OBJECTIVE:To analyze whole-mount (WM) pathology slides and to evaluate prostate ductal anatomy as a potential contrast mechanism for visualization of prostate cancer using micro-ultrasound. METHODS:Ten men scheduled to undergo radical prostatectomy for prostate cancer (PCa) were enrolled in an IRB-approved study. After prostatectomy, prostates were grossed, sectioned, and stained. All clinically significant PCa lesions were annotated by an experienced pathologist. The scanned slides were then analyzed using a custom computer vision script (MATLAB) to identify and quantify ductal features. Using the annotated lesions, binary masks were made to delineate benign and PCa groups. A color thresholding step was then applied to the tissue within each mask to select the white space where the prostate ducts are located. We extracted 2 main features as follows: the duct equivalent diameter and the ductal ratio, defined as the proportion of tissue area occupied by prostate ducts. RESULTS:A total of 54 slides were processed, with 145,150 benign and 9245 PCa ducts identified. The mean equivalent diameter of benign ducts was 155.7 ± 147.8 µm. PCa ducts had a mean equivalent diameter of 129.4 ± 94.3 µm. The average ductal ratio for benign tissue was 0.086 ± 0.029 and 0.039 ± 0.017 for PCa tissue. CONCLUSION:Prostate ducts are large enough to be resolved on micro-ultrasound but too small for conventional ultrasound. In addition, PCa tissue exhibits an approximate 50% reduction in ductal ratio, which highlights the potential of ductal anatomy as a contrast mechanism for PCa visualization using micro-ultrasound.

Abstract This prognostic study created optimized ensembles of calibrated random forest models to predict clinically significant prostate cancer (csPCa, grade group ≥2 PCa) using total prostate-specific antigen (PSA), free PSA, negative biopsy status, and age, with or without DRE and MRI data. Observational data were aggregated from cohorts in six organizations in Canada, the USA, and Czechia. Prostate biopsies were performed between 2009 and 2024. Risk models (ClarityDX Prostate + DRE, ClarityDX Prostate + MRI, and ClarityDX Prostate + MRI + DRE) were derived (training cohorts n = 1626 to 2191) and validated (validation cohorts n = 378 to 1318) from different clinical sites. The models had ROC AUC values ≥ 0.80. Adding DRE improved the ROC AUC to 0.82 while models using MRI features had ROC AUC values of 0.87 (without DRE) and 0.88 (with DRE) in the validation cohort. These four ClarityDX Prostate models offer high accuracy in predicting csPCa in individuals in variable clinical settings.

We aimed to determine if, using baseline MRI-guided biopsy (MRGB), durability of active surveillance (AS) could be pre-determined, follow-up biopsies avoided, and if by incorporating focal therapy (FT), AS extended. A cohort of 869 men in the UCLA protocol study of AS (2010-2022) was analyzed. Inclusion criteria were baseline MRI-guided biopsy (MRGB) showing Grade Group (GG) ≤ 2 and >1 year enrollment. After 2016, FT was offered to men with GG2 and those progressing to GG3. The 869 men accrued 3500 patient-years of follow-up (median follow-up 4.1 years). At baseline, men were GG1 (505), GG2 (174), and 'GG0' (190), the latter describing those with prior diagnostic GG1 or 2, but negative baseline MRGB. Overall, progression to ≥ GG3 among the 664 with serial MRGB was 7% for GG0, 19% for GG1, and 34% for GG2. During follow-up, absence of progression (negative predictive value, NPV) was correctly identified by MRI in nearly 95% of men with baseline GG0; 90% of men with GG1; and 70% of men with GG2. FT was performed in 99/393 eligible men (25%); among them, five-year probability of RP/RT-free survival was 84% compared to 46% in the no-FT group (p<0.01). Durability of AS may be linked to baseline MRGB. In men starting AS with MRGB and low-risk prostate cancer, subsequent MRI exhibits high NPV, indicating routine follow-up biopsy is avoidable. In some men, FT may allow extension of AS and deferral of surgery or radiation.

PURPOSE:A biomarker to help predict outcomes after prostate cancer (PCa) focal therapy would be of considerable interest. We sought to assess the association between treatment failure after focal therapy and the Decipher score, a tumor-based genomic classifier (GC). MATERIALS AND METHODS:We performed a post hoc analysis of a single-center phase II trial (ClinicalTrials.gov identifier: NCT03503643) in which patients with unilateral grade group (GG) 2-4 PCa (n = 108) underwent hemigland cryoablation of the prostate (2017-2021; n = 108). Pretreatment biopsy tissue was subjected to transcriptomic profiling to generate GC scores. The primary outcome was the association between GC-low (<0.45) versus GC-high (≥0.45) and in-field recurrence (GG ≥2) on magnetic resonance imaging-guided biopsy 6 months post-treatment, evaluated using multivariable logistic regression. RESULTS:In the GC-high group (n = 37), treatment failure occurred in 17 patients (46%). In the GC-low group (n = 71), treatment failure occurred in 15 patients (21%). These differences were statistically significant (odds ratio [OR], 2.61 [95% CI, 1.05 to 6.51]; P = .04). Differences at 18 months were also significant (76% v 44%; OR, 3.58 [95% CI, 1.37 to 9.36], P = .009). CONCLUSION:In patients with PCa otherwise suitable for management with focal therapy, a high GC score (≥0.45) was independently associated with treatment failure. A GC score derived from diagnostic biopsy can be used to help predict focal therapy outcomes.

You have accessJournal of UrologyProstate Cancer: Localized: Ablative Therapy I (MP25)1 May 2024MP25-02 METRICS OF TREATMENT OUTCOME FOLLOWING PARTIAL GLAND ABLATION FOR PROSTATE CANCER: PSA, MRI, OR BIOPSY? Wayne Brisbane, Shannon Richardson, Adam Kinnaird, Lorna Kwan, Samantha Gonzalez, Alan M. Priester, Ely R. Felker, Anthony E. Sisk, Merdie Delfin, and Leonard S. Marks Wayne BrisbaneWayne Brisbane , Shannon RichardsonShannon Richardson , Adam KinnairdAdam Kinnaird , Lorna KwanLorna Kwan , Samantha GonzalezSamantha Gonzalez , Alan M. PriesterAlan M. Priester , Ely R. FelkerEly R. Felker , Anthony E. SiskAnthony E. Sisk , Merdie DelfinMerdie Delfin , and Leonard S. MarksLeonard S. Marks View All Author Informationhttps://doi.org/10.1097/01.JU.0001008692.26556.39.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: We tested the hypothesis that changes in MRI or PSA following focal therapy (FT) of prostate cancer (PCa) could replace biopsy in assessment of treatment outcome. METHODS: Subjects were all 309 men with GG2 (69%) or GG3 (31%) PCa who underwent FT from 2016 to 2022 in prospective trials of High Intensity Focused Ultrasound (HIFU; NCT03620786), N=90, or cryotherapy (CRYO; NCT03503643), N=219. PSA & MRI-guided biopsy (MRGB), targeted and systematic, was performed at baseline, 6 and 18 months (Figure 1). Main outcome was presence or absence (+/-) of clinically significant PCa (csPCa; ≥GG2) on follow-up (f/u) MRGB; concordance of biopsy with MRI (+/- lesions) & PSA (decrease by≥50% from baseline) was also determined. RESULTS: 261/309 men completed biopsies at 6 months, and if no csPCa was found, they went on to a 2nd f/u biopsy at 18 mos (N=210). Mean age was 68 yrs (IQR: 63, 72); 77% were White, 5% Black, 18% other/NA. At baseline, MRI lesions (PIRADS 3-5) were present in 236 men; median PSA was 6.6 (IQR 4.7, 9.8). At 6 months after FT, csPCa was absent in 188 (72%) (successful FT) and still present in 73 (28%) (failed FT) (Table 1). Among successes, MRI lesions were no longer present in 147/187 (79%); among failures, lesions were no longer present in 47/65 (72%) (p=0.13) (Table 1). PSA decreased by >50% in most successes (127/190; 67%) and also in most failures (37/68; 54%) (p=0.57). Sensitivity of MRI was 28% and specificity was 79%. Sensitivity of PSA was 46% and specificity 67%. Similar results were seen at 2nd f/u biopsy (Table 1). CRYO and HIFU treatment results were comparable. When change in either MRI or PSA suggested absence of csPCa, the combined sensitivity was 73% and specificity was 75%. CONCLUSIONS: After FT, presence of residual csPCa is best determined by MRGB, rather than by indirect metrics, i.e., PSA or MRI. FT energy appears to have a suppressant effect on the prostate markers independent of the anti-neoplastic effect. Download PPT Source of Funding: This work was supported in part by grants R01CA158627 and R01CA218547 from the National Cancer Institute; grant UL1TR000124 from University of California, Los Angeles (UCLA) Clinical and Translational Science Institute © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e403 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Wayne Brisbane More articles by this author Shannon Richardson More articles by this author Adam Kinnaird More articles by this author Lorna Kwan More articles by this author Samantha Gonzalez More articles by this author Alan M. Priester More articles by this author Ely R. Felker More articles by this author Anthony E. Sisk More articles by this author Merdie Delfin More articles by this author Leonard S. Marks More articles by this author Expand All Advertisement PDF downloadLoading ...

AbstractObjectivesThe aim of this study is to evaluate new software (Unfold AI) in the estimation of prostate tumour volume (TV) and prediction of focal therapy outcomes.Subjects/patients and methodsSubjects were 204 men with prostate cancer (PCa) of grade groups 2–4 (GG ≥ 2), who were enrolled in a trial of partial gland cryoablation (PGA) at UCLA from 2017 to 2022. Magnetic resonance imaging (MRI)‐guided biopsy (MRGB) was performed at diagnosis and at 6 and 18 months following PGA. Utilising Unfold AI (FDA‐cleared 2022), which generates a 3D map of GG ≥ 2 PCa margins, we retrospectively estimated TV for each patient. TV was compared against conventional baseline variables as a correlate of a successful primary outcome—defined here as the absence of GG ≥ 2 on follow‐up MRGB at 6 months. Secondary outcomes were MRGB at 18 months and failure‐free survival, that is, lack of metastasis or salvage whole gland therapy. Receiver operating curves and multivariate analysis were used to determine significance.ResultsA successful primary outcome was observed in 77.7% of patients. Significant correlates of a successful ablation were percent pattern 4 and TV; areas under the curve (AUCs) were 0.60 and 0.73, respectively. GG was not a correlate of success (AUC = 0.51). A TV of 1.5 cc provided the optimal combination of sensitivity (55.8%) and specificity (85.7%) at 6 months. TV was also significantly associated with secondary outcomes. In multivariate analysis, TV was the variable most associated with 6‐ and 18‐month biopsy success (adjusted odds ratios [aORs] were 6.1 and 4.2). Utilising TV ≤ 1.5 cc as a PGA criterion would have prevented 72% of failures at the cost of 42% of successes.ConclusionThe AI‐based software Unfold AI estimates TV, which is significantly associated with biopsy outcomes after focal cryoablation. The rate of treatment success is inversely related to TV.

You have accessJournal of UrologyProstate Cancer: Detection & Screening V (PD50)1 May 2024PD50-04 CAN PSMA-TARGETED PROSTATE BIOPSY DETECT CANCER WHEN MRI-GUIDED BIOPSY IS NEGATIVE? Wayne G. Brisbane, Mark T. Topoozian, Jeremie Calais, Merdie K. Delfin, Lorna Kwan, Samantha R. Gonzalez, and Leonard S. Marks Wayne G. BrisbaneWayne G. Brisbane , Mark T. TopoozianMark T. Topoozian , Jeremie CalaisJeremie Calais , Merdie K. DelfinMerdie K. Delfin , Lorna KwanLorna Kwan , Samantha R. GonzalezSamantha R. Gonzalez , and Leonard S. MarksLeonard S. Marks View All Author Informationhttps://doi.org/10.1097/01.JU.0001008620.35181.96.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: We aimed to determine the value of using PSMA 'hot spots' in the prostate as fusion-biopsy targets in patients where MRI-guided biopsy (MRGB) might have been falsely negative. METHODS: Enrolled in a prospective trial (NCT 05160597) were 38 consecutive men average age 68 y.o. (range 56-84). who, after negative MRGB, remained PCa-suspects because of increased PSA density (PSAD) >0.15 and/or MRI lesion of PIRADS >4. All subjects were treatment-naïve; all had a previous mpMRI; and within a year of PSMA biopsy, all had a negative MRGB (targeted and/or systematic). After screening with a 68Gallium-PSMA scan, 25/38 had an intra-prostatic PSMA focus (Maximum Standardized Uptake Value, SUVmax >3, range 3-30) and underwent targeted biopsy. PSMA-PET positive Lesions were contoured using Profuse software by co-author J.C.; 12 lesions were posterior; 8 anterior; 5 both. Contoured lesions were networked into an Artemis device, and the 25 study patients underwent targeted biopsy of the PSMA-PET hotspot via PET/CT-US fusion (Figure 1). Targeted cores (∼6) were obtained as with MRGB (Sonn, J.Urol.189: 86, 2013). RESULTS: PSMA-targeted fusion biopsy was completed under local anesthesia in all 25 men without incident in an average time of 15 minutes. 10/25 men (40%) were found to harbor csPCa (GGG>2) in or around the hotspot; 12 (48%) were biopsy-negative; and 3 (12%) had GG1 findings. Of the 10 with csPCa, 4 were GG2 and 6 were >GG3; 4 of the 10 PSMA hotspots overlaid MRI lesions, but 6 were de novo. Maximum cancer core length averaged 4.7 mm +/- 2.6 SD. Both SUVmax and the 'Primary Score' (Emmett, J. Nucl. Med, 2022) were correlated with Gleason Grade Group (Figure 2). When SUVmax was >10, 7 of 10 men had csPCa. When SUVmax was <6, none of 9 men had csPCa. Neither PIRADS grade nor PSA density was related to PSMA-guided biopsy result (p=NS). CONCLUSIONS: Diagnosis of csPCa may be made by targeted biopsy of PSMA hotspots in the prostate, when MRGB is negative. Detection rate of PSMA-targeted biopsy is directly related to SUVmax and Primary Score. Download PPTDownload PPT Source of Funding: NCI-RO1CA218547 © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e1057 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Wayne G. Brisbane More articles by this author Mark T. Topoozian More articles by this author Jeremie Calais More articles by this author Merdie K. Delfin More articles by this author Lorna Kwan More articles by this author Samantha R. Gonzalez More articles by this author Leonard S. Marks More articles by this author Expand All Advertisement PDF downloadLoading ...

Solid organ tumors can be treated using focal laser ablation in which a needle housing a laser fiber is inserted into a tumor and oncologic control is achieved through thermally induced tissue coagulation. The key advantage of this procedure over conventional approaches is that it has few side-effects due to its minimally invasive nature. However, the lack of an inexpensive and accurate monitoring modality hinders widespread adoption. To address this unmet need, we have developed an optical probe capable of accurately monitoring the ablation procedure in real-time, thus ensuring complete coagulation of the cancerous tissue. This is achieved by placing the probe at the desired ablation boundary and interrogating laser-tissue interaction in real-time to detect the onset and propagation of the coagulation boundary. Here, we validate this novel monitoring modality through in silico, ex vivo and in vivo studies. Monte Carlo simulation is performed to demonstrate the utility of the probe in a range of tissues including prostate, liver and brain. In all cases the virtual optical probe is capable of detecting the coagulation boundary without any tissue specific calibration. Next, an ex vivo bovine liver study is presented in which laser ablation under optical probe monitoring is shown to be capable of creating ablation zones with a radial accuracy better than 1mm. Finally, case studies from the first clinical trial of this technology are presented showing successful adoption of this technique for monitoring laser ablation of prostate cancer.

Micro-ultrasound has recently been introduced as a low-cost alternative to multi-parametric MRI for imaging prostate cancer. Early clinical studies have demonstrated promising results; however, robust validation via comparison with whole-mount pathology has yet to be achieved. Due to micro-ultrasound probe design and tissue deformation during scanning, it is difficult to accurately correlate micro-ultrasound imaging planes with ground truth whole-mount pathology slides. In this study, we developed a multi-step methodology to co-register micro-ultrasound and MRI to whole-mount pathology. The three-step process had a registration error of 3.90 ± 0.11 mm and consists of: (1) micro-ultrasound image reconstruction, (2) 3D landmark registration of micro-ultrasound to MRI, and (3) 2D capsule registration of MRI to whole-mount pathology. This process was then used in a preliminary reader study to compare the diagnostic accuracy of micro-ultrasound and MRI in 15 patients who underwent radical prostatectomy for prostate cancer. Micro-ultrasound was found to have equivalent performance to retrospective MRI review for index lesion detection (91.7% vs. 80%), while demonstrating an increased detection of tumor extent (52.5% vs. 36.7%) with similar false positive regions-of-interest (38.3% vs. 40.8%). Prospective MRI review had reduced detection of index lesions (73.3%) and tumor extent (18.9%) but improved false positive regions-of-interest (22.7%) relative to micro-ultrasound and retrospective MRI. Further evaluation is needed with a larger sample size.

The current prostate cancer (PCa) screen test, prostate-specific antigen (PSA), has a high sensitivity for PCa but low specificity for high-risk, clinically significant PCa (csPCa), resulting in overdiagnosis and overtreatment of non-csPCa. Early identification of csPCa while avoiding unnecessary biopsies in men with non-csPCa is challenging. We built an optimized machine learning platform (ClarityDX) and showed its utility in generating models predicting csPCa. Integrating the ClarityDX platform with blood-based biomarkers for clinically significant PCa and clinical biomarker data from a 3448-patient cohort, we developed a test to stratify patients’ risk of csPCa; called ClarityDX Prostate. When predicting high risk cancer in the validation cohort, ClarityDX Prostate showed 95% sensitivity, 35% specificity, 54% positive predictive value, and 91% negative predictive value, at a ≥ 25% threshold. Using ClarityDX Prostate at this threshold could avoid up to 35% of unnecessary prostate biopsies. ClarityDX Prostate showed higher accuracy for predicting the risk of csPCa than PSA alone and the tested model-based risk calculators. Using this test as a reflex test in men with elevated PSA levels may help patients and their healthcare providers decide if a prostate biopsy is necessary.

e17105 Background: In patients suspected of prostate cancer, the decision to perform a biopsy hinges on available clinical data. Magnetic resonance imaging (MRI) and digital rectal exam (DRE) data are informative but may not be available. We created accurate models for clinically significant prostate cancer (csPCa), flexible to MRI and DRE data availability. Methods: Optimized ensembles of calibrated random forest models predicting csPCa (Grade Group ≥2) used total PSA, free PSA, prior negative biopsy status, and age, with or without DRE and MRI data (prostate volume and PI-RADS score). Risk models were derived (training cohorts n=1257 to 2191) and validated (validation cohorts n=317 to 1257) from different clinical sites. Models were evaluated by the area under the receiver operating characteristic curve (ROC AUC), sensitivity, specificity, positive predictive value, and negative predictive value, using thresholds providing ~ 95% sensitivity. Feature importance was determined by SHAP analysis. Results: All models had an AUC of at least 0.80, showing that predicting csPCa can be accurate without MRI or DRE data. Including MRI data significantly increased the AUC in the validation cohort (ClarityDX Prostate 0.80 vs ClarityDX Prostate +MRI 0.87). DRE had moderate value for models without MRI data (ClarityDX Prostate 0.80 vs ClarityDX Prostate +DRE 0.82) and minor value with MRI data (ClarityDX Prostate +MRI vs ClarityDX Prostate +DRE+MRI; AUC 0.87 vs 0.87; specificity 45% vs 47%, Table). Mean absolute SHAP values were highest for PI-RADS and prostate volume. Conclusions: These optimized risk models provide high accuracy for predicting csPCa in various clinical settings. Including MRI data greatly increases model accuracy, while DRE has a smaller effect on model accuracy. [Table: see text]

You have accessJournal of UrologyProstate Cancer: Localized: Ablative Therapy I (MP25)1 May 2024MP25-08 SOFTWARE TO DETERMINE EXTENT OF TUMOR MARGINS IN FOCAL THERAPY OF PROSTATE CANCER (UNFOLD-AI®) Wayne G. Brisbane, Alan Priester, Mark T. Topoozian, Anissa V. Nguyen, Merdie K. Delfin, Samantha R. Gonzalez, and Leonard S. Marks Wayne G. BrisbaneWayne G. Brisbane , Alan PriesterAlan Priester , Mark T. TopoozianMark T. Topoozian , Anissa V. NguyenAnissa V. Nguyen , Merdie K. DelfinMerdie K. Delfin , Samantha R. GonzalezSamantha R. Gonzalez , and Leonard S. MarksLeonard S. Marks View All Author Informationhttps://doi.org/10.1097/01.JU.0001008692.26556.39.08AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Margins of prostate cancer (PCa) are poorly defined by MRI, but margins must be treated to ensure a complete ablation. Herein we test a recently FDA-cleared software, Unfold-AI®, to improve the definition of tumor margins and guide focal therapy. METHODS: Unfold-AI®, a PCa margin-prediction software based on artificial intelligence (AI), was described by Priester (Eur.Urol. O.S. vol. 54, page 20-27, 2023). It is derived from biopsy and clinical information of 875 men undergoing 1145 MRI-guided biopsies (13k cores, 128k MR images). The Unfold-AI® output is a patient-specific encapsulation confidence score (ECS) estimating the probability of complete tumor ablation when recommended margins are followed. ECS values from 0 to 1 (Figure 1). are directly related to the probability of focal therapy success.We retrospectively applied Unfold-AI® in 118 treatment-naïve men in a trial of hemi-gland cryotherapy (CRYO) for PCa >GG2 (NCT03503643). Median age was 68 yr (IQR 63-72); 67% were Caucasian, 5% Black, 28% other; PSA 6.8 ng/ml (IQR 4.7-10.3); prostate volume 44cc (IQR 32-56). MRI-guided biopsy (MRGB), targeted + systematic, was performed before and 6 months after CRYO. To determine predictive value, the ECS score and standard PCa variables were compared against the presence or absence of >GG2 in post-CRYO MRGB. ROC analysis was used to assess the significance of the metrics in predicting CRYO outcomes (Table 1). RESULTS: Baseline GG was 66.8% GG2, 28.0% GG3, and 5.2% GG4. Treatment success (no >GG2 on MRGB) was 77.7% and was not related to baseline GG. Univariate ROC analysis (Table 1) showed that max cancer core length, % Gleason pattern 4, and ECS were predictive of success. However, in a multivariate logistic regression, only ECS≥0.7 remained as a predictor of success (aOR 4.7, 1.7-12.9). Sensitivity and specificity for ECS≥0.7 was 70% (61-79) and 68% (51-85), respectively. CONCLUSIONS: In predicting focal therapy success, tumor margins -- as determined here by the ECS of Unfold-AI® --may be more important than other baseline parameters (e.g., GG). Download PPT Source of Funding: NCI-RO1CA218547 © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e407 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Wayne G. Brisbane More articles by this author Alan Priester More articles by this author Mark T. Topoozian More articles by this author Anissa V. Nguyen More articles by this author Merdie K. Delfin More articles by this author Samantha R. Gonzalez More articles by this author Leonard S. Marks More articles by this author Expand All Advertisement PDF downloadLoading ...