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Purpose We explored the impact of magnetic resonance imaging-ultrasound fusion prostate biopsy on the prediction of final surgical pathology. Materials and Methods A total of 54 consecutive men undergoing radical prostatectomy at UCLA after fusion biopsy were included in this prospective, institutional review board approved pilot study. Using magnetic resonance imaging-ultrasound fusion, tissue was obtained from a 12-point systematic grid (mapping biopsy) and from regions of interest detected by multiparametric magnetic resonance imaging (targeted biopsy). A single radiologist read all magnetic resonance imaging, and a single pathologist independently rereviewed all biopsy and whole mount pathology, blinded to prior interpretation and matched specimen. Gleason score concordance between biopsy and prostatectomy was the primary end point. Results Mean patient age was 62 years and median prostate specific antigen was 6.2 ng/ml. Final Gleason score at prostatectomy was 6 (13%), 7 (70%) and 8–9 (17%). A tertiary pattern was detected in 17 (31%) men. Of 45 high suspicion (image grade 4–5) magnetic resonance imaging targets 32 (71%) contained prostate cancer. The per core cancer detection rate was 20% by systematic mapping biopsy and 42% by targeted biopsy. The highest Gleason pattern at prostatectomy was detected by systematic mapping biopsy in 54%, targeted biopsy in 54% and a combination in 81% of cases. Overall 17% of cases were upgraded from fusion biopsy to final pathology and 1 (2%) was downgraded. The combination of targeted biopsy and systematic mapping biopsy was needed to obtain the best predictive accuracy. Conclusions In this pilot study magnetic resonance imaging-ultrasound fusion biopsy allowed for the prediction of final prostate pathology with greater accuracy than that reported previously using conventional methods (81% vs 40% to 65%). If confirmed, these results will have important clinical implications. [ABSTRACT FROM AUTHOR]

Objectives Targeted biopsy, using magnetic resonance (MR)-ultrasound (US) fusion, may allow tracking of specific cancer sites in the prostate. We aimed to evaluate the initial use of the technique to follow tumor sites in men on active surveillance of prostate cancer. Methods and materials A total of 53 men with prostate cancer (all T1c category) underwent rebiopsy of 74 positive biopsy sites, which were tracked and targeted using the Artemis MR-US fusion device (Eigen, Grass Valley, CA) from March 2010 through January 2013. The initial biopsy included 12 cores from a standard template (mapped by software) and directed biopsies from regions of interest seen on MR imaging (MRI). In the repeat biopsy, samples were taken from sites containing cancer at the initial biopsy. Outcomes of interest at second MR-US biopsy included (a) presence of any cancer and (b) presence of clinically significant cancer. Results All cancers on initial biopsy had either Gleason score 3+3 = 6 ( n = 63) or 3+4 = 7 ( n = 11). At initial biopsy, 23 cancers were within an MRI target, and 51 were found on systematic biopsy. Cancer detection rate on repeat biopsy (29/74, 39%) was independent of Gleason score on initial biopsy ( P = not significant) but directly related to initial cancer core length ( P <0.02). Repeat sampling of cancerous sites within MRI targets was more likely to show cancer than resampling of tumorous systematic sites (61% vs. 29%, P = 0.005). When initial cancer core length was≥4 mm within an MRI target, more than 80% (5/6) of follow-up tracking biopsies were positive. An increase of Gleason score was uncommon (9/74, 12%). Conclusions Monitoring of specific prostate cancer–containing sites may be achieved in some men using an electronic tracking system. The chances of finding tumor on repeat specific-site sampling was directly related to the length of tumor in the initial biopsy core and presence of tumor within an MRI target; upgrading of Gleason score was uncommon. Further research is required to evaluate the potential utility of site-specific biopsy tracking for patients with prostate cancer on active surveillance. [ABSTRACT FROM AUTHOR]

Recent advances in multiparametric magnetic resonance imaging (MRI) have enabled image-guided detection of prostate cancer. Fusion of MRI with real-time ultrasound (US) allows the information from MRI to be used to direct biopsy needles under US guidance in an office-based procedure. Fusion can be performed either cognitively or electronically, using a fusion device. Fusion devices allow superimposition (coregistration) of stored MRI images on real-time US images; areas of suspicion found on MRI can then serve as targets during US-guided biopsy. Currently available fusion devices use a variety of technologies to perform coregistration: robotic tracking via a mechanical arm with built-in encoders (Artemis/Eigen, BioJet/Geoscan); electromagnetic tracking (UroNav/Philips-Invivo, Hi-RVS/Hitachi); or tracking with a 3D US probe (Urostation/Koelis). Targeted fusion biopsy has been shown to identify more clinically significant cancers and fewer insignificant cancers than conventional biopsy. Fusion biopsy appears to be a major advancement over conventional biopsy because it allows (1) direct targeting of suspicious areas not seen on US and (2) follow-up biopsy of specific cancerous sites in men undergoing active surveillance. [ABSTRACT FROM AUTHOR]

Objective: To determine whether multiparametric magnetic resonance imaging might improve the identification of patients with higher risk disease at diagnosis and thereby reduce the incidence of undergrading or understaging. Methods: We retrospectively reviewed the clinical records of 115 patients who underwent multiparametric magnetic resonance imaging before radical prostatectomy. We used Epstein's criteria of insignificant disease with and without a magnetic resonance imaging (MRI) parameter (apparent diffusion coefficient) to calculate sensitivity, specificity, as well as negative and positive predictive values [NPV and PPV] across varying definitions of clinically significant cancer based on Gleason grade and tumor volume (0.2 mL, 0.5 mL, and 1.3 mL) on whole-mount prostate specimens. Logistic regression analysis was performed to determine the incremental benefit of MRI in delineating significant cancer. Results: The majority had a prostate-specific antigen from 4.1-10.0 (67%), normal rectal examinations (90%), biopsy Gleason score ≤6 (68%), and ≤2 cores positive (55%). Of the 58 patients pathologically staged with Gleason 7 or pT3 disease at prostatectomy, Epstein's criteria alone missed 12 patients (sensitivity of 79% and NPV of 68%). Addition of apparent diffusion coefficient improved the sensitivity and NPV for predicting significant disease at prostatectomy to 93% and 84%, respectively. MRI improved detection of large Gleason 6 (≥1.3 mL, P = .006) or Gleason ≥7 lesions of any size (P <.001). Conclusion: Integration of MRI with existing clinical staging criteria helps identify patients with significant cancer. Clinicians should consider utilizing MRI in the decision-making process. [Copyright &y& Elsevier]

Background Purpose Methods Results Conclusion Renal cell carcinoma (RCC) is a common cancer that varies in clinical behavior. Clear cell RCC (ccRCC) is the most common RCC subtype, with both aggressive and indolent manifestations. Indolent ccRCC is often low‐grade without necrosis and can be monitored without treatment. Aggressive ccRCC is often high‐grade and can cause metastasis and death if not promptly detected and treated. While most RCCs are detected on computed tomography (CT) scans, aggressiveness classification is based on pathology images acquired from invasive biopsy or surgery.CT imaging‐based aggressiveness classification would be an important clinical advance, as it would facilitate non‐invasive risk stratification and treatment planning. Here, we present a novel machine learning method, Correlated Feature Aggregation By Region (CorrFABR), for CT‐based aggressiveness classification of ccRCC.CorrFABR is a multimodal fusion algorithm that learns from radiology and pathology images, and clinical variables in a clinically‐relevant manner. CorrFABR leverages registration‐independent radiology (CT) and pathology image correlations using features from vision transformer‐based foundation models to facilitate aggressiveness assessment on CT images. CorrFABR consists of three main steps: (a) Feature aggregation where region‐level features are extracted from radiology and pathology images at widely varying image resolutions, (b) Fusion where radiology features correlated with pathology features (pathology‐informed CT biomarkers) are learned, and (c) Classification where the learned pathology‐informed CT biomarkers, together with clinical variables of tumor diameter, gender, and age, are used to distinguish aggressive from indolent ccRCC using multi‐layer perceptron‐based classifiers. Pathology images are only required in the first two steps of CorrFABR, and are not required in the prediction module. Therefore, CorrFABR integrates information from CT images, pathology images, and clinical variables during training, but for inference, it relies solely on CT images and clinical variables, ensuring its clinical applicability. CorrFABR was trained with heterogenous, publicly‐available data from 298 ccRCC tumors (136 indolent tumors, 162 aggressive tumors) in a five‐fold cross‐validation setup and evaluated on an independent test set of 74 tumors with a balanced distribution of aggressive and indolent tumors. Ablation studies were performed to test the utility of each component of CorrFABR.CorrFABR outperformed the other classification methods, achieving an ROC‐AUC (area under the curve) of 0.855 ± 0.0005 (95% confidence interval: 0.775, 0.947), F1‐score of 0.793 ± 0.029, sensitivity of 0.741 ± 0.058, and specificity of 0.876 ± 0.032 in classifying ccRCC as aggressive or indolent subtypes. It was found that pathology‐informed CT biomarkers learned through registration‐independent correlation learning improves classification performance over using CT features alone, irrespective of the kind of features or the classification model used. Tumor diameter, gender, and age provide complementary clinical information, and integrating pathology‐informed CT biomarkers with these clinical variables further improves performance.CorrFABR provides a novel method for CT‐based aggressiveness classification of ccRCC by enabling the identification of pathology‐informed CT biomarkers, and integrating them with clinical variables. CorrFABR enables learning of these pathology‐informed CT biomarkers through a novel registration‐independent correlation learning module that considers unaligned radiology and pathology images at widely varying image resolutions. [ABSTRACT FROM AUTHOR]

Objectives Materials and Methods Results Conclusion To externally validate the performance of the DeepDx Prostate artificial intelligence (AI) algorithm (Deep Bio Inc., Seoul, South Korea) for Gleason grading on whole‐mount prostate histopathology, considering potential variations observed when applying AI models trained on biopsy samples to radical prostatectomy (RP) specimens due to inherent differences in tissue representation and sample size.The commercially available DeepDx Prostate AI algorithm is an automated Gleason grading system that was previously trained using 1133 prostate core biopsy images and validated on 700 biopsy images from two institutions. We assessed the AI algorithm's performance, which outputs Gleason patterns (3, 4, or 5), on 500 1‐mm2 tiles created from 150 whole‐mount RP specimens from a third institution. These patterns were then grouped into grade groups (GGs) for comparison with expert pathologist assessments. The reference standard was the International Society of Urological Pathology GG as established by two experienced uropathologists with a third expert to adjudicate discordant cases. We defined the main metric as the agreement with the reference standard, using Cohen's kappa.The agreement between the two experienced pathologists in determining GGs at the tile level had a quadratically weighted Cohen's kappa of 0.94. The agreement between the AI algorithm and the reference standard in differentiating cancerous vs non‐cancerous tissue had an unweighted Cohen's kappa of 0.91. Additionally, the AI algorithm's agreement with the reference standard in classifying tiles into GGs had a quadratically weighted Cohen's kappa of 0.89. In distinguishing cancerous vs non‐cancerous tissue, the AI algorithm achieved a sensitivity of 0.997 and specificity of 0.88; in classifying GG ≥2 vs GG 1 and non‐cancerous tissue, it demonstrated a sensitivity of 0.98 and specificity of 0.85.The DeepDx Prostate AI algorithm had excellent agreement with expert uropathologists and performance in cancer identification and grading on RP specimens, despite being trained on biopsy specimens from an entirely different patient population. [ABSTRACT FROM AUTHOR]

Purpose: To compare the intensity of muscle contractions in irreversible electroporation (IRE) treatments when traditional IRE and high-frequency IRE (H-FIRE) waveforms are used in combination with a single applicator and distal grounding pad (A+GP) configuration.Materials and Methods: An ex vivo in situ porcine model was used to compare muscle contractions induced by traditional monopolar IRE waveforms vs high-frequency bipolar IRE waveforms. Pulses with voltages between 200 and 5,000 V were investigated, and muscle contractions were recorded by using accelerometers placed on or near the applicators.Results: H-FIRE waveforms reduced the intensity of muscle contractions in comparison with traditional monopolar IRE pulses. A high-energy burst of 2-μs alternating-polarity pulses energized for 200 μs at 4,500 V produced less intense muscle contractions than traditional IRE pulses, which were 25-100 μs in duration at 3,000 V.Conclusions: H-FIRE appears to be an effective technique to mitigate the muscle contractions associated with traditional IRE pulses. This may enable the use of voltages greater than 3,000 V necessary for the creation of large ablations in vivo. [ABSTRACT FROM AUTHOR]

The article presents a study on the effects of a magnetic resonance imaging (MRI)-ultrasound fusion technique on prostate cancer detection in men with prior negative biopsies but with an increased prostate specific antigen (PSA). It compares targeted and systematic biopsies in relation to detecting cancer in the patients. The efficacy of the MRI-US fusion targeted biopsy is also discussed.

The authors discusses the use of partial nephrectomy in the health care system of the U.S. Department of Veterans Affairs (VA). Guidelines issued by the American Urological Association describe nephron sparing surgery (NSS) as the preferred treatment modality for renal cell carcinoma (RCC). An overview of the Veteran's Affairs Central Cancer Registry (VACCR) data repository is provided. The authors conclude that current partial nephrectomy rates in the VA health care system are comparable to those in the SEER-Medicare tumor registry studies.

Purpose: To mathematically model and test ex vivo a modified technique of irreversible electroporation (IRE) to produce large spherical ablations by using a single probe.Materials and Methods: Computed simulations were performed by using varying voltages, electrode exposure lengths, and tissue types. A vegetable (potato) tissue model was then used to compare ablations created by conventional and high-frequency IRE protocols by using 2 probe configurations: a single probe with two collinear electrodes (2EP) or a single electrode configured with a grounding pad (P+GP). The new P+GP electrode configuration was evaluated in ex vivo liver tissue.Results: The P+GP configuration produced more spherical ablation volumes than the 2EP configuration in computed simulations and tissue models. In prostate tissue, computed simulations predicted ablation volumes at 3,000 V of 1.6 cm(3) for the P+GP configurations, compared with 0.94 cm(3) for the 2EP configuration; in liver tissue, the predicted ablation volumes were 4.7 times larger than those in the prostate. Vegetable model studies verify that the P+GP configuration produces larger and more spherical ablations than those produced by the 2EP. High-frequency IRE treatment of ex vivo liver with the P+GP configuration created a 2.84 × 2.21-cm ablation zone.Conclusions: Computer modeling showed that P+GP configuration for IRE procedures yields ablations that are larger than the 2EP configuration, creating substantial ablation zones with a single electrode placement. When tested in tissue models and an ex vivo liver model, the P+GP configuration created ablation zones that appear to be of clinically relevant size and shape. [ABSTRACT FROM AUTHOR]

PURPOSE: Accurately distinguishing renal cell carcinoma (RCC) from normal kidney tissue is critical for identifying positive surgical margins (PSMs) during partial and radical nephrectomy, which remains the primary intervention for localized RCC. Techniques that detect PSM with higher accuracy and faster turnaround time than intraoperative frozen section (IFS) analysis can help decrease reoperation rates, relieve patient anxiety and costs, and potentially improve patient outcomes. MATERIALS AND METHODS: Here, we extended our combined desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and machine learning methodology to identify metabolite and lipid species from tissue surfaces that can distinguish normal tissues from clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) tissues. RESULTS: From 24 normal and 40 renal cancer (23 ccRCC, 13 pRCC, and 4 chRCC) tissues, we developed a multinomial lasso classifier that selects 281 total analytes from over 27,000 detected molecular species that distinguishes all histological subtypes of RCC from normal kidney tissues with 84.5% accuracy. On the basis of independent test data reflecting distinct patient populations, the classifier achieves 85.4% and 91.2% accuracy on a Stanford test set (20 normal and 28 RCC) and a Baylor-UT Austin test set (16 normal and 41 RCC), respectively. The majority of the model's selected features show consistent trends across data sets affirming its stable performance, where the suppression of arachidonic acid metabolism is identified as a shared molecular feature of ccRCC and pRCC. CONCLUSION: Together, these results indicate that signatures derived from DESI-MSI combined with machine learning may be used to rapidly determine surgical margin status with accuracies that meet or exceed those reported for IFS. Combining mass spectrometry imaging and machine learning enables successful diagnosis of renal cell carcinoma from normal kidney tissues. [ABSTRACT FROM AUTHOR]

• When considering high intensity focused ultrasound (HIFU) ablation therapies, patients marginally prioritized urinary incontinence (UI) over prostate biopsy outcomes, and erectile dysfunction (ED) was the least important outcome. • Patients are willing to accept increased risk of ED or UI to obtain an increased chance of favorable biopsy outcome. • Results from this study may help inform development and evaluation of future HIFU ablation therapies. Food and Drug Administration must make decisions about emerging high intensity focused ultrasound (HIFU) devices that may lack relevant clinical oncologic data but present with known side effects. This study aims to capture patients' perspective by quantifying their preferences regarding the available benefit and important side effects associated with HIFU for localized prostate cancer. Preferences for HIFU outcomes were examined using a discrete choice experiment survey. Participants were asked to choose a preferred treatment option in 9 choice questions. Each included a pair of hypothetical treatment profiles that have similar attributes/outcomes with varying levels. Outcomes included prostate biopsy outcome and treatment-related risks of erectile dysfunction (ED) and urinary incontinence (UI). We calculated the maximum risk of side effect patients were willing to tolerate in exchange for increased benefit. Preferences were further explored via clinical and demographic data. About 223 subjects with a mean age of 64.8 years completed the survey. Respondents were willing to accept a 1.51%-point increase in new ED risk for a 1%-point increase in favorable biopsy outcome. They were also willing to accept a 0.93%-point increase in new UI risk for a 1%-point increase in biopsy outcome. Subjects who perceived their cancer to be more aggressive had higher risk tolerance for UI. Younger men were willing to tolerate less ED risk than older men. Respondents with greater than college level of education had a lower risk tolerance for ED or UI. Results may inform development and regulatory evaluation for future HIFU ablation devices by providing supplemental information from the patient perspective. Emerging high intensity focused ultrasound devices may lack relevant clinical oncologic data but present with known side effects. A discrete choice experiment showed that patients with localized prostate cancer were willing to accept increased treatment-related risk of erectile dysfunction or urinary incontinence to obtain increased chance of favorable biopsy outcome. Results may inform development and evaluation of future HIFU therapies. [ABSTRACT FROM AUTHOR]

Background: Most Prostate Imaging-Reporting and Data System (PI-RADS) 3 lesions do not contain clinically significant prostate cancer (CSPCa; grade group ≥2). This study was aimed at identifying clinical and magnetic resonance imaging (MRI)-derived risk fac- tors that predict CSPCa in men with PI-RADS 3 lesions.Methods: This study analyzed the detection of CSPCa in men who underwent MRI-targeted biopsy for PI-RADS 3 lesions. Multivariable logistic regression models with goodness-of-fit testing were used to identify variables associated with CSPCa. Receiver operating curves and decision curve analyses were used to estimate the clinical utility of a predictive model.Results: Of the 1784 men reviewed, 1537 were included in the training cohort, and 247 were included in the validation cohort. The 309 men with CSPCa (17.3%) were older, had a higher prostate-specific antigen (PSA) density, and had a greater likelihood of an anteriorly located lesion than men without CSPCa (p < .01). Multivariable analysis revealed that PSA density (odds ratio [OR], 1.36; 95% confidence interval [CI], 1.05-1.85; p < .01), age (OR, 1.05; 95% CI, 1.02-1.07; p < .01), and a biopsy-naive status (OR, 1.83; 95% CI, 1.38-2.44) were independently associated with CSPCa. A prior negative biopsy was negatively associated (OR, 0.35; 95% CI, 0.24-0.50; p < .01). The application of the model to the validation cohort resulted in an area under the curve of 0.78. A predicted risk threshold of 12% could have prevented 25% of biopsies while detecting almost 95% of CSPCas with a sensitivity of 94% and a specificity of 34%.Conclusions: For PI-RADS 3 lesions, an elevated PSA density, older age, and a biopsy-naive status were associated with CSPCa, whereas a prior negative biopsy was negatively associated. A predictive model could prevent PI-RADS 3 biopsies while missing few CSPCas.Lay Summary: Among men with an equivocal lesion (Prostate Imaging-Reporting and Data System 3) on multiparametric magnetic resonance imaging (mpMRI), those who are older, those who have a higher prostate-specific antigen density, and those who have never had a biopsy before are at higher risk for having clinically significant prostate cancer (CSPCa) on subsequent biopsy. However, men with at least one negative biopsy have a lower risk of CSPCa. A new predictive model can greatly reduce the need to biopsy equivocal lesions noted on mpMRI while missing only a few cases of CSPCa. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer remains the second deadliest cancer for American men despite clinical advancements. Currently, magnetic resonance imaging (MRI) is considered the most sensitive non‐invasive imaging modality that enables visualization, detection, and localization of prostate cancer, and is increasingly used to guide targeted biopsies for prostate cancer diagnosis. However, its utility remains limited due to high rates of false positives and false negatives as well as low inter‐reader agreements. Purpose: Machine learning methods to detect and localize cancer on prostate MRI can help standardize radiologist interpretations. However, existing machine learning methods vary not only in model architecture, but also in the ground truth labeling strategies used for model training. We compare different labeling strategies and the effects they have on the performance of different machine learning models for prostate cancer detection on MRI. Methods: Four different deep learning models (SPCNet, U‐Net, branched U‐Net, and DeepLabv3+) were trained to detect prostate cancer on MRI using 75 patients with radical prostatectomy, and evaluated using 40 patients with radical prostatectomy and 275 patients with targeted biopsy. Each deep learning model was trained with four different label types: pathology‐confirmed radiologist labels, pathologist labels on whole‐mount histopathology images, and lesion‐level and pixel‐level digital pathologist labels (previously validated deep learning algorithm on histopathology images to predict pixel‐level Gleason patterns) on whole‐mount histopathology images. The pathologist and digital pathologist labels (collectively referred to as pathology labels) were mapped onto pre‐operative MRI using an automated MRI‐histopathology registration platform. Results: Radiologist labels missed cancers (ROC‐AUC: 0.75‐0.84), had lower lesion volumes (~68% of pathology lesions), and lower Dice overlaps (0.24‐0.28) when compared with pathology labels. Consequently, machine learning models trained with radiologist labels also showed inferior performance compared to models trained with pathology labels. Digital pathologist labels showed high concordance with pathologist labels of cancer (lesion ROC‐AUC: 0.97‐1, lesion Dice: 0.75‐0.93). Machine learning models trained with digital pathologist labels had the highest lesion detection rates in the radical prostatectomy cohort (aggressive lesion ROC‐AUC: 0.91‐0.94), and had generalizable and comparable performance to pathologist label‐trained‐models in the targeted biopsy cohort (aggressive lesion ROC‐AUC: 0.87‐0.88), irrespective of the deep learning architecture. Moreover, machine learning models trained with pixel‐level digital pathologist labels were able to selectively identify aggressive and indolent cancer components in mixed lesions on MRI, which is not possible with any human‐annotated label type. Conclusions: Machine learning models for prostate MRI interpretation that are trained with digital pathologist labels showed higher or comparable performance with pathologist label‐trained models in both radical prostatectomy and targeted biopsy cohort. Digital pathologist labels can reduce challenges associated with human annotations, including labor, time, inter‐ and intra‐reader variability, and can help bridge the gap between prostate radiology and pathology by enabling the training of reliable machine learning models to detect and localize prostate cancer on MRI. [ABSTRACT FROM AUTHOR]

Background: Men with grade group 2 or 3 prostate cancer are often considered ineligible for active surveillance; some patients with grade group 2 prostate cancer who are managed with active surveillance will have early disease progression requiring radical therapy. This study aimed to investigate whether MRI-guided focused ultrasound focal therapy can safely reduce treatment burden for patients with localised grade group 2 or 3 intermediate-risk prostate cancer.Methods: In this single-arm, multicentre, phase 2b study conducted at eight health-care centres in the USA, we recruited men aged 50 years and older with unilateral, MRI-visible, primary, intermediate-risk, previously untreated prostate adenocarcinoma (prostate-specific antigen ≤20 ng/mL, grade group 2 or 3; tumour classification ≤T2) confirmed on combined biopsy (combining MRI-targeted and systematic biopsies). MRI-guided focused ultrasound energy, sequentially titrated to temperatures sufficient for tissue ablation (about 60-70°C), was delivered to the index lesion and a planned margin of 5 mm or more of normal tissue, using real-time magnetic resonance thermometry for intraoperative monitoring. Co-primary outcomes were oncological outcomes (absence of grade group 2 and higher cancer in the treated area at 6-month and 24-month combined biopsy; when 24-month biopsy data were not available and grade group 2 or higher cancer had occurred in the treated area at 6 months, the 6-month biopsy results were included in the final analysis) and safety (adverse events up to 24 months) in all patients enrolled in the study. This study is registered with ClinicalTrials.gov, NCT01657942, and is no longer recruiting.Findings: Between May 4, 2017, and Dec 21, 2018, we assessed 194 patients for eligibility and treated 101 patients with MRI-guided focused ultrasound. Median age was 63 years (IQR 58-67) and median concentration of prostate-specific antigen was 5·7 ng/mL (IQR 4·2-7·5). Most cancers were grade group 2 (79 [78%] of 101). At 24 months, 78 (88% [95% CI 79-94]) of 89 men had no evidence of grade group 2 or higher prostate cancer in the treated area. No grade 4 or grade 5 treatment-related adverse events were reported, and only one grade 3 adverse event (urinary tract infection) was reported. There were no treatment-related deaths.Interpretation: 24-month biopsy outcomes show that MRI-guided focused ultrasound focal therapy is safe and effectively treats grade group 2 or 3 prostate cancer. These results support focal therapy for select patients and its use in comparative trials to determine if a tissue-preserving approach is effective in delaying or eliminating the need for radical whole-gland treatment in the long term.Funding: Insightec and the National Cancer Institute. [ABSTRACT FROM AUTHOR]

Simple Summary: In approximately 50% of prostate cancer patients undergoing surgical treatment, cancer has extended beyond the prostate boundary (i.e., extraprostatic extension). The aim of our study was to expand artificial intelligence (AI) models that identify cancer in the prostate to also identify the cancer that spreads outside the boundary of the prostate. By combining past models with image post-processing steps and clinical decision rules, we built an autonomous approach to detect the extension of the cancer beyond the prostate boundary using prostate MRI. Our study included 123 prostate cancer patients (38 with extraprostatic extension), and our proposed method can detect cancer outside the prostate boundary in more cases than radiologists. The localization of extraprostatic extension (EPE), i.e., local spread of prostate cancer beyond the prostate capsular boundary, is important for risk stratification and surgical planning. However, the sensitivity of EPE detection by radiologists on MRI is low (57% on average). In this paper, we propose a method for computational detection of EPE on multiparametric MRI using deep learning. Ground truth labels of cancers and EPE were obtained in 123 patients (38 with EPE) by registering pre-surgical MRI with whole-mount digital histopathology images from radical prostatectomy. Our approach has two stages. First, we trained deep learning models using the MRI as input to generate cancer probability maps both inside and outside the prostate. Second, we built an image post-processing pipeline that generates predictions for EPE location based on the cancer probability maps and clinical knowledge. We used five-fold cross-validation to train our approach using data from 74 patients and tested it using data from an independent set of 49 patients. We compared two deep learning models for cancer detection: (i) UNet and (ii) the Correlated Signature Network for Indolent and Aggressive prostate cancer detection (CorrSigNIA). The best end-to-end model for EPE detection, which we call EPENet, was based on the CorrSigNIA cancer detection model. EPENet was successful at detecting cancers with extraprostatic extension, achieving a mean area under the receiver operator characteristic curve of 0.72 at the patient-level. On the test set, EPENet had 80.0% sensitivity and 28.2% specificity at the patient-level compared to 50.0% sensitivity and 76.9% specificity for the radiologists. To account for spatial location of predictions during evaluation, we also computed results at the sextant-level, where the prostate was divided into sextants according to standard systematic 12-core biopsy procedure. At the sextant-level, EPENet achieved mean sensitivity 61.1% and mean specificity 58.3%. Our approach has the potential to provide the location of extraprostatic extension using MRI alone, thus serving as an independent diagnostic aid to radiologists and facilitating treatment planning. [ABSTRACT FROM AUTHOR]

Purpose: To assess whether simultaneous hyperpolarized C-13 magnetic resonance spectroscopy (MRS)/positron emission tomography (PET)/multiparametric magnetic resonance (mpMR) imaging is feasible in an orthotopic canine prostate cancer (PCa) model using a clinical PET/MR system and whether the combined imaging datasets can be fused with transrectal ultrasound (TRUS) in real time for multimodal image fusion-guided targeted biopsy of PCa.Procedures: Institutional Animal Care and Use Committee approval was obtained for this study. Canine prostate adenocarcinoma (Ace-1) cells were orthotopically injected into the prostate of four dogs. Once tumor engraftment was confirmed by TRUS, simultaneous hyperpolarized C-13 MRS of [1-13C]pyruvate, PET (2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG), [68Ga]NODAGA-SCH1), and mpMR (T2W, DWI) imaging was performed using a clinical PET/MR system. Multimodality imaging data sets were then fused with TRUS and image-guided targeted biopsy was performed. Imaging results were then correlated with histological findings.Results: Successful tumor engraftment was histologically confirmed in three of the four dogs (dogs 2, 3, and 4) and simultaneous C-13 MRS/PET/mpMR was feasible in all three. In dog 2, C-13 MRS showed increased lactate signal in the tumor (lactate/totalC = 0.47) whereas mpMR did not show any signal changes. In dog 3, [18F]FDG-PET (SUVmean = 1.90) and C-13 MRS (lactate/totalC = 0.59) showed elevated metabolic activity in the tumor. In dog 4, [18F]FDG (SUVmean = 2.43), [68Ga]NODAGA-SCH1 (SUVmean = 0.75), and C-13 MRS (Lac/totalC = 0.53) showed elevated uptake in tumor compared to control tissue and multimodal image fusion-guided biopsy of the tumor was successfully performed.Conclusion: Simultaneous C-13 MRS/PET/mpMR imaging and multimodal image fusion-guided biopsy is feasible in a canine PCa model. [ABSTRACT FROM AUTHOR]

The question of whether ultrasound point shear wave elastography can differentiate renal cell carcinoma (RCC) from angiomyolipoma (AML) is controversial. This study prospectively enrolled 51 patients with 52 renal tumors (42 RCCs, 10 AMLs). We obtained 10 measurements of shear wave velocity (SWV) in the renal tumor, cortex and medulla. Median SWV was first used to classify RCC versus AML. Next, the prediction accuracy of 4 machine learning algorithms-logistic regression, naïve Bayes, quadratic discriminant analysis and support vector machines (SVMs)-was evaluated, using statistical inputs from the tumor, cortex and combined statistical inputs from tumor, cortex and medulla. After leave-one-out cross validation, models were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). Tumor median SWV performed poorly (AUC = 0.62; p = 0.23). Except logistic regression, all machine learning algorithms reached statistical significance using combined statistical inputs (AUC = 0.78-0.98; p < 7.1 × 10-3). SVMs demonstrated 94% accuracy (AUC = 0.98; p = 3.13 × 10-6) and clearly outperformed median SWV in differentiating RCC from AML (p = 2.8 × 10-4). [ABSTRACT FROM AUTHOR]

Accurate identification of prostate cancer in frozen sections at the time of surgery can be challenging, limiting the surgeon’s ability to best determine resection margins during prostatectomy. We performed desorption electrospray ionization mass spectrometry imaging (DESI-MSI) on 54 banked human cancerous and normal prostate tissue specimens to investigate the spatial distribution of a wide variety of small metabolites, carbohydrates, and lipids. In contrast to several previous studies, our method included Krebs cycle intermediates (m/z <200), which we found to be highly informative in distinguishing cancer from benign tissue. Malignant prostate cells showed marked metabolic derangements compared with their benign counterparts. Using the “Least absolute shrinkage and selection operator” (Lasso), we analyzed all metabolites from the DESI-MS data and identified parsimonious sets of metabolic profiles for distinguishing between cancer and normal tissue. In an independent set of samples, we could use these models to classify prostate cancer from benign specimens with nearly 90% accuracy per patient. Based on previous work in prostate cancer showing that glucose levels are high while citrate is low, we found that measurement of the glucose/citrate ion signal ratio accurately predicted cancer when this ratio exceeds 1.0 and normal prostate when the ratio is less than 0.5. After brief tissue preparation, the glucose/citrate ratio can be recorded on a tissue sample in 1 min or less, which is in sharp contrast to the 20 min or more required by histopathological examination of frozen tissue specimens. [ABSTRACT FROM AUTHOR]

The authors comment on a collaborative consensus statement about prostate magnetic resonance imaging (MRI) and MRI-targeted biopsy in patients with negative prostate biopsy. Topics discussed include the American Urological Association (AUA) guideline for biopsy-naive patients, detection of clinically significant cancer at repeat biopsy using MRI targeting and deferral of repeat biopsy based on a negative MRI.

Prostate biopsy and image-guided treatment procedures are often performed under the guidance of ultrasound fused with magnetic resonance images (MRI). Accurate image fusion relies on accurate segmentation of the prostate on ultrasound images. Yet, the reduced signal-to-noise ratio and artifacts (e.g., speckle and shadowing) in ultrasound images limit the performance of automated prostate segmentation techniques and generalizing these methods to new image domains is inherently difficult. In this study, we address these challenges by introducing a novel 2.5D deep neural network for prostate segmentation on ultrasound images. Our approach addresses the limitations of transfer learning and finetuning methods (i.e., drop in performance on the original training data when the model weights are updated) by combining a supervised domain adaptation technique and a knowledge distillation loss. The knowledge distillation loss allows the preservation of previously learned knowledge and reduces the performance drop after model finetuning on new datasets. Furthermore, our approach relies on an attention module that considers model feature positioning information to improve the segmentation accuracy. We trained our model on 764 subjects from one institution and finetuned our model using only ten subjects from subsequent institutions. We analyzed the performance of our method on three large datasets encompassing 2067 subjects from three different institutions. Our method achieved an average Dice Similarity Coefficient (Dice) of 94. 0 ± 0. 03 and Hausdorff Distance (HD95) of 2.28 mm in an independent set of subjects from the first institution. Moreover, our model generalized well in the studies from the other two institutions (Dice: 91. 0 ± 0. 03 ; HD95: 3.7 mm and Dice: 82. 0 ± 0. 03 ; HD95: 7.1 mm). We introduced an approach that successfully segmented the prostate on ultrasound images in a multi-center study, suggesting its clinical potential to facilitate the accurate fusion of ultrasound and MRI images to drive biopsy and image-guided treatments. [Display omitted] • We introduce a deep learning framework for accurate prostate gland segmentation in TRUS images, with the presence of considerable variation in intensity and image acquisition parameters. We improve the generalization capabilities of our model across data from three institutions. • To limit the effect of catastrophic forgetting during transfer learning, we adapted a training scheme that utilizes knowledge distillation loss during the finetuning process on new data. • Extensive experiments on multi-center data with different ultrasound probes demonstrate the proposed approach brings substantial gains over existing approaches. [ABSTRACT FROM AUTHOR]

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/mL2, 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. [ABSTRACT FROM AUTHOR]

• Introduce task amenability for quantifying task-specific image quality assessment • Propose meta-reinforcement learning algorithms for learning label-efficient, adaptable task amenability • Select task amenable data for improving performance in three tasks from two clinical applications [Display omitted] In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability. When the task is performed using machine learning algorithms, such as a neural-network-based task predictor for image classification or segmentation, the performance of the task predictor provides an objective estimate of task amenability. In this work, we use an IQA controller to predict the task amenability which, itself being parameterised by neural networks, can be trained simultaneously with the task predictor. We further develop a meta-reinforcement learning framework to improve the adaptability for both IQA controllers and task predictors, such that they can be fine-tuned efficiently on new datasets or meta-tasks. We demonstrate the efficacy of the proposed task-specific, adaptable IQA approach, using two clinical applications for ultrasound-guided prostate intervention and pneumonia detection on X-ray images. [ABSTRACT FROM AUTHOR]

Background: Clinical guidelines favor MRI before prostate biopsy due to proven benefits. However, adoption patterns across the US are unclear., Methods: This study used the Merative™ Marketscan® Commercial & Medicare Databases to analyze 872,829 prostate biopsies in 726,663 men from 2007-2022. Pre-biopsy pelvic MRI within 90 days was the primary outcome. Descriptive statistics and generalized estimating equations assessed changes over time, urban-rural differences, and state-level variation., Results: Pre-biopsy MRI utilization increased significantly from 0.5% in 2007 to 35.5% in 2022, with faster adoption in urban areas (36.1% in 2022) versus rural areas (28.3% in 2022). Geographic disparities were notable, with higher utilization in California, New York, and Minnesota, and lower rates in the Southeast and Mountain West., Conclusions: The study reveals a paradigm shift in prostate cancer diagnostics towards MRI-guided approaches, influenced by evolving guidelines and clinical evidence. Disparities in access, particularly in rural areas and specific regions, highlight the need for targeted interventions to ensure equitable access to advanced diagnostic techniques., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Purpose: Asian, Black, and Hispanic men are underrepresented in prostate cancer (PCa) clinical trials. Few novel prostate cancer biomarkers have been validated in diverse cohorts. We aimed to determine if Stockholm3 can improve prostate cancer detection in a diverse cohort., Methods: An observational prospective multicentered (17 sites) clinical trial (2019-2023), supplemented by prospectively recruited participants (2008-2020) in a urology clinic setting included men with suspicion of PCa and underwent prostate biopsy. Before biopsy, sample was collected for measurement of the Stockholm3 risk score. Parameters include prostate-specific antigen (PSA), free PSA, KLK2, GDF15, PSP94, germline risk (single-nucleotide polymorphisms), age, family history, and previous negative biopsy. The primary endpoint was detection of International Society of Urological Pathology (ISUP) Grade ≥2 cancer (clinically significant PCa, csPC). The two primary aims were to (1) demonstrate noninferior sensitivity (0.8 lower bound 95% CI noninferiority margin) in detecting csPC using Stockholm3 compared with PSA (relative sensitivity) and (2) demonstrate superior specificity by reducing biopsies with benign results or low-grade cancers (relative specificity)., Results: A total of 2,129 biopsied participants were included: Asian (16%, 350), Black or African American (Black; 24%, 505), Hispanic or Latino and White (Hispanic; 14%, 305), and non-Hispanic or non-Latino and White (White; 46%, 969). Overall, Stockholm3 showed noninferior sensitivity compared with PSA ≥4 ng/mL (relative sensitivity: 0.95 [95% CI, 0.92 to 0.99]) and nearly three times higher specificity (relative specificity: 2.91 [95% CI, 2.63 to 3.22]). Results were consistent across racial and ethnic subgroups: noninferior sensitivity (0.91-0.98) and superior specificity (2.51-4.70). Compared with PSA, Stockholm3 could reduce benign and ISUP 1 biopsies by 45% overall and between 42% and 52% across racial and ethnic subgroups., Conclusion: In a substantially diverse population, Stockholm3 significantly reduces unnecessary prostate biopsies while maintaining a similar sensitivity to PSA in detecting csPC.

Background and Objective: Micro-ultrasound (MUS) uses a high-frequency transducer with superior resolution to conventional ultrasound, which may differentiate prostate cancer from normal tissue and thereby allow targeted biopsy. Preliminary evidence has shown comparable sensitivity to magnetic resonance imaging (MRI), but consistency between users has yet to be described. Our objective was to assess agreement of MUS interpretation across multiple readers., Methods: After institutional review board approval, we prospectively collected MUS images for 57 patients referred for prostate biopsy after multiparametric MRI from 2022 to 2023. MUS images were interpreted by six urologists at four institutions with varying experience (range 2-6 yr). Readers were blinded to MRI results and clinical data. The primary outcome was reader agreement on the locations of suspicious lesions, measured in terms of Light's κ and positive percent agreement (PPA). Reader sensitivity for identification of grade group (GG) ≥2 prostate cancer was a secondary outcome., Key Findings and Limitations: Analysis revealed a κ value of 0.30 (95% confidence interval [CI] 0.21-0.39). PPA was 33% (95% CI 25-42%). The mean patient-level sensitivity for GG ≥2 cancer was 0.66 ± 0.05 overall and 0.87 ± 0.09 when cases with anterior lesions were excluded. Readers were 12 times more likely to detect higher-grade cancers (GG ≥3), with higher levels of agreement for this subgroup (κ 0.41, PPA 45%). Key limitations include the inability to prospectively biopsy reader-delineated targets and the inability of readers to perform live transducer maneuvers., Conclusions and Clinical Implications: Inter-reader agreement on the location of suspicious lesions on MUS is lower than rates previously reported for MRI. MUS sensitivity for cancer in the anterior gland is lacking., Patient Summary: The ability to find cancer on imaging scans can vary between doctors. We found that there was frequent disagreement on the location of prostate cancer when doctors were using a new high-resolution scan method called micro-ultrasound. This suggests that the performance of micro-ultrasound is not yet consistent enough to replace MRI (magnetic resonance imaging) for diagnosis of prostate cancer., (© 2024 The Author(s).)

Image registration can map the ground truth extent of prostate cancer from histopathology images onto MRI, facilitating the development of machine learning methods for early prostate cancer detection. Here, we present RAdiology PatHology Image Alignment (RAPHIA), an end-to-end pipeline for efficient and accurate registration of MRI and histopathology images. RAPHIA automates several time-consuming manual steps in existing approaches including prostate segmentation, estimation of the rotation angle and horizontal flipping in histopathology images, and estimation of MRI-histopathology slice correspondences. By utilizing deep learning registration networks, RAPHIA substantially reduces computational time. Furthermore, RAPHIA obviates the need for a multimodal image similarity metric by transferring histopathology image representations to MRI image representations and vice versa. With the assistance of RAPHIA, novice users achieved expert-level performance, and their mean error in estimating histopathology rotation angle was reduced by 51% (12 degrees vs 8 degrees), their mean accuracy of estimating histopathology flipping was increased by 5% (95.3% vs 100%), and their mean error in estimating MRI-histopathology slice correspondences was reduced by 45% (1.12 slices vs 0.62 slices). When compared to a recent conventional registration approach and a deep learning registration approach, RAPHIA achieved better mapping of histopathology cancer labels, with an improved mean Dice coefficient of cancer regions outlined on MRI and the deformed histopathology (0.44 vs 0.48 vs 0.50), and a reduced mean per-case processing time (51 vs 11 vs 4.5 min). The improved performance by RAPHIA allows efficient processing of large datasets for the development of machine learning models for prostate cancer detection on MRI. Our code is publicly available at: https://github.com/pimed/RAPHIA., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mirabela Rusu is a paid consultant for Roche, the conflict is unrelated to this research. Also, Mirabela Rusu has research grants from Phillips Healthcare. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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).)

Targeting of lesions seen on multiparametric MRI (mpMRI) improves prostate cancer (PC) detection at biopsy. However, 20%-65% of highly suspicious lesions on mpMRI (PI-RADS [Prostate Imaging-Reporting and Data System] 4 or 5) are false-positives (FPs), while 5%-10% of clinically significant PC (csPC) are missed. Prostate-specific membrane antigen (PSMA) and gastrin-releasing peptide receptors (GRPRs) are both overexpressed in PC. We therefore aimed to evaluate the potential of 68 Ga-PSMA11 and 68 Ga-RM2 PET/MRI for biopsy guidance in patients with suspected PC. Methods: A highly selective cohort of 13 men, aged 58.0 ± 7.1 y, with suspected PC (persistently high prostate-specific antigen [PSA] and PSA density) but negative or equivocal mpMRI results or negative biopsy were prospectively enrolled to undergo 68 Ga-PSMA11 and 68 Ga-RM2 PET/MRI. PET/MRI included whole-body and dedicated pelvic imaging after a delay of 20 min. All patients had targeted biopsy of any lesions seen on PET followed by standard 12-core biopsy. The SUV max of suspected PC lesions was collected and compared with gold standard biopsy. Results: PSA and PSA density at enrollment were 9.8 ± 6.0 (range, 1.5-25.5) ng/mL and 0.20 ± 0.18 (range, 0.06-0.68) ng/mL 2 , respectively. Standardized systematic biopsy revealed a total of 14 PCs in 8 participants: 7 were csPC and 7 were nonclinically significant PC (ncsPC). 68 Ga-PSMA11 identified 25 lesions, of which 11 (44%) were true-positive (TP) (5 csPC). 68 Ga-RM2 showed 27 lesions, of which 14 (52%) were TP, identifying all 7 csPC and also 7 ncsPC. There were 17 concordant lesions in 11 patients versus 14 discordant lesions in 7 patients between 68 Ga-PSMA11 and 68 Ga-RM2 PET. Incongruent lesions had the highest rate of FP (12 FP vs. 2 TP). SUV max was significantly higher for TP than FP lesions in delayed pelvic imaging for 68 Ga-PSMA11 (6.49 ± 4.14 vs. 4.05 ± 1.55, P = 0.023) but not for whole-body images, nor for 68 Ga-RM2. Conclusion: Our results show that 68 Ga-PSMA11 and 68 Ga-RM2 PET/MRI are feasible for biopsy guidance in suspected PC. Both radiopharmaceuticals detected additional clinically significant cancers not seen on mpMRI in this selective cohort. 68 Ga-RM2 PET/MRI identified all csPC confirmed at biopsy., (© 2023 by the Society of Nuclear Medicine and Molecular Imaging.)