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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.)

Purpose: Provide a direct, non-invasive diagnostic measure of microscopic tissue texture in the size scale between tens of microns and the much larger scale measurable by clinical imaging. This paper presents a method and data demonstrating the ability to measure these microscopic pathologic tissue textures (histology) in the presence of subject motion in an MR scanner. This size range is vital to diagnosing a wide range of diseases., Theory/methods: MR micro-Texture (MRµT) resolves these textures by a combination of measuring a targeted set of k-values to characterize texture-as in diffraction analysis of materials, performing a selective internal excitation to isolate a volume of interest (VOI), applying a high k-value phase encode to the excited spins in the VOI, and acquiring each individual k-value data point in a single excitation-providing motion immunity and extended acquisition time for maximizing signal-to-noise ratio. Additional k-value measurements from the same tissue can be made to characterize the tissue texture in the VOI-there is no need for these additional measurements to be spatially coherent as there is no image to be reconstructed. This method was applied to phantoms and tissue specimens including human prostate tissue., Results: Data demonstrating resolution <50 µm, motion immunity, and clearly differentiating between normal and cancerous tissue textures are presented., Conclusion: The data reveal textural differences not resolvable by standard MR imaging. As MRµT is a pulse sequence, it is directly translatable to MRI scanners currently in clinical practice to meet the need for further improvement in cancer imaging., (© 2021 International Society for Magnetic Resonance in Medicine.)

The use of MRI for prostate cancer diagnosis and treatment is increasing rapidly. However, identifying the presence and extent of cancer on MRI remains challenging, leading to high variability in detection even among expert radiologists. Improvement in cancer detection on MRI is essential to reducing this variability and maximizing the clinical utility of MRI. To date, such improvement has been limited by the lack of accurately labeled MRI datasets. Data from patients who underwent radical prostatectomy enables the spatial alignment of digitized histopathology images of the resected prostate with corresponding pre-surgical MRI. This alignment facilitates the delineation of detailed cancer labels on MRI via the projection of cancer from histopathology images onto MRI. We introduce a framework that performs 3D registration of whole-mount histopathology images to pre-surgical MRI in three steps. First, we developed a novel multi-image super-resolution generative adversarial network (miSRGAN), which learns information useful for 3D registration by producing a reconstructed 3D MRI. Second, we trained the network to learn information between histopathology slices to facilitate the application of 3D registration methods. Third, we registered the reconstructed 3D histopathology volumes to the reconstructed 3D MRI, mapping the extent of cancer from histopathology images onto MRI without the need for slice-to-slice correspondence. When compared to interpolation methods, our super-resolution reconstruction resulted in the highest PSNR relative to clinical 3D MRI (32.15 dB vs 30.16 dB for BSpline interpolation). Moreover, the registration of 3D volumes reconstructed via super-resolution for both MRI and histopathology images showed the best alignment of cancer regions when compared to (1) the state-of-the-art RAPSODI approach, (2) volumes that were not reconstructed, or (3) volumes that were reconstructed using nearest neighbor, linear, or BSpline interpolations. The improved 3D alignment of histopathology images and MRI facilitates the projection of accurate cancer labels on MRI, allowing for the development of improved MRI interpretation schemes and machine learning models to automatically detect cancer on MRI., Competing Interests: Declaration of Competing Interest Mirabela Rusu has research grants from GE Healthcare and Philips Healthcare. The authors declare no other known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objectives: We sought to identify clinical and magnetic resonance imaging (MRI) characteristics in men with the Prostate Imaging - Reporting and Data System (PI-RADS) category 3 index lesions that predict clinically significant prostate cancer (CaP) on MRI targeted biopsy., Materials and Methods: Multicenter study of prospectively collected data for biopsy-naive men (n = 247) who underwent MRI-targeted and systematic biopsies for PI-RADS 3 index lesions. The primary endpoint was diagnosis of clinically significant CaP (Grade Group ≥2). Multivariable logistic regression models assessed for factors associated with clinically significant CaP. The probability distributions of clinically significant CaP based on different levels of predictors of multivariable models were plotted in a heatmap., Results: Men with clinically significant CaP had smaller prostate volume (39.20 vs. 55.10 ml, P < 0.001) and lower apparent diffusion coefficient (ADC) values (973 vs. 1068 μm 2 /s, P = 0.013), but higher prostate-specific antigen (PSA) density (0.21 vs. 0.13 ng/ml 2 , P = 0.027). On multivariable analyses, lower prostate volume (odds ratio [OR]: 0.95, 95% confidence interval [CI]: 0.92-0.97), lower ADC value (OR: 0.99, 95% CI: 0.99-1.00), and Prostate-specific antigen density >0.15 ng/ml 2 (OR: 3.51, 95% CI 1.61-7.68) were independently associated with significant CaP., Conclusion: Higher PSA density, lower prostate volume and ADC values are associated with clinically significant CaP in biopsy-naïve men with PI-RADS 3 lesions. We present regression-derived probabilities of detecting clinically significant CaP based on various clinical and imaging values that can be used in decision-making. Our findings demonstrate an opportunity for MRI refinement or biomarker discovery to improve risk stratification for PI-RADS 3 lesions., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: Prostate imaging reporting and data system version 2 (PI-RADS v2) relegates dynamic contrast enhanced (DCE) imaging to a minor role. We sought to determine how often DCE is used in PI-RADS v2 scoring., Materials and Methods: We retrospectively reviewed data from 388 patients who underwent prostate magnetic resonance imaging and subsequent biopsy from January 2016 through December 2017. In accordance with PI-RADS v2, DCE was deemed necessary if a peripheral-zone lesion had a diffusion-weighted imaging score of 3, or if a transition-zone lesion had a T2 score of 3 and diffusion-weighted imaging experienced technical failure. Receiver operating characteristic curve analysis assessed the accuracy of prostate-specific antigen density (PSAD) at different threshold values for differentiating lesions that would be equivocal with noncontrast technique. Accuracy of PSAD was compared to DCE using McNemar's test., Results: Sixty-nine lesions in 62 patients (16%) required DCE for PI-RADS scoring. Biopsy of 10 (14%) of these lesions showed clinically significant cancer (Gleason score ≥7). In the subgroup of patients with equivocal lesions, those with clinically significant cancer had significantly higher PSADs than those with clinically insignificant lesions (means of 0.18 and 0.13 ng/mL/mL, respectively; P= 0.038). In this subgroup, there was no statistical difference in accuracy in determining clinically significant cancer between a PSAD threshold value of 0.13 and DCE (P= 0.25)., Conclusions: Only 16% of our patients needed DCE to generate the PI-RADS version 2 score, raising the possibility of limiting the initial screening prostate MRI to a noncontrast exam. PSAD may also be used to further decrease the need for or to replace DCE altogether., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: Multiparametric magnetic resonance imaging (mpMRI) interpreted by experts is a powerful tool for diagnosing prostate cancer. However, the generalizability of published results across radiologists of varying expertise has not been verified., Objective: To assess variability in mpMRI reporting and diagnostic accuracy across radiologists of varying experience in routine clinical care., Design, Setting, and Participants: Men who underwent mpMRI and MR-fusion biopsy between 2014-2016. Each MRI scan was read by one of nine radiologists using the Prostate Imaging Reporting and Data System (PIRADS) and was not re-read before biopsy. Biopsy histopathology was the reference standard., Outcome Measurements and Statistical Analysis: Outcomes were the PIRADS score distribution and diagnostic accuracy across nine radiologists. We evaluated the association between age, prostate-specific antigen, PIRADS score, and radiologist in predicting clinically significant cancer (Gleason ≥7) using multivariable logistic regression. We conducted sensitivity analyses for case volume and changes in accuracy over time., Results and Limitations: We analyzed data for 409 subjects with 503 MRI lesions. While the number of lesions (mean 1.2 lesions/patient) did not differ across radiologists, substantial variation existed in PIRADS distribution and cancer yield. The significant cancer detection rate was 3-27% for PIRADS 3 lesions, 23-65% for PIRADS 4, and 40-80% for PIRADS 5 across radiologists. Some 13-60% of men with a PIRADS score of <3 on MRI harbored clinically significant cancer. The area under the receiver operating characteristic curve varied from 0.69 to 0.81 for detection of clinically significant cancer. PIRADS score (p<0.0001) and radiologist (p=0.042) were independently associated with cancer in multivariable analysis. Neither individual radiologist volume nor study period impacted the results. MRI scans were not retrospectively re-read by all radiologists, precluding measurement of inter-observer agreement., Conclusions: We observed considerable variability in PIRADS score assignment and significant cancer yield across radiologists. We advise internal evaluation of mpMRI accuracy before widespread adoption., Patient Summary: We evaluated the interpretation of multiparametric magnetic resonance imaging of the prostate in routine clinical care. Diagnostic accuracy depends on the Prostate Imaging Reporting and Data System score and the radiologist., (Copyright © 2017 European Association of Urology. All rights reserved.)

Purpose: After an initial negative biopsy there is an ongoing need for strategies to improve patient selection for repeat biopsy as well as the diagnostic yield from repeat biopsies., Materials and Methods: As a collaborative initiative of the AUA (American Urological Association) and SAR (Society of Abdominal Radiology) Prostate Cancer Disease Focused Panel, an expert panel of urologists and radiologists conducted a literature review and formed consensus statements regarding the role of prostate magnetic resonance imaging and magnetic resonance imaging targeted biopsy in patients with a negative biopsy, which are summarized in this review., Results: The panel recognizes that many options exist for men with a previously negative biopsy. If a biopsy is recommended, prostate magnetic resonance imaging and subsequent magnetic resonance imaging targeted cores appear to facilitate the detection of clinically significant disease over standardized repeat biopsy. Thus, when high quality prostate magnetic resonance imaging is available, it should be strongly considered for any patient with a prior negative biopsy who has persistent clinical suspicion for prostate cancer and who is under evaluation for a possible repeat biopsy. The decision of whether to perform magnetic resonance imaging in this setting must also take into account the results of any other biomarkers and the cost of the examination, as well as the availability of high quality prostate magnetic resonance imaging interpretation. If magnetic resonance imaging is done, it should be performed, interpreted and reported in accordance with PI-RADS version 2 (v2) guidelines. Experience of the reporting radiologist and biopsy operator are required to achieve optimal results and practices integrating prostate magnetic resonance imaging into patient care are advised to implement quality assurance programs to monitor targeted biopsy results., Conclusions: Patients receiving a PI-RADS assessment category of 3 to 5 warrant repeat biopsy with image guided targeting. While transrectal ultrasound guided magnetic resonance imaging fusion or in-bore magnetic resonance imaging targeting may be valuable for more reliable targeting, especially for lesions that are small or in difficult locations, in the absence of such targeting technologies cognitive (visual) targeting remains a reasonable approach in skilled hands. At least 2 targeted cores should be obtained from each magnetic resonance imaging defined target. Given the number of studies showing a proportion of missed clinically significant cancers by magnetic resonance imaging targeted cores, a case specific decision must be made whether to also perform concurrent systematic sampling. However, performing solely targeted biopsy should only be considered once quality assurance efforts have validated the performance of prostate magnetic resonance imaging interpretations with results consistent with the published literature. In patients with negative or low suspicion magnetic resonance imaging (PI-RADS assessment category of 1 or 2, respectively), other ancillary markers (ie PSA, PSAD, PSAV, PCA3, PHI, 4K) may be of value in identifying patients warranting repeat systematic biopsy, although further data are needed on this topic. If a repeat biopsy is deferred on the basis of magnetic resonance imaging findings, then continued clinical and laboratory followup is advised and consideration should be given to incorporating repeat magnetic resonance imaging in this diagnostic surveillance regimen., (Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2014 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2014 Elsevier Inc. All rights reserved.)

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., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Background: Conventional biopsy fails to detect the presence of some prostate cancers (PCas). Men with a prior negative biopsy but persistently elevated prostate-specific antigen (PSA) pose a diagnostic dilemma, as some harbor elusive cancer., Objective: To determine whether use of magnetic resonance-ultrasound (MR-US) fusion biopsy results in improved detection of PCa compared to repeat conventional biopsy., Design, Setting, and Participants: In a consecutive-case series, 105 subjects with prior negative biopsy and elevated PSA values underwent multiparametric magnetic resonance imaging (MRI) and fusion biopsy in an outpatient setting., Intervention: Suspicious areas on multiparametric MRI were delineated and graded by a radiologist; MR-US fusion biopsy was performed by a urologist using the Artemis device; targeted and systematic biopsies were obtained regardless of MRI result., Outcome Measurements and Statistical Analysis: Detection rates of all PCa and clinically significant PCa (Gleason ≥3+4 or Gleason 6 with maximal cancer core length ≥4 mm) were determined. The yield of targeted biopsy was compared to systematic biopsy. The ability of an MRI grading system to predict clinically significant cancer was investigated. Stepwise multivariate logistic regression analysis was performed to determine predictors of significant cancer on biopsy., Results and Limitations: Fusion biopsy revealed PCa in 36 of 105 men (34%; 95% confidence interval [CI], 25-45). Seventy-two percent of men with PCa had clinically significant disease; 21 of 23 men (91%) with PCa on targeted biopsy had significant cancer compared to 15 of 28 (54%) with systematic biopsy. Degree of suspicion on MRI was the most powerful predictor of significant cancer on multivariate analysis. Twelve of 14 (86%) subjects with a highly suspicious MRI target were diagnosed with clinically significant cancer., Conclusions: MR-US fusion biopsy provides improved detection of PCa in men with prior negative biopsies and elevated PSA values. Most cancers found were clinically significant., (Copyright © 2013 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

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 © 2014 Elsevier Inc. All rights reserved.)

Purpose: Targeted biopsy of lesions identified on magnetic resonance imaging may enhance the detection of clinically relevant prostate cancers. We evaluated prostate cancer detection rates in 171 consecutive men using magnetic resonance ultrasound fusion prostate biopsy., Materials and Methods: Subjects underwent targeted biopsy for active surveillance (106) or persistently increased prostate specific antigen but negative prior conventional biopsy (65). Before biopsy, each man underwent multiparametric magnetic resonance imaging at 3.0 Tesla. Lesions on magnetic resonance imaging were outlined in 3 dimensions and assigned increasing cancer suspicion levels (image grade 1 to 5) by a uroradiologist. A biopsy tracking system was used to fuse the stored magnetic resonance imaging with real-time ultrasound, generating a 3-dimensional prostate model on the fly. Working from the 3-dimensional model, transrectal biopsy of target lesions and 12 systematic biopsies were performed with the patient under local anesthesia in the clinic., Results: A total of 171 subjects (median age 65 years) underwent targeted biopsy. At biopsy, median prostate specific antigen was 4.9 ng/ml and prostate volume was 48 cc. A targeted biopsy was 3 times more likely to identify cancer than a systematic biopsy (21% vs 7%). Prostate cancer was found in 53% of men, 38% of whom had Gleason grade 7 or greater cancer. Of the men with Gleason 7 or greater cancer 38% had disease detected only on targeted biopsies. Targeted biopsy findings correlated with level of suspicion on magnetic resonance imaging. Of 16 men 15 (94%) with an image grade 5 target (highest suspicion) had prostate cancer, including 7 with Gleason 7 or greater cancer., Conclusions: Prostate lesions identified on magnetic resonance imaging can be accurately targeted using magnetic resonance ultrasound fusion biopsy by a urologist in clinic. Biopsy findings correlate with level of suspicion on magnetic resonance imaging., (Copyright © 2013 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.)

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.

BackgroundMen 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.MethodsIn 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.FindingsBetween 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.Interpretation24-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.FundingInsightec and the National Cancer Institute.

Purpose: Established in 1994, the Epstein histological criteria (Gleason score 6 or less, 2 or fewer cores positive and 50% or less of any core) have been widely used to select men for active surveillance. However, with the advent of targeted biopsy, which may be more accurate than conventional biopsy, we reevaluated the likelihood of reclassification upon confirmatory rebiopsy using multiparametric magnetic resonance imaging-ultrasound fusion. Materials and Methods: We identified 113 men enrolled in active surveillance at our institution who met Epstein criteria and subsequently underwent confirmatory targeted biopsy via multiparametric magnetic resonance imaging-ultrasound fusion. Median patient age was 64 years, median prostate specific antigen was 4.2 ng/ml and median prostate volume was 46.8 cc. Targets or regions of interest on multiparametric magnetic resonance imaging-ultrasound fusion were graded by suspicion level and biopsied at 3 mm intervals along the longest axis (median 10.5 mm). Also, 12 systematic cores were obtained during confirmatory rebiopsy. Our reporting is consistent with START (Standards of Reporting for MRI-targeted Biopsy Studies) criteria. Results: Confirmatory fusion biopsy resulted in reclassification in 41 men (36%), including 26 (23%) due to Gleason grade 6 or greater and 15 (13%) due to high volume Gleason 6 disease. When stratified by suspicion on multiparametric magnetic resonance imaging-ultrasound fusion, the likelihood of reclassification was 24% to 29% for target grade 0 to 3, 45% for grade 4 and 100% for grade 5 (p = 0.001). Men with grade 4 and 5 vs lower grade targets were greater than 3 times more likely to be reclassified (OR 3.2, 95% CI 1.4-7.1, p = 0.006). Conclusions: Upon confirmatory rebiopsy using multiparametric magnetic resonance imaging-ultrasound fusion men with high suspicion targets on imaging were reclassified 45% to 100% of the time. Criteria for active surveillance should be reevaluated when multiparametric magnetic resonance imaging-ultrasound fusion guided prostate biopsy is used. © 2014 American Urological Association Education and Research, Inc.

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]

A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care. [ABSTRACT FROM AUTHOR]

Purpose: While multi‐parametric magnetic resonance imaging (MRI) shows great promise in assisting with prostate cancer diagnosis and localization, subtle differences in appearance between cancer and normal tissue lead to many false positive and false negative interpretations by radiologists. We sought to automatically detect aggressive cancer (Gleason pattern ≥ 4) and indolent cancer (Gleason pattern 3) on a per‐pixel basis on MRI to facilitate the targeting of aggressive cancer during biopsy. Methods: We created the Stanford Prostate Cancer Network (SPCNet), a convolutional neural network model, trained to distinguish between aggressive cancer, indolent cancer, and normal tissue on MRI. Ground truth cancer labels were obtained by registering MRI with whole‐mount digital histopathology images from patients who underwent radical prostatectomy. Before registration, these histopathology images were automatically annotated to show Gleason patterns on a per‐pixel basis. The model was trained on data from 78 patients who underwent radical prostatectomy and 24 patients without prostate cancer. The model was evaluated on a pixel and lesion level in 322 patients, including six patients with normal MRI and no cancer, 23 patients who underwent radical prostatectomy, and 293 patients who underwent biopsy. Moreover, we assessed the ability of our model to detect clinically significant cancer (lesions with an aggressive component) and compared it to the performance of radiologists. Results: Our model detected clinically significant lesions with an area under the receiver operator characteristics curve of 0.75 for radical prostatectomy patients and 0.80 for biopsy patients. Moreover, the model detected up to 18% of lesions missed by radiologists, and overall had a sensitivity and specificity that approached that of radiologists in detecting clinically significant cancer. Conclusions: Our SPCNet model accurately detected aggressive prostate cancer. Its performance approached that of radiologists, and it helped identify lesions otherwise missed by radiologists. Our model has the potential to assist physicians in specifically targeting the aggressive component of prostate cancers during biopsy or focal treatment. [ABSTRACT FROM AUTHOR]

Purpose: Magnetic resonance imaging (MRI) has great potential to improve prostate cancer diagnosis; however, subtle differences between cancer and confounding conditions render prostate MRI interpretation challenging. The tissue collected from patients who undergo radical prostatectomy provides a unique opportunity to correlate histopathology images of the prostate with preoperative MRI to accurately map the extent of cancer from histopathology images onto MRI. We seek to develop an open‐source, easy‐to‐use platform to align presurgical MRI and histopathology images of resected prostates in patients who underwent radical prostatectomy to create accurate cancer labels on MRI. Methods: Here, we introduce RAdiology Pathology Spatial Open‐Source multi‐Dimensional Integration (RAPSODI), the first open‐source framework for the registration of radiology and pathology images. RAPSODI relies on three steps. First, it creates a three‐dimensional (3D) reconstruction of the histopathology specimen as a digital representation of the tissue before gross sectioning. Second, RAPSODI registers corresponding histopathology and MRI slices. Third, the optimized transforms are applied to the cancer regions outlined on the histopathology images to project those labels onto the preoperative MRI. Results: We tested RAPSODI in a phantom study where we simulated various conditions, for example, tissue shrinkage during fixation. Our experiments showed that RAPSODI can reliably correct multiple artifacts. We also evaluated RAPSODI in 157 patients from three institutions that underwent radical prostatectomy and have very different pathology processing and scanning. RAPSODI was evaluated in 907 corresponding histpathology‐MRI slices and achieved a Dice coefficient of 0.97 ± 0.01 for the prostate, a Hausdorff distance of 1.99 ± 0.70 mm for the prostate boundary, a urethra deviation of 3.09 ± 1.45 mm, and a landmark deviation of 2.80 ± 0.59 mm between registered histopathology images and MRI. Conclusion: Our robust framework successfully mapped the extent of cancer from histopathology slices onto MRI providing labels from training machine learning methods to detect cancer on MRI. [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]

Utilization of pre-biopsy multiparametric MRI (mpMRI) is increasing. To optimize the usefulness of mpMRI, physicians should accurately quote patients a numerical risk of cancer based on their MRI. The Prostate Imaging Reporting and Data System (PIRADS) standardizes interpretation of mpMRI; however, reported rates of clinically significant prostate cancer (CSC) stratified by PIRADS score vary widely. While some publications use radical prostatectomy (RP) specimens as gold standard, others use biopsy. We hypothesized that much of the variation in CSC stems from differences in cancer prevalence in RP cohorts (100% prevalence) vs biopsy cohorts. To quantify the impact of this selection bias on cancer yield according to PIRADS score, we analyzed data from 614 men with 854 lesions who underwent targeted biopsy from 2014 to 2018. Of these, 125 men underwent RP. We compared the PIRADS detection rates of CSC (Gleason ≥7) on targeted biopsy between the biopsy-only and RP cohorts. For all PIRADS scores, CSC yield was much greater in patients who underwent RP. For example, CSC was found in 30% of PIRADS 3 lesions in men who underwent RP vs 7.6% in men who underwent biopsy. Our results show that mpMRI performance appears to be better in men who undergo RP compared with those who only receive biopsy. Physicians should understand the effect of this selection bias and its magnitude when discussing mpMRI results with patients considering biopsy, and take great caution in quoting CSC yields from publications using RP as gold standard. [ABSTRACT FROM AUTHOR]

Purpose Established in 1994, the Epstein histological criteria (Gleason score 6 or less, 2 or fewer cores positive and 50% or less of any core) have been widely used to select men for active surveillance. However, with the advent of targeted biopsy, which may be more accurate than conventional biopsy, we reevaluated the likelihood of reclassification upon confirmatory rebiopsy using multiparametric magnetic resonance imaging-ultrasound fusion. Materials and Methods We identified 113 men enrolled in active surveillance at our institution who met Epstein criteria and subsequently underwent confirmatory targeted biopsy via multiparametric magnetic resonance imaging-ultrasound fusion. Median patient age was 64 years, median prostate specific antigen was 4.2 ng/ml and median prostate volume was 46.8 cc. Targets or regions of interest on multiparametric magnetic resonance imaging-ultrasound fusion were graded by suspicion level and biopsied at 3 mm intervals along the longest axis (median 10.5 mm). Also, 12 systematic cores were obtained during confirmatory rebiopsy. Our reporting is consistent with START (Standards of Reporting for MRI-targeted Biopsy Studies) criteria. Results Confirmatory fusion biopsy resulted in reclassification in 41 men (36%), including 26 (23%) due to Gleason grade 6 or greater and 15 (13%) due to high volume Gleason 6 disease. When stratified by suspicion on multiparametric magnetic resonance imaging-ultrasound fusion, the likelihood of reclassification was 24% to 29% for target grade 0 to 3, 45% for grade 4 and 100% for grade 5 (p = 0.001). Men with grade 4 and 5 vs lower grade targets were greater than 3 times more likely to be reclassified (OR 3.2, 95% CI 1.4-7.1, p = 0.006). Conclusions Upon confirmatory rebiopsy using multiparametric magnetic resonance imaging-ultrasound fusion men with high suspicion targets on imaging were reclassified 45% to 100% of the time. Criteria for active surveillance should be reevaluated when multiparametric magnetic resonance imaging-ultrasound fusion guided prostate biopsy is used. [ABSTRACT FROM AUTHOR]

Objectives: To determine whether PSA density (PSAD), can sub-stratify risk of biopsy upgrade among men on active surveillance (AS) with normal baseline MRI.Methods: We identified a cohort of patients with low and favorable intermediate-risk prostate cancer on AS at two large academic centers from February 2013 - December 2017. Analysis was restricted to patients with GG1 cancer on initial biopsy and a negative baseline or surveillance mpMRI, defined by the absence of PI-RADS 2 or greater lesions. We assessed ability of PSA, prostate volume and PSAD to predict upgrading on confirmatory biopsy.Results: We identified 98 patients on AS with negative baseline or surveillance mpMRI. Median PSA at diagnosis was 5.8 ng/mL and median PSAD was 0.08 ng/mL/mL. Fourteen men (14.3%) experienced Gleason upgrade at confirmatory biopsy. Patients who were upgraded had higher PSA (7.9 vs 5.4 ng/mL, P = .04), PSAD (0.20 vs 0.07 ng/mL/mL, P < .001), and lower prostate volumes (42.5 vs 65.8 mL, P = .01). On multivariate analysis, PSAD was associated with pathologic upgrade (OR 2.23 per 0.1-increase, P = .007). A PSAD cutoff at 0.08 generated a NPV of 98% for detection of pathologic upgrade.Conclusion: PSAD reliably discriminated the risk of Gleason upgrade at confirmatory biopsy among men with low-grade prostate cancer with negative MRI. PSAD could be clinically implemented to reduce the intensity of surveillance for a subset of patients. [ABSTRACT FROM AUTHOR]

Objective: To create an online resource that teaches urologists how to interpret prostate multiparametric magnetic resonance imaging (mpMRI). As prostate mpMRI becomes widely adopted for cancer diagnosis and targeted biopsy, it is increasingly important that urologists are comfortable and experienced in assessing the images. The purpose of this study was to create an online mpMRI ibook and measure its effect on instilling proficiency among urology residents.Methods: We created a case-based ibook aimed at teaching clinicians how to identify and score prostate lesions on mpMRI using the Prostate Imaging-Reporting and Data System (PIRADS) v2. Residents completed a 43-question pretest before gaining access to the ibook for 1 month. The test asks participants to identify and score visible lesions using interactive mpMRI images. After a formal review of the material, they completed a post-test. Participants also rated their diagnostic confidence on a scale of 1-10 before and after reviewing the ibook. The change in performance and confidence scores for each resident was compared using Wilcoxon signed-rank test.Results: Eleven urology residents completed the pretest, review session and post-test. The mean test score rose from 37% (median 40%) to 57% (median 58%) after reviewing the ibook. Improvement was significant (P= .0039). Confidence scores also improved (P = .001).Conclusion: We created an interactive ibook that teaches urologists how to evaluate prostate mpMRIs and demonstrated improved performance in interpretation among urology residents. This effective module can be incorporated into resident education on a national level and offered as a self-teaching resource for practicing urologists. [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]

• Distinguishing indolent from aggressive prostate cancer on MRI is a clinical need • An automated method for distinguishing indolent from aggressive cancer is presented • Correlated feature learning is used to capture pathology characteristics on MRI • Deep learning is used for cancer detection and characterization of aggressiveness • Our method can improve prostate cancer care by guiding treatment planning [Display omitted] Automated methods for detecting prostate cancer and distinguishing indolent from aggressive disease on Magnetic Resonance Imaging (MRI) could assist in early diagnosis and treatment planning. Existing automated methods of prostate cancer detection mostly rely on ground truth labels with limited accuracy, ignore disease pathology characteristics observed on resected tissue, and cannot selectively identify aggressive (Gleason Pattern ≥ 4) and indolent (Gleason Pattern=3) cancers when they co-exist in mixed lesions. In this paper, we present a radiology-pathology fusion approach, CorrSigNIA, for the selective identification and localization of indolent and aggressive prostate cancer on MRI. CorrSigNIA uses registered MRI and whole-mount histopathology images from radical prostatectomy patients to derive accurate ground truth labels and learn correlated features between radiology and pathology images. These correlated features are then used in a convolutional neural network architecture to detect and localize normal tissue, indolent cancer, and aggressive cancer on prostate MRI. CorrSigNIA was trained and validated on a dataset of 98 men, including 74 men that underwent radical prostatectomy and 24 men with normal prostate MRI. CorrSigNIA was tested on three independent test sets including 55 men that underwent radical prostatectomy, 275 men that underwent targeted biopsies, and 15 men with normal prostate MRI. CorrSigNIA achieved an accuracy of 80% in distinguishing between men with and without cancer, a lesion-level ROC-AUC of 0.81 ± 0.31 in detecting cancers in both radical prostatectomy and biopsy cohort patients, and lesion-levels ROC-AUCs of 0.82 ± 0.31 and 0.86 ± 0.26 in detecting clinically significant cancers in radical prostatectomy and biopsy cohort patients respectively. CorrSigNIA consistently outperformed other methods across different evaluation metrics and cohorts. In clinical settings, CorrSigNIA may be used in prostate cancer detection as well as in selective identification of indolent and aggressive components of prostate cancer, thereby improving prostate cancer care by helping guide targeted biopsies, reducing unnecessary biopsies, and selecting and planning treatment. [ABSTRACT FROM AUTHOR]

Background: While multiparametric MRI (mpMRI) has high sensitivity for detection of clinically significant prostate cancer (CSC), false positives and negatives remain common. Calculators that combine mpMRI with clinical variables can improve cancer risk assessment, while providing more accurate predictions for individual patients. We sought to create and externally validate nomograms incorporating Prostate Imaging Reporting and Data System (PIRADS) scores and clinical data to predict the presence of CSC in men of all biopsy backgrounds.Methods: Data from 2125 men undergoing mpMRI and MR fusion biopsy from 2014 to 2018 at Stanford, Yale, and UAB were prospectively collected. Clinical data included age, race, PSA, biopsy status, PIRADS scores, and prostate volume. A nomogram predicting detection of CSC on targeted or systematic biopsy was created.Results: Biopsy history, Prostate Specific Antigen (PSA) density, PIRADS score of 4 or 5, Caucasian race, and age were significant independent predictors. Our nomogram-the Stanford Prostate Cancer Calculator (SPCC)-combined these factors in a logistic regression to provide stronger predictive accuracy than PSA density or PIRADS alone. Validation of the SPCC using data from Yale and UAB yielded robust AUC values.Conclusions: The SPCC combines pre-biopsy mpMRI with clinical data to more accurately predict the probability of CSC in men of all biopsy backgrounds. The SPCC demonstrates strong external generalizability with successful validation in two separate institutions. The calculator is available as a free web-based tool that can direct real-time clinical decision-making. [ABSTRACT FROM AUTHOR]

• For the first time, deep learning is used for MRI-histopathology image registration. • Our registration network avoids the shortcomings of multi-modal similarity measures. • Our registration network allows mapping of the ground truth cancer labels onto MRI. • This important advance will facilitate the detection of prostate cancer on MRI. • Our code is freely available at https://github.com/pimed//ProsRegNet. Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, interpretation of MRI suffers from high inter-observer variability across radiologists, thereby contributing to missed clinically significant cancers, overdiagnosed low-risk cancers, and frequent false positives. Interpretation of MRI could be greatly improved by providing radiologists with an answer key that clearly shows cancer locations on MRI. Registration of histopathology images from patients who had radical prostatectomy to pre-operative MRI allows such mapping of ground truth cancer labels onto MRI. However, traditional MRI-histopathology registration approaches are computationally expensive and require careful choices of the cost function and registration hyperparameters. This paper presents ProsRegNet, a deep learning-based pipeline to accelerate and simplify MRI-histopathology image registration in prostate cancer. Our pipeline consists of image preprocessing, estimation of affine and deformable transformations by deep neural networks, and mapping cancer labels from histopathology images onto MRI using estimated transformations. We trained our neural network using MR and histopathology images of 99 patients from our internal cohort (Cohort 1) and evaluated its performance using 53 patients from three different cohorts (an additional 12 from Cohort 1 and 41 from two public cohorts). Results show that our deep learning pipeline has achieved more accurate registration results and is at least 20 times faster than a state-of-the-art registration algorithm. This important advance will provide radiologists with highly accurate prostate MRI answer keys, thereby facilitating improvements in the detection of prostate cancer on MRI. Our code is freely available at https://github.com/pimed//ProsRegNet. Image, graphical abstract [ABSTRACT FROM AUTHOR]

Objectives: The PRECISION trial provides level 1 evidence supporting prebiopsy multiparametric magnetic resonance imaging (mpMRI) followed by targeted biopsy only when mpMRI is abnormal [1]. This approach reduced over-detection of low-grade cancer while increasing detection of clinically significant cancer (CSC). Still, important questions remain regarding the reproducibility of these findings outside of a clinical trial and quantifying missed CSC diagnoses using this approach. To address these issues, we retrospectively applied the PRECISION strategy in men who each underwent prebiopsy mpMRI followed by systematic and targeted biopsy.Methods and Materials: Clinical, imaging, and pathology data were prospectively collected from 358 biopsy naïve men and 202 men with previous negative biopsies. To apply the PRECISION approach, a retrospective analysis was done comparing the cancer yield from 2 diagnostic strategies: (1) mpMRI followed by targeted biopsy alone for men with Prostate Imaging Reporting and Data System ≥ 3 lesions and (2) systematic biopsy alone for all men. Primary outcomes were biopsies avoided and the proportion of CSC cancer (Grade Group 2-5) and non-CSC (Grade Group 1).Results: In biopsy naïve patients, the mpMRI diagnostic strategy would have avoided 19% of biopsies while detecting 2.5% more CSC (P= 0.480) and 12% less non-CSC (P< 0.001). Thirteen percent (n= 9) of men with normal mpMRI had CSC on systematic biopsy. For previous negative biopsy patients, the mpMRI diagnostic strategy avoided 21% of biopsies, while detecting 1.5% more CSC (P= 0.737) and 13% less non-CSC (P< 0.001). Seven percent (n= 3) of men with normal mpMRI had CSC on systematic biopsy.Conclusions: Our results provide external validation of the PRECISION finding that mpMRI followed by targeted biopsy of suspicious lesions reduces biopsies and over-diagnosis of low-grade cancer. Unlike PRECISION, we did not find increased diagnosis of CSC. This was true in both biopsy naïve and previously negative biopsy cohorts. We have incorporated this information into shared decision making, which has led some men to choose to avoid biopsy. However, we continue to recommend targeted and systematic biopsy in men with abnormal MRI. [ABSTRACT FROM AUTHOR]