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Background: Tissue preservation strategies have been increasingly used for the management of localized prostate cancer. Focal ablation using ultrasound-guided high-intensity focused ultrasound (HIFU) has demonstrated promising short and medium-term oncological outcomes. Advancements in HIFU therapy such as the introduction of tissue change monitoring (TCM) aim to further improve treatment efficacy., Objective: To evaluate the association between intraoperative TCM during HIFU focal therapy for localized prostate cancer and oncological outcomes 12 mo afterward., Design, Setting, and Participants: Seventy consecutive men at a single institution with prostate cancer were prospectively enrolled. Men with prior treatment, metastases, or pelvic radiation were excluded to obtain a final cohort of 55 men., Intervention: All men underwent HIFU focal therapy followed by magnetic resonance (MR)-fusion biopsy 12 mo later. Tissue change was quantified intraoperatively by measuring the backscatter of ultrasound waves during ablation., Outcome Measurements and Statistical Analysis: Gleason grade group (GG) ≥2 cancer on postablation biopsy was the primary outcome. Secondary outcomes included GG ≥1 cancer, Prostate Imaging Reporting and Data System (PI-RADS) scores ≥3, and evidence of tissue destruction on post-treatment magnetic resonance imaging (MRI). A Student's t - test analysis was performed to evaluate the mean TCM scores and efficacy of ablation measured by histopathology. Multivariate logistic regression was also performed to identify the odds of residual cancer for each unit increase in the TCM score., Results and Limitations: A lower mean TCM score within the region of the tumor (0.70 vs 0.97, p = 0.02) was associated with the presence of persistent GG ≥2 cancer after HIFU treatment. Adjusting for initial prostate-specific antigen, PI-RADS score, Gleason GG, positive cores, and age, each incremental increase of TCM was associated with an 89% reduction in the odds (odds ratio: 0.11, confidence interval: 0.01-0.97) of having residual GG ≥2 cancer on postablation biopsy. Men with higher mean TCM scores (0.99 vs 0.72, p = 0.02) at the time of treatment were less likely to have abnormal MRI (PI-RADS ≥3) at 12 mo postoperatively. Cases with high TCM scores also had greater tissue destruction measured on MRI and fewer visible lesions on postablation MRI., Conclusions: Tissue change measured using TCM values during focal HIFU of the prostate was associated with histopathology and radiological outcomes 12 mo after the procedure., Patient Summary: In this report, we looked at how well ultrasound changes of the prostate during focal high-intensity focused ultrasound (HIFU) therapy for the treatment of prostate cancer predict patient outcomes. We found that greater tissue change measured by the HIFU device was associated with less residual cancer at 1 yr. This tool should be used to ensure optimal ablation of the cancer and may improve focal therapy outcomes in the future., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Magnetic resonance imaging (MRI) underestimation of prostate cancer extent complicates the definition of focal treatment margins., Objective: To validate focal treatment margins produced by an artificial intelligence (AI) model., Design Setting and Participants: Testing was conducted retrospectively in an independent dataset of 50 consecutive patients who had radical prostatectomy for intermediate-risk cancer. An AI deep learning model incorporated multimodal imaging and biopsy data to produce three-dimensional cancer estimation maps and margins. AI margins were compared with conventional MRI regions of interest (ROIs), 10-mm margins around ROIs, and hemigland margins. The AI model also furnished predictions of negative surgical margin probability, which were assessed for accuracy., Outcome Measurements and Statistical Analysis: Comparing AI with conventional margins, sensitivity was evaluated using Wilcoxon signed-rank tests and negative margin rates using chi-square tests. Predicted versus observed negative margin probability was assessed using linear regression. Clinically significant prostate cancer (International Society of Urological Pathology grade ≥2) delineated on whole-mount histopathology served as ground truth., Results and Limitations: The mean sensitivity for cancer-bearing voxels was higher for AI margins (97%) than for conventional ROIs (37%, p  < 0.001), 10-mm ROI margins (93%, p  = 0.24), and hemigland margins (94%, p  < 0.001). For index lesions, AI margins were more often negative (90%) than conventional ROIs (0%, p  < 0.001), 10-mm ROI margins (82%, p  = 0.24), and hemigland margins (66%, p  = 0.004). Predicted and observed negative margin probabilities were strongly correlated (R 2  = 0.98, median error = 4%). Limitations include a validation dataset derived from a single institution's prostatectomy population., Conclusions: The AI model was accurate and effective in an independent test set. This approach could improve and standardize treatment margin definition, potentially reducing cancer recurrence rates. Furthermore, an accurate assessment of negative margin probability could facilitate informed decision-making for patients and physicians., Patient Summary: Artificial intelligence was used to predict the extent of tumors in surgically removed prostate specimens. It predicted tumor margins more accurately than conventional methods., (© 2023 The Author(s).)

Purpose: To evaluate the performance of multiparametric magnetic resonance imaging (mpMRI) and PSA testing in follow-up after high intensity focused ultrasound (HIFU) focal therapy for localized prostate cancer., Methods: A total of 73 men with localized prostate cancer were prospectively enrolled and underwent focal HIFU followed by per-protocol PSA and mpMRI with systematic plus targeted biopsies at 12 months after treatment. We evaluated the association between post-treatment mpMRI and PSA with disease persistence on the post-ablation biopsy. We also assessed post-treatment functional and oncological outcomes., Results: Median age was 69 years (Interquartile Range (IQR): 66-74) and median PSA was 6.9 ng/dL (IQR: 5.3-9.9). Of 19 men with persistent GG ≥ 2 disease, 58% (11 men) had no visible lesions on MRI. In the 14 men with PIRADS 4 or 5 lesions, 7 (50%) had either no cancer or GG 1 cancer at biopsy. Men with false negative mpMRI findings had higher PSA density (0.16 vs. 0.07 ng/mL 2 , P = 0.01). No change occurred in the mean Sexual Health Inventory for Men (SHIM) survey scores (17.0 at baseline vs. 17.7 post-treatment, P = 0.75) or International Prostate Symptom Score (IPSS) (8.1 at baseline vs. 7.7 at 24 months, P = 0.81) after treatment., Conclusions: Persistent GG ≥ 2 cancer may occur after focal HIFU. mpMRI alone without confirmatory biopsy may be insufficient to rule out residual cancer, especially in patients with higher PSA density. Our study also validates previously published studies demonstrating preservation of urinary and sexual function after HIFU treatment., Competing Interests: Conflict of interests No relevant financial disclosures or conflict of interests. There is no applicable funding for this study., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care., Competing Interests: The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: MR has research grants from GE Healthcare and Philips Healthcare., (© The Author(s), 2022.)