The Role of Multiparametric MRI (mpMRI) in the Prediction of Adverse Prostate Cancer Pathology in Radical Prostatectomy Specimen.

INTRODUCTION: Prostate cancer (PCa) risk stratification is essential in guiding therapeutic decision. Multiparametric magnetic resonance tomography (mpMRI) holds promise in the prediction of adverse pathologies (AP) after prostatectomy (RP). This study aims to identify clinical and imaging markers in the prediction of adverse pathology. METHODS: Patients with PCa, diagnosed by targeted biopsy after mpMRI and undergoing RP, were included. The predictive accuracy of mpMRI for extraprostatic extension (ECE), seminal vesicle infiltration (SVI), and lymph node positivity was calculated from the final histopathology. RESULTS: 846 patients were involved. Independent risk parameters include imaging findings such as ECE (OR 3.12), SVI (OR 2.55), and PI-RADS scoring (4: OR 2.01 and 5: OR 4.34). mpMRI parameters such as ECE, SVI, and lymph node metastases showed a high prognostic accuracy (73.28% vs. 95.35% vs. 93.38%) with moderate sensitivity compared to the final histopathology. The ROC analysis of our combined scoring system (D'Amico classification, PSA density, and MRI risk factors) improves the prediction of adverse pathology (AUC: 0.73 vs. 0.69). CONCLUSION: Our study supports the use of mpMRI for comprehensive pretreatment risk assessment in PCa. Due to the high accuracy of factors like ECE, SVI, and PI-RADS scoring, utilizing mpMRI data enabled accurate prediction of unfavorable pathology after RP.

New advances of the androgen receptor in prostate cancer: report from the 1st International Androgen Receptor Symposium.

The androgen receptor (AR) is a crucial player in various aspects of male reproduction and has been associated with the development and progression of prostate cancer (PCa). Therefore, the protein is the linchpin of current PCa therapies. Despite great research efforts, the AR signaling pathway has still not been deciphered, and the emergence of resistance is still the biggest problem in PCa treatment. To discuss the latest developments in AR research, the "1st International Androgen Receptor Symposium" offered a forum for the exchange of clinical and scientific innovations around the role of the AR in prostate cancer (PCa) and to stimulate new collaborative interactions among leading scientists from basic, translational, and clinical research. The symposium included three sessions covering preclinical studies, prognostic and diagnostic biomarkers, and ongoing prostate cancer clinical trials. In addition, a panel discussion about the future direction of androgen deprivation therapy and anti-AR therapy in PCa was conducted. Therefore, the newest insights and developments in therapeutic strategies and biomarkers are discussed in this report.

HoloLens augmented reality system for transperineal free-hand prostate procedures.

Purpose: An augmented reality (AR) system was developed to facilitate free-hand real-time needle guidance for transperineal prostate (TP) procedures and to overcome the limitations of a traditional guidance grid. Approach: The HoloLens AR system enables the superimposition of annotated anatomy derived from preprocedural volumetric images onto a patient and addresses the most challenging part of free-hand TP procedures by providing real-time needle tip localization and needle depth visualization during insertion. The AR system accuracy, or the image overlay accuracy ( n = 56 ), and needle targeting accuracy ( n = 24 ) were evaluated within a 3D-printed phantom. Three operators each used a planned-path guidance method ( n = 4 ) and free-hand guidance ( n = 4 ) to guide needles into targets in a gel phantom. Placement error was recorded. The feasibility of the system was further evaluated by delivering soft tissue markers into tumors of an anthropomorphic pelvic phantom via the perineum. Results: The image overlay error was 1.29 ± 0.57 mm , and needle targeting error was 2.13 ± 0.52 mm . The planned-path guidance placements showed similar error compared to the free-hand guidance ( 4.14 ± 1.08 mm versus 4.20 ± 1.08 mm , p = 0.90 ). The markers were successfully implanted either into or in close proximity to the target lesion. Conclusions: The HoloLens AR system can provide accurate needle guidance for TP interventions. AR support for free-hand lesion targeting is feasible and may provide more flexibility than grid-based methods, due to the real-time 3D and immersive experience during free-hand TP procedures.

Deep learning-based artificial intelligence for prostate cancer detection at biparametric MRI.

PURPOSE: To present fully automated DL-based prostate cancer detection system for prostate MRI. METHODS: MRI scans from two institutions, were used for algorithm training, validation, testing. MRI-visible lesions were contoured by an experienced radiologist. All lesions were biopsied using MRI-TRUS-guidance. Lesions masks, histopathological results were used as ground truth labels to train UNet, AH-Net architectures for prostate cancer lesion detection, segmentation. Algorithm was trained to detect any prostate cancer ≥ ISUP1. Detection sensitivity, positive predictive values, mean number of false positive lesions per patient were used as performance metrics. RESULTS: 525 patients were included for training, validation, testing of the algorithm. Dataset was split into training (n = 368, 70%), validation (n = 79, 15%), test (n = 78, 15%) cohorts. Dice coefficients in training, validation sets were 0.403, 0.307, respectively, for AHNet model compared to 0.372, 0.287, respectively, for UNet model. In validation set, detection sensitivity was 70.9%, PPV was 35.5%, mean number of false positive lesions/patient was 1.41 (range 0-6) for UNet model compared to 74.4% detection sensitivity, 47.8% PPV, mean number of false positive lesions/patient was 0.87 (range 0-5) for AHNet model. In test set, detection sensitivity for UNet was 72.8% compared to 63.0% for AHNet, mean number of false positive lesions/patient was 1.90 (range 0-7), 1.40 (range 0-6) in UNet, AHNet models, respectively. CONCLUSION: We developed a DL-based AI approach which predicts prostate cancer lesions at biparametric MRI with reasonable performance metrics. While false positive lesion calls remain as a challenge of AI-assisted detection algorithms, this system can be utilized as an adjunct tool by radiologists.

Molecular biomarkers in the context of focal therapy for prostate cancer: recommendations of a Delphi Consensus from the Focal Therapy Society.

BACKGROUND: Focal therapy (FT) for prostate cancer (PCa) is promising. However, long-term oncological results are awaited and there is no consensus on follow-up strategies. Molecular biomarkers (MB) may be useful in selecting, treating and following up men undergoing FT, though there is limited evidence in this field to guide practice. We aimed to conduct a consensus meeting, endorsed by the Focal Therapy Society, amongst a large group of experts, to understand the potential utility of MB in FT for localized PCa. METHODS: A 38-item questionnaire was built following a literature search. The authors then performed three rounds of a Delphi Consensus using DelphiManager, using the GRADE grid scoring system, followed by a face-to-face expert meeting. Three areas of interest were identified and covered concerning MB for FT, 1) the current/present role; 2) the potential/future role; 3) the recommended features for future studies. Consensus was defined using a 70% agreement threshold. RESULTS: Of 95 invited experts, 42 (44.2%) completed the three Delphi rounds. Twenty-four items reached a consensus and they were then approved at the meeting involving (N.=15) experts. Fourteen items reached a consensus on uncertainty, or they did not reach a consensus. They were re-discussed, resulting in a consensus (N.=3), a consensus on a partial agreement (N.=1), and a consensus on uncertainty (N.=10). A final list of statements were derived from the approved and discussed items, with the addition of three generated statements, to provide guidance regarding MB in the context of FT for localized PCa. Research efforts in this field should be considered a priority. CONCLUSIONS: The present study detailed an initial consensus on the use of MB in FT for PCa. This is until evidence becomes available on the subject.

Using Prostate Imaging-Reporting and Data System (PI-RADS) Scores to Select an Optimal Prostate Biopsy Method: A Secondary Analysis of the Trio Study.

BACKGROUND: While magnetic resonance imaging (MRI)-targeted biopsy (TBx) results in better prostate cancer (PCa) detection relative to systematic biopsy (SBx), the combination of both methods increases clinically significant PCa detection relative to either Bx method alone. However, combined Bx subjects patients to higher number of Bx cores and greater detection of clinically insignificant PCa. OBJECTIVE: To determine if prebiopsy prostate MRI can identify men who could forgo combined Bx without a substantial risk of missing clinically significant PCa (csPC). DESIGN, SETTING, AND PARTICIPANTS: Men with MRI-visible prostate lesions underwent combined TBx plus SBx. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary outcomes were detection rates for grade group (GG) ≥2 and GG ≥3 PCa by TBx and SBx, stratified by Prostate Imaging-Reporting and Data System (PI-RADS) score. RESULTS AND LIMITATIONS: Among PI-RADS 5 cases, nearly all csPCs were detected by TBx, as adding SBx resulted in detection of only 2.5% more GG ≥2 cancers. Among PI-RADS 3-4 cases, however, SBx addition resulted in detection of substantially more csPCs than TBx alone (8% vs 7.5%). Conversely, TBx added little to detection of csPC among men with PI-RADS 2 lesions (2%) relative to SBx (7.8%). CONCLUSIONS: While combined Bx increases the detection of csPC among men with MRI-visible prostate lesions, this benefit was largely restricted to PI-RADS 3-4 lesions. Using a strategy of TBx only for PI-RADS 5 and combined Bx only for PI-RADS 3-4 would avoid excess biopsies for men with PI-RADS 5 lesions while resulting in a low risk of missing csPC (1%). PATIENT SUMMARY: Our study investigated an optimized strategy to diagnose aggressive prostate cancer in men with an abnormal prostate MRI (magnetic resonance imaging) scan while minimizing the risk of excess biopsies. We used a scoring system for MRI scan images called PI-RADS. The results show that MRI-targeted biopsies alone could be used for men with a PI-RADS score of 5, while men with a PI-RADS score of 3 or 4 would benefit from a combination of MRI-targeted biopsy and systematic biopsy. This trial is registered at ClinicalTrials.gov as NCT00102544.

A Cascaded Deep Learning-Based Artificial Intelligence Algorithm for Automated Lesion Detection and Classification on Biparametric Prostate Magnetic Resonance Imaging.

RATIONALE AND OBJECTIVES: Prostate MRI improves detection of clinically significant prostate cancer; however, its diagnostic performance has wide variation. Artificial intelligence (AI) has the potential to assist radiologists in the detection and classification of prostatic lesions. Herein, we aimed to develop and test a cascaded deep learning detection and classification system trained on biparametric prostate MRI using PI-RADS for assisting radiologists during prostate MRI read out. MATERIALS AND METHODS: T2-weighted, diffusion-weighted (ADC maps, high b value DWI) MRI scans obtained at 3 Tesla from two institutions (n = 1043 in-house and n = 347 Prostate-X, respectively) acquired between 2015 to 2019 were used for model training, validation, testing. All scans were retrospectively reevaluated by one radiologist. Suspicious lesions were contoured and assigned a PI-RADS category. A 3D U-Net-based deep neural network was used to train an algorithm for automated detection and segmentation of prostate MRI lesions. Two 3D residual neural network were used for a 4-class classification task to predict PI-RADS categories 2 to 5 and BPH. Training and validation used 89% (n = 1290 scans) of the data using 5 fold cross-validation, the remaining 11% (n = 150 scans) were used for independent testing. Algorithm performance at lesion level was assessed using sensitivities, positive predictive values (PPV), false discovery rates (FDR), classification accuracy, Dice similarity coefficient (DSC). Additional analysis was conducted to compare AI algorithm's lesion detection performance with targeted biopsy results. RESULTS: Median age was 66 years (IQR = 60-71), PSA 6.7 ng/ml (IQR = 4.7-9.9) from in-house cohort. In the independent test set, algorithm correctly detected 111 of 198 lesions leading to 56.1% (49.3%-62.6%) sensitivity. PPV was 62.7% (95% CI 54.7%-70.7%) with FDR of 37.3% (95% CI 29.3%-45.3%). Of 79 true positive lesions, 82.3% were tumor positive at targeted biopsy, whereas of 57 false negative lesions, 50.9% were benign at targeted biopsy. Median DSC for lesion segmentation was 0.359. Overall PI-RADS classification accuracy was 30.8% (95% CI 24.6%-37.8%). CONCLUSION: Our cascaded U-Net, residual network architecture can detect, classify cancer suspicious lesions at prostate MRI with good detection, reasonable classification performance metrics.

Why Does Magnetic Resonance Imaging-Targeted Biopsy Miss Clinically Significant Cancer?

PURPOSE: Multiple studies demonstrate magnetic resonance imaging (MRI)-targeted biopsy detects more clinically significant cancer than systematic biopsy; however, some clinically significant cancers are detected by systematic biopsy only. While these events are rare, we sought to perform a retrospective analysis of these cases to ascertain the reasons that MRI-targeted biopsy missed clinically significant cancer which was subsequently detected on systematic prostate biopsy. MATERIALS AND METHODS: Patients were enrolled in a prospective study comparing cancer detection rates by transrectal MRI-targeted fusion biopsy and systematic 12-core biopsy. Patients with an elevated prostate specific antigen (PSA), abnormal digital rectal examination, or imaging findings concerning for prostate cancer underwent prostate MRI and subsequent MRI-targeted and systematic biopsy in the same setting. The subset of patients with grade group (GG) ≥3 cancer found on systematic biopsy and GG ≤2 cancer (or no cancer) on MRI-targeted biopsy was classified as MRI-targeted biopsy misses. A retrospective analysis of the MRI and MRI-targeted biopsy real-time screen captures determined the cause of MRI-targeted biopsy miss. Multivariable logistic regression analysis compared baseline characteristics of patients with MRI-targeted biopsy misses to GG-matched patients whose clinically significant cancer was detected by MRI-targeted biopsy. RESULTS: Over the study period of 2007 to 2019, 2,103 patients met study inclusion criteria and underwent combined MRI-targeted and systematic prostate biopsies. A total of 41 (1.9%) men were classified as MRI-targeted biopsy misses. Most MRI-targeted biopsy misses were due to errors in lesion targeting (21, 51.2%), followed by MRI-invisible lesions (17, 40.5%) and MRI lesions missed by the radiologist (3, 7.1%). On logistic regression analysis, lower Prostate Imaging-Reporting and Data System (PI-RADSTM) score was associated with having clinically significant cancer missed on MRI-targeted biopsy. CONCLUSIONS: While uncommon, most MRI-targeted biopsy misses are due to errors in lesion targeting, which highlights the importance of accurate co-registration and targeting when using software-based fusion platforms. Additionally, some patients will harbor MRI-invisible lesions which are untargetable by MRI-targeted platforms. The presence of a low PI-RADS score despite a high PSA is suggestive of harboring an MRI-invisible lesion.

Deep Learning Based Staging of Bone Lesions From Computed Tomography Scans.

In this study, we formulated an efficient deep learning-based classification strategy for characterizing metastatic bone lesions using computed tomography scans (CTs) of prostate cancer patients. For this purpose, 2,880 annotated bone lesions from CT scans of 114 patients diagnosed with prostate cancer were used for training, validation, and final evaluation. These annotations were in the form of lesion full segmentation, lesion type and labels of either benign or malignant. In this work, we present our approach in developing the state-of-the-art model to classify bone lesions as benign or malignant, where (1) we introduce a valuable dataset to address a clinically important problem, (2) we increase the reliability of our model by patient-level stratification of our dataset following lesion-aware distribution at each of the training, validation, and test splits, (3) we explore the impact of lesion texture, morphology, size, location, and volumetric information on the classification performance, (4) we investigate the functionality of lesion classification using different algorithms including lesion-based average 2D ResNet-50, lesion-based average 2D ResNeXt-50, 3D ResNet-18, 3D ResNet-50, as well as the ensemble of 2D ResNet-50 and 3D ResNet-18. For this purpose, we employed a train/validation/test split equal to 75%/12%/13% with several data augmentation methods applied to the training dataset to avoid overfitting and to increase reliability. We achieved an accuracy of 92.2% for correct classification of benign vs. malignant bone lesions in the test set using an ensemble of lesion-based average 2D ResNet-50 and 3D ResNet-18 with texture, volumetric information, and morphology having the greatest discriminative power respectively. To the best of our knowledge, this is the highest ever achieved lesion-level accuracy having a very comprehensive data set for such a clinically important problem. This level of classification performance in the early stages of metastasis development bodes well for clinical translation of this strategy.

Harnessing clinical annotations to improve deep learning performance in prostate segmentation.

PURPOSE: Developing large-scale datasets with research-quality annotations is challenging due to the high cost of refining clinically generated markup into high precision annotations. We evaluated the direct use of a large dataset with only clinically generated annotations in development of high-performance segmentation models for small research-quality challenge datasets. MATERIALS AND METHODS: We used a large retrospective dataset from our institution comprised of 1,620 clinically generated segmentations, and two challenge datasets (PROMISE12: 50 patients, ProstateX-2: 99 patients). We trained a 3D U-Net convolutional neural network (CNN) segmentation model using our entire dataset, and used that model as a template to train models on the challenge datasets. We also trained versions of the template model using ablated proportions of our dataset, and evaluated the relative benefit of those templates for the final models. Finally, we trained a version of the template model using an out-of-domain brain cancer dataset, and evaluated the relevant benefit of that template for the final models. We used five-fold cross-validation (CV) for all training and evaluation across our entire dataset. RESULTS: Our model achieves state-of-the-art performance on our large dataset (mean overall Dice 0.916, average Hausdorff distance 0.135 across CV folds). Using this model as a pre-trained template for refining on two external datasets significantly enhanced performance (30% and 49% enhancement in Dice scores respectively). Mean overall Dice and mean average Hausdorff distance were 0.912 and 0.15 for the ProstateX-2 dataset, and 0.852 and 0.581 for the PROMISE12 dataset. Using even small quantities of data to train the template enhanced performance, with significant improvements using 5% or more of the data. CONCLUSION: We trained a state-of-the-art model using unrefined clinical prostate annotations and found that its use as a template model significantly improved performance in other prostate segmentation tasks, even when trained with only 5% of the original dataset.

The Risk of Prostate Cancer Progression in Active Surveillance Patients with Bilateral Disease Detected by Combined Magnetic Resonance Imaging-Fusion and Systematic Biopsy.

PURPOSE: We sought to evaluate whether bilateral prostate cancer detected at active surveillance (AS) enrollment is associated with progression to Grade Group (GG) ≥2 and to compare the efficacy of combined targeted biopsy plus systematic biopsy (Cbx) vs systematic biopsy (Sbx) or targeted biopsy alone to detect bilateral disease. MATERIALS AND METHODS: A prospectively maintained database of patients referred to our institution from 2007-2020 was queried. The study cohort included all AS patients with GG1 on confirmatory Cbx and followup of at least 1 year. Cox proportional hazard analysis identified baseline characteristics associated with progression to ≥GG2 at any point throughout followup. RESULTS: Of 579 patients referred, 103 patients had GG1 on Cbx and were included in the study; 49/103 (47.6%) patients progressed to ≥GG2, with 30/72 (41.7%) patients with unilateral disease progressing and 19/31 (61.3%) patients with bilateral disease progressing. Median time to progression was 68 months vs 52 months for unilateral and bilateral disease, respectively (p=0.006). Both prostate specific antigen density (HR 1.72, p=0.005) and presence of bilateral disease (HR 2.21, p=0.012) on confirmatory biopsy were associated with AS progression. At time of progression, GG and risk group were significantly higher in patients with bilateral versus unilateral disease. Cbx detected 16% more patients with bilateral disease than Sbx alone. CONCLUSIONS: Bilateral disease and prostate specific antigen density at confirmatory Cbx conferred greater risk of earlier AS progression. Cbx was superior to Sbx for identifying bilateral disease. AS risk-stratification protocols may benefit from including presence of bilateral disease and should use Cbx to detect bilateral disease.

MRI-guided focal laser ablation of prostate cancer: a prospective single-arm, single-center trial with 3 years of follow-up.

PURPOSE: We aimed to assess post-interventional and 36-month follow-up results of a single-center, single-arm, in-bore phase I trial of focal laser ablation (FLA) guided by multiparametric magnetic resonance imaging (mpMRI). METHODS: FLA procedures were done in-bore MRI using a transperineal approach. Primary endpoints were feasibility and safety expressed as lack of grade 3 complications. Secondary endpoints were changes in international prostate symptom score (IPSS), sexual health inventory for men (SHIM), quality of life (QoL) scores, and serum prostate specific antigen (PSA) levels. Treatment outcomes were assessed by combined mpMRI-ultrasound fusion-guided and extended sextant systematic biopsy after 12, 24, and optionally after 36 months. RESULTS: Fifteen participants were included. Seven patients (46.67%) had Gleason 3+3 and 8 patients (53.33%) had Gleason 3+4 cancer. All patients tolerated the procedure well, and no grade 3/4 complications occurred. All grade 1 and 2 complications were transient and resolved completely. There was no significant change in mean IPSS from baseline (-1, p = 0.460) and QoL (0, p = 0.441) scores following FLA but there was a significant drop in mean SHIM scores (-2, p = 0.010) compared to pretreatment baselines. Mean PSA significantly decreased after FLA (-2.5, p < 0.001). Seven out of 15 patients (46.67%) had residual cancer in, adjacent, or in close proximity to the treatment area (1 × 4+3=7, 1 × 3+4=7, and 5 × 3+3=6). Four out of 15 patients (26.67%) underwent salvage therapy (2 repeat FLA, 2 radical prostatectomy). CONCLUSION: After 3 years of follow-up we conclude focal laser ablation is safe and feasible without significant complications.

Changes in Magnetic Resonance Imaging Using the Prostate Cancer Radiologic Estimation of Change in Sequential Evaluation Criteria to Detect Prostate Cancer Progression for Men on Active Surveillance.

BACKGROUND: The ability of serial magnetic resonance imaging (MRI) to capture pathologic progression during active surveillance (AS) remains in question. OBJECTIVE: To determine whether changes in MRI are associated with pathologic progression for patients on AS. DESIGN, SETTING, AND PARTICIPANTS: From July 2007 through January 2020, we identified all patients evaluated for AS at our institution. Following confirmatory biopsy, a total of 391 patients who underwent surveillance MRI and biopsy at least once were identified (median follow-up of 35.6 mo, interquartile range 19.7-60.6). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: All MRI intervals were scored using the "Prostate Cancer Radiologic Estimation of Change in Sequential Evaluation" (PRECISE) criteria, with PRECISE scores =4 considered a positive change in MRI. A generalized estimating equation-based logistic regression analysis was conducted for all intervals with a PRECISE score of <4 to determine the predictors of Gleason grade group (GG) progression despite stable MRI. RESULTS AND LIMITATIONS: A total of 621 MRI intervals were scored by PRECISE and validated by biopsy. The negative predictive value of stable MRI (PRECISE score <4) was greatest for detecting GG1 to?=?GG3 disease (0.94 [0.91-0.97]). If 2-yr surveillance biopsy were performed exclusively for a positive change in MRI, 3.7% (4/109) of avoided biopsies would have resulted in missed progression from GG1 to?=?GG3 disease. Prostate-specific antigen (PSA) density (odds ratio 1.95 [1.17-3.25], p?=? 0.01) was a risk factor for progression from GG1 to =GG3 disease despite stable MRI. CONCLUSIONS: In patients with GG1 disease and stable MRI (PRECISE score <4) on surveillance, grade progression to?=?GG3 disease is not common. In patients with grade progression detected on biopsy despite stable MRI, elevated PSA density appeared to be a risk factor for progression to?=?GG3 disease. PATIENT SUMMARY: For patients with low-risk prostate cancer on active surveillance, the risk of progressing to grade group 3 disease is low with a stable magnetic resonance image (MRI) after 2?yr. Having higher prostate-specific antigen density increases the risk of progression, despite having a stable MRI.

Prognostic Features of Biochemical Recurrence of Prostate Cancer Following Radical Prostatectomy Based on Multiparametric MRI and Immunohistochemistry Analysis of MRI-guided Biopsy Specimens.

Background Although prostate MRI is routinely used for the detection and staging of localized prostate cancer, imaging-based assessment and targeted molecular sampling for risk stratification are an active area of research. Purpose To evaluate features of preoperative MRI and MRI-guided biopsy immunohistochemistry (IHC) findings associated with biochemical recurrence (BCR) of prostate cancer after surgery. Materials and Methods In this retrospective case-control study, patients underwent multiparametric MRI before MRI-guided biopsy followed by radical prostatectomy between 2008 and 2016. Lesions were retrospectively scored with the Prostate Imaging Reporting and Data System (PI-RADS) (version 2) by radiologists who were blinded to the clinical-pathologic results. The IHC staining, including stains for the ETS-related gene, phosphatase and tensin homolog, androgen receptor, prostate specific antigen, and p53, was performed with targeted biopsy specimens of the index lesion (highest suspicion at MRI and pathologic grade) and scored by pathologists who were blinded to clinical-pathologic outcomes. Cox proportional hazards regression analysis was used to evaluate associations with recurrence-free survival (RFS). Results The median RFS was 31.7 months (range, 1-101 months) for 39 patients (median age, 62 years; age range, 47-76 years) without BCR and 14.6 months (range, 1-61 months) for 40 patients (median age, 59 years; age range, 47-73 years) with BCR. MRI features that showed a significant relationship with the RFS interval included an index lesion with a PI-RADS score of 5 (hazard ratio [HR], 2.10; 95% CI: 1.05, 4.21; P = .04); index lesion burden, defined as ratio of index lesion volume to prostate volume (HR, 1.55; 95% CI: 1.2, 2.1; P = .003); and suspicion of extraprostatic extension (EPE) (HR, 2.18; 95% CI: 1.1, 4.2; P = .02). Presurgical multivariable analysis indicated that suspicion of EPE at MRI (adjusted HR, 2.19; 95% CI: 1.1, 4.3; P = .02) and p53 stain intensity (adjusted HR, 2.22; 95% CI: 1.0, 4.7; P = .04) were significantly associated with RFS. Conclusion MRI features, including Prostate Imaging Reporting and Data System score, index lesion burden, extraprostatic extension, and preoperative guided biopsy p53 immunohistochemistry stain intensity are associated with biochemical relapse of prostate cancer after surgery. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Costa in this issue.

Risk of adverse pathology at prostatectomy in the era of MRI and targeted biopsies; rethinking active surveillance for intermediate risk prostate cancer patients.

PURPOSE: Men with intermediate risk (IR) prostate cancer (CaP) are often excluded from active surveillance (AS) due to higher rates of adverse pathology (AP). We determined our rate of AP in men who underwent multiparametric MRI (MpMRI) with combined biopsy (CB) consisting of targeted biopsy (TB) and systematic biopsy (SB) prior to radical prostatectomy (RP). METHODS: A retrospective review was conducted of men with Gleason Grade Group (GG) 2 disease who underwent RP after SB alone or after preoperative MRI with CB. AP was defined as either pathologic stage T3a (AP ≥ T3a) or pathologic stage T3b (AP ≥ T3b) and/or GG upgrading. Rates of AP were determined for both groups and those who fit the National Comprehensive Cancer Network (NCCN) definition of favorable IR (FIR) or the low volume IR (LVIR) criteria. Multivariable logistic regression was used to determine predictive factors. RESULTS: The overall rate of AP ≥ T3b was 21.2% in the SB group vs. 8.6% in the MRI with CB group, P = 0.006. This rate was lowered to 6.8% and 5.6% when men met the definition of NCCN FIR or LVIR, respectively. Suspicion for extraprostatic extension (EPE) (OR 7.65, 95% CI 1.77-33.09, P = 0.006) and positive cores of GG 2 on SB (OR 1.43, 95% CI 1.05-1.96, P = 0.023) were significant for predicting AP ≥ T3b. CONCLUSIONS: Rates of AP at RP after MRI with CB are lower than studies prior to the adoption of this technology, suggesting that more men with IR disease may be considered for AS. However, increasing cores positive on SB and MRI findings suggestive of EPE remain unsafe.

Quick guide on radiology image pre-processing for deep learning applications in prostate cancer research.

Purpose: Deep learning has achieved major breakthroughs during the past decade in almost every field. There are plenty of publicly available algorithms, each designed to address a different task of computer vision in general. However, most of these algorithms cannot be directly applied to images in the medical domain. Herein, we are focused on the required preprocessing steps that should be applied to medical images prior to deep neural networks. Approach: To be able to employ the publicly available algorithms for clinical purposes, we must make a meaningful pixel/voxel representation from medical images which facilitates the learning process. Based on the ultimate goal expected from an algorithm (classification, detection, or segmentation), one may infer the required pre-processing steps that can ideally improve the performance of that algorithm. Required pre-processing steps for computed tomography (CT) and magnetic resonance (MR) images in their correct order are discussed in detail. We further supported our discussion by relevant experiments to investigate the efficiency of the listed preprocessing steps. Results: Our experiments confirmed how using appropriate image pre-processing in the right order can improve the performance of deep neural networks in terms of better classification and segmentation. Conclusions: This work investigates the appropriate pre-processing steps for CT and MR images of prostate cancer patients, supported by several experiments that can be useful for educating those new to the field (https://github.com/NIH-MIP/Radiology_Image_Preprocessing_for_Deep_Learning).

Making a case "for" focal therapy of the prostate in intermediate risk prostate cancer: current perspective and ongoing trials.

Focal therapy is growing as an alternative management options for men with clinically localized prostate cancer. Parallel to the increasing popularity of active surveillance (AS) as a treatment for low-risk disease, there has been an increased interest towards providing focal therapy for patients with intermediate-risk disease. Focal therapy can act as a logical "middle ground" in patients who seek treatment while minimizing potential side effects of definitive whole-gland treatment. The aim of the current review is to define the rationale of focal therapy in patients with intermediate-risk prostate cancer and highlight the importance of patient selection in focal therapy candidacy.