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Objective: The objective of this study is to compare detection rates of extracapsular extension (ECE) of prostate cancer (PCa) using artificial intelligence (AI)-generated cancer maps versus MRI and conventional nomograms., Materials and Methods: We retrospectively analysed data from 147 patients who received MRI-targeted biopsy and subsequent radical prostatectomy between September 2016 and May 2022. AI-based software cleared by the United States Food and Drug Administration (Unfold AI, Avenda Health) was used to map 3D cancer probability and estimate ECE risk. Conventional ECE predictors including MRI Likert scores, capsular contact length of MRI-visible lesions, PSMA T stage, Partin tables, and the "PRedicting ExtraCapsular Extension" nomogram were used for comparison.Postsurgical specimens were processed using whole-mount histopathology sectioning, and a genitourinary pathologist assessed each quadrant for ECE presence. ECE predictors were then evaluated on the patient (Unfold AI versus all comparators) and quadrant level (Unfold AI versus MRI Likert score). Receiver operator characteristic curves were generated and compared using DeLong's test., Results: Unfold AI had a significantly higher area under the curve (AUC = 0.81) than other predictors for patient-level ECE prediction. Unfold AI achieved 68% sensitivity, 78% specificity, 71% positive predictive value, and 75% negative predictive value. At the quadrant level, Unfold AI exceeded the AUC of MRI Likert scores for posterior (0.89 versus 0.82, p  = 0.003), anterior (0.84 versus 0.80, p  = 0.34), and all quadrants (0.89 versus 0.82, p  = 0.002). The false negative rate of Unfold AI was lower than MRI in both the anterior (-60%) and posterior prostate (-40%)., Conclusions: Unfold AI accurately predicted ECE risk, outperforming conventional methodologies. It notably improved ECE prediction over MRI in posterior quadrants, with the potential to inform nerve-spare technique and prevent positive margins. By enhancing PCa staging and risk stratification, AI-based cancer mapping may lead to better oncological and functional outcomes for patients., Competing Interests: Unfold AI is commercially available cancer mapping software provided by Avenda Health. Dr. Brisbane receives no financial compensation from Avenda Health. He had complete control of the data and supervised manuscript preparation. Dr. Sayre performed all statistical analyses. Dr. Priester and Dr. Mota were responsible for deriving AI‐based ECE risk prediction. Dr. Priester, Dr. Mota, and Mr. Shubert are employees at Avenda Health. Dr. Natarajan and Dr. Marks are co‐founders of Avenda Health., (© 2024 The Author(s). BJUI Compass published by John Wiley & Sons Ltd on behalf of BJU International Company.)

Purpose: Defining prostate cancer contours is a complex task, undermining the efficacy of interventions such as focal therapy. A multireader multicase study compared physicians' performance using artificial intelligence (AI) vs standard-of-care methods for tumor delineation., Materials and Methods: Cases were interpreted by 7 urologists and 3 radiologists from 5 institutions with 2 to 23 years of experience. Each reader evaluated 50 prostatectomy cases retrospectively eligible for focal therapy. Each case included a T2-weighted MRI, contours of the prostate and region(s) of interest suspicious for cancer, and a biopsy report. First, readers defined cancer contours cognitively, manually delineating tumor boundaries to encapsulate all clinically significant disease. Then, after ≥ 4 weeks, readers contoured the same cases using AI software. Using tumor boundaries on whole-mount histopathology slides as ground truth, AI-assisted, cognitively-defined, and hemigland cancer contours were evaluated. Primary outcome measures were the accuracy and negative margin rate of cancer contours. All statistical analyses were performed using generalized estimating equations., Results: The balanced accuracy (mean of voxel-wise sensitivity and specificity) of AI-assisted cancer contours (84.7%) was superior to cognitively-defined (67.2%) and hemigland contours (75.9%; P < .0001). Cognitively-defined cancer contours systematically underestimated cancer extent, with a negative margin rate of 1.6% compared to 72.8% for AI-assisted cancer contours ( P < .0001)., Conclusions: AI-assisted cancer contours reduce underestimation of prostate cancer extent, significantly improving contouring accuracy and negative margin rate achieved by physicians. This technology can potentially improve outcomes, as accurate contouring informs patient management strategy and underpins the oncologic efficacy of treatment.

Focal laser ablation offers a minimally invasive method of treating solid organ tumors via hyperthermia. Real-time monitoring of the induced tissue damage is critical for clinical success, and is typically accomplished using thermal measurements and Arrhenius models. In this manuscript, the utility of interstitial fluence probes in assessing coagulation directly in real-time was assessed through a Monte Carlo simulation and an experimental study in tissue mimicking prostate phantoms. In the simulation results, fluence increases greater than 100% were observed inside the coagulation zone, as coagulation effectively acts as a 'light trap'. Moreover, the passing of the coagulation boundary at any given point was shown to correspond with an inflection in fluence with a mean absolute difference of 0.1mm and 0.4mm observed for the simulation and phantom respectively. These results suggest that interstitial fluence probes may be capable of providing real-time feedback during focal laser ablation.

Current methods of prostate cancer diagnosis and therapy rely on accurate imaging of the prostate using real-time ultrasound. Transurethral ultrasound (TUUS) may improve upon the current gold standard through improved 3D visualization and co-registration (fusion) with CT and MRI. A prototype transurethral ultrasound (TUUS) catheter-based transducer array and system was developed, featuring 32 elements with a diameter of 18F (6mm). A robust, multi-channel ultrasound transceiver was also developed to enable TUUS imaging using pulse-echo and frequency-based signal processing methods. The feasibility of a TUUS imaging system suitable for multi-modal image fusion and novel ultrasound signaling techniques was demonstrated.