constructing a full-resolution WMS. PythoStitcher is based on a genetic algorithm that iteratively optimizes the affine transformation matrix for each tissue fragment using the Euclidean distance between corner points of neighbouring fragments. We demonstrate the efficiency and generalisability of PythoStitcher in a pilot validation of ten prostatectomy cases with four fragments and two oesophagectomy cases with two fragments. On average, PythoStitcher obtained the final stitching result in 60 seconds (range 51-73) for the prostatectomy cases and 68 seconds (range 43-94) for the oesophagectomy cases. The stitching accuracy was objectively evaluated by computing the average Euclidean distance between all corner points from the edges of neighbouring fragments. For the reconstructed WMS at full resolution, the average residual registration error was 1.6 mm (range 0.5-3.2) for the prostatectomy cases and 2.1 mm (range 0.5-3.8) for the oesophagectomy cases. The key strengths of PythoStitcher include its computational efficiency, the ability to reconstruct the WMS in full resolution, and an open-source implementation in Python.

No abstract

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.