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BackgroundPartial gland ablation (PGA) is a new option for treatment of prostate cancer (PCa). Cryotherapy, an early method of PGA, has had favorable evaluations, but few studies have employed a strict protocol using biopsy endpoints in men with clinically significant prostate cancer (csPCa).Methods143 men with unilateral csPCa were enrolled in a prospective, observational trial of outpatient PGA-cryotherapy. Treatment was a 2-cycle freeze of the affected prostate part. Participants were evaluated with MRI-guided biopsy (MRGB) at baseline and at 6 months and 18 months after treatment. Absence of csPCa upon MRGB was the primary endpoint; quality-of-life at baseline and at 6 months after treatment was assessed by EPIC-CP questionnaires in the domains of urinary and sexual function.ResultsOf the 143 participants, 136 (95%) completed MRGB at 6 months after treatment. In 103/136 (76%), the biopsy revealed no csPCa. Of the 103, 71 subsequently had an 18-month comprehensive biopsy; of the 71 with 18-month biopsies, 46 (65%) were found to have no csPCa. MRI lesions became undetectable in 96/130 (74%); declines in median serum PSA levels (6.9 to 2.5 ng/mL), PSA density (0.15 to 0.07), and prostate volume (42 to 34cc) were observed (all p

PurposeThe appropriate number of systematic biopsy cores to retrieve during magnetic resonance imaging (MRI)-targeted prostate biopsy is not well defined. We aimed to demonstrate a biopsy sampling approach that reduces required core count while maintaining diagnostic performance.Materials and methodsWe collected data from a cohort of 971 men who underwent MRI-ultrasound fusion targeted biopsy for suspected prostate cancer. A regional targeted biopsy (RTB) was evaluated retrospectively; only cores within 2 cm of the margin of a radiologist-defined region of interest were considered part of the RTB. We compared detection rates for clinically significant prostate cancer (csPCa) and cancer upgrading rate on final whole mount pathology after prostatectomy between RTB, combined, MRI-targeted, and systematic biopsy.ResultsA total of 16,459 total cores from 971 men were included in the study data sets, of which 1,535 (9%) contained csPCa. The csPCa detection rates for systematic, MRI-targeted, combined, and RTB were 27.0% (262/971), 38.3% (372/971), 44.8% (435/971), and 44.0% (427/971), respectively. Combined biopsy detected significantly more csPCa than systematic and MRI-targeted biopsy (p

ObjectiveTo demonstrate enabling multi-institutional training without centralizing or sharing the underlying physical data via federated learning (FL).Materials and methodsDeep learning models were trained at each participating institution using local clinical data, and an additional model was trained using FL across all of the institutions.ResultsWe found that the FL model exhibited superior performance and generalizability to the models trained at single institutions, with an overall performance level that was significantly better than that of any of the institutional models alone when evaluated on held-out test sets from each institution and an outside challenge dataset.DiscussionThe power of FL was successfully demonstrated across 3 academic institutions while avoiding the privacy risk associated with the transfer and pooling of patient data.ConclusionFederated learning is an effective methodology that merits further study to enable accelerated development of models across institutions, enabling greater generalizability in clinical use.

PurposeDeveloping 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 methodsWe 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.ResultsOur 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.ConclusionWe 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.

ObjectiveTo detail the development of an electronic report that graphically conveys all relevant information from targeted prostate biopsy.MethodsThe Urology Integrated Diagnostic Report (Uro-IDR) is based on a published framework (RadPath) which enables the compilation of diagnostic data from urology, radiology, and pathology. Each component of the Uro-IDR is generated by the contributing clinician, is assembled in one document, and provides correlation of the 3 inputs at a glance. Upon completion, the Uro-IDR is automatically linked to the electronic medical record as an interactive file and can also be downloaded for offline sharing as a PDF.ResultsAt our institution, 1638 individual Uro-IDRs were generated between June 2016 and April 2019. There were 5715 views of these documents via the EMR. The average turnaround time for the creation of an individual report decreased from nearly 8 days at the time of its launch to 2 days after 6 months of use. The average time for report generation was 22 seconds for the pathologist and 69 seconds for the radiologist. An instructive video is linked to this article.ConclusionThe Uro-IDR has proven to be a feasible, efficient, clinically useful form to concisely transmit key information about targeted prostate biopsy to both clinicians and patients.

ObjectivesTo create reliable predictive metrics of unilateral disease using spatial tracking from a fusion device, thereby improving patient selection for hemi-gland ablation of prostate cancer.Patients and methodsWe identified patients who received magnetic resonance imaging (MRI)/ultrasound-guided biopsy and radical prostatectomy at a single institution between 2011 and 2018. In addition to standard clinical features, we extracted quantitative features related to biopsy core and MRI target locations predictive of tumour unilaterality. Classification and Regression Tree (CART) analysis was used to create a decision tree (DT) for identifying cancer laterality. We evaluated concordance of model-determined laterality with final surgical pathology.ResultsA total of 173 patients were identified with biopsy coordinates and surgical pathology available. Based on CART analysis, in addition to biopsy- and MRI-confirmed disease unilaterality, patients should be further screened for cancer detected within 7 mm of midline in a 40 mL prostate, which equates to the central third of any-sized prostate by radius. The area under the curve for this DT was 0.82. Standard diagnostics and the DT correctly identified disease laterality in 73% and 80% of patients, respectively (P = 0.13). Of the patients identified as unilateral by standard diagnostics, 47% had undetected contralateral disease or were otherwise incorrectly identified. This error rate was reduced to 17% (P = 0.01) with the DT.ConclusionUsing spatial tracking from fusion devices, a DT was more reliable for identifying laterality of prostate cancer compared to standard diagnostics. Patients with cancer detected within the central third of the prostate by radius are poor hemi-gland ablation candidates due to the risk of midline extension of tumour.

PurposeWe assessed focal therapy eligibility in men who underwent multiparametric magnetic resonance imaging and targeted biopsy with correlation to whole mount histology after radical prostatectomy.Materials and methodsSubjects were selected from among the 454 men in whom targeted biopsy proven prostate cancer was derived from regions of interest on multiparametric magnetic resonance imaging from 2010 to 2016. Focal therapy eligibility was limited to a maximum Gleason score of 4 + 3 in regions of interest with or without other foci of low risk prostate cancer (Gleason score 3 + 3 and less than 4 mm). Men who did not meet NCCN® intermediate risk criteria were classified as ineligible for focal therapy. Of the 454 men 64 underwent radical prostatectomy and biopsy findings were compared to final pathology findings.ResultsOf the 454 men with a biopsy proven region of interest 175 (38.5%) were eligible for focal therapy. Fusion biopsy, which combined targeted and template biopsy, had 80.0% sensitivity (12 of 15 cases), 73.5% specificity (36 of 49) and 75.0% accuracy (48 of 64) for focal therapy eligibility. Targeted cores alone yielded 73.3% sensitivity (11 of 15 cases), 47.9% specificity (23 of 48) and 54.7% accuracy (35 of 64). Gleason score and extension across the midline differed in 4 and 9, respectively, of the 13 cases that showed discordant biopsy and whole mount histology.ConclusionsUsing intermediate risk eligibility criteria more than a third of men with a targeted biopsy proven lesion identified on multiparametric magnetic resonance imaging would have been eligible for focal therapy. Eligibility determined by fusion biopsy was concordant with whole mount histology in 75% of cases. Improved selection criteria are needed to reliably determine focal therapy eligibility.

Robots ultrasound (RUS) can be defined as the combination of ultrasound imaging with a robotic system in medical interventions. With their potential for high precision, dexterity, and repeatability, robots are often uniquely suited for ultrasound integration. Although the field is relatively young, it has already generated a multitude of robotic systems for application in dozens of medical procedures. This paper reviews the robotic ultrasound systems that have been developed over the past two decades and describes their potential impact on modern medicine. The RUS projects reviewed include extracorporeal devices, needle guidance systems, and intraoperative systems.