RESUMEN
Objective: To construct a convolutional neural network (CNN) model that can recognize and delineate anatomic structures on intraoperative video frames of robot-assisted radical prostatectomy (RARP) and to use these annotations to predict the surgical urethral length (SUL). Background: Urethral dissection during RARP impacts patient urinary incontinence (UI) outcomes, and requires extensive training. Large differences exist between incontinence outcomes of different urologists and hospitals. Also, surgeon experience and education are critical toward optimal outcomes. Therefore, new approaches are warranted. SUL is associated with UI. Artificial intelligence (AI) surgical image segmentation using a CNN could automate SUL estimation and contribute toward future AI-assisted RARP and surgeon guidance. Methods: Eighty-eight intraoperative RARP videos between June 2009 and September 2014 were collected from a single center. Two hundred sixty-four frames were annotated according to prostate, urethra, ligated plexus, and catheter. Thirty annotated images from different RARP videos were used as a test data set. The dice (similarity) coefficient (DSC) and 95th percentile Hausdorff distance (Hd95) were used to determine model performance. SUL was calculated using the catheter as a reference. Results: The DSC of the best performing model were 0.735 and 0.755 for the catheter and urethra classes, respectively, with a Hd95 of 29.27 and 72.62, respectively. The model performed moderately on the ligated plexus and prostate. The predicted SUL showed a mean difference of 0.64 to 1.86 mm difference vs human annotators, but with significant deviation (standard deviation = 3.28-3.56). Conclusion: This study shows that an AI image segmentation model can predict vital structures during RARP urethral dissection with moderate to fair accuracy. SUL estimation derived from it showed large deviations and outliers when compared with human annotators, but with a small mean difference (<2 mm). This is a promising development for further research on AI-assisted RARP.
RESUMEN
Cancer-associated RNF43 mutations lead to activation of ß-catenin signaling through aberrantly increasing Wnt-receptor levels at the membrane. Importantly, inactivating RNF43 mutations have been suggested to render cancer cells sensitive to Wnt-based therapeutics. However, the extent to which RNF43 mutations lead to impaired regulation of Wnt/ß-catenin signaling has been poorly investigated. Here, we observed that tumors with a functional mismatch repair system show a predominant 5'-location of truncating RNF43 mutations, suggesting C-terminal truncations such as the most commonly reported p.G659fs mutation, do not affect ß-catenin signaling. In accordance, expressing C-terminal truncation mutants and wild-type RNF43, showed equal effects on ß-catenin signaling, Wnt-receptor turnover, and DVL-binding. We confirmed these observations at endogenous levels by CRISPR-Cas9-mediated knockout of G659fs RNF43 expression in KM12 cells and generating comparable mutations in HEK293T cells. We could not confirm previous reports linking RNF43 to p53 and E-cadherin breakdown. Our data also suggest that only colorectal cancer cells harboring N-terminal mutations of RNF43 convey Wnt-dependency onto the tumor cells. Results of this study have potentially important clinical implications indicating that Wnt-based therapeutics should be applied cautiously in cancer patients harboring RNF43 mutations.
