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PURPOSE: Active surveillance for patients with low and intermediate risk prostate cancers is becoming a more utilized option in recent years. However, the use of magnetic resonance imaging and imaging-targeted biopsy for monitoring grade progression has been poorly studied in this population. We aim to define the utility of magnetic resonance imaging-targeted biopsy and systematic biopsy in an active surveillance population. MATERIALS AND METHODS: Between July 2007 and January 2020, patients with diagnosed prostate cancer who elected active surveillance were monitored with prostate magnetic resonance imaging, imaging-targeted biopsy and standard systematic biopsy. Patients were eligible for surveillance if diagnosed with any volume Gleason grade 1 disease and select Gleason grade 2 disease. Grade progression (Gleason grade 1 to ≥2 disease and Gleason grade 2 to ≥3 disease) for each biopsy modality was measured at 2 years, 4 years and 6+ years. RESULTS: In total, 369 patients had both magnetic resonance imaging-targeted and systematic biopsy and were surveilled for at least 1 year. At 2 years, systematic biopsy, magnetic resonance imaging-targeted biopsy and combined biopsy (systematic+imaging-targeted) detected grade progression in 44 patients (15.9%), 73 patients (26.4%) and 90 patients (32.5%), respectively. Magnetic resonance imaging-targeted biopsy detected more cancer grade progression compared to systematic biopsy in both the low and intermediate risk populations (p

BACKGROUND: Radiologists currently subjectively examine multi-parametric magnetic resonance imaging (MP-MRI) to determine prostate tumor aggressiveness using the Prostate Imaging Reporting and Data System scoring system (PI-RADS). Recent studies showed that modified signal to clutter ratio (SCR), tumor volume, and eccentricity (elongation or roundness) of prostate tumors correlated with Gleason score (GS). No previous studies have combined the prostate tumor’s shape, SCR, tumor volume, in order to predict potential tumor aggressiveness and GS. METHODS: MP-MRI (T1, T2, diffusion, dynamic contrast-enhanced images) were obtained, resized, translated, and stitched to form spatially registered multi-parametric cubes. Multi-parametric signatures that characterize prostate tumors were inserted into a target detection algorithm [adaptive cosine estimator (ACE)]. Pixel-based blobbing, and labeling were applied to the threshold ACE images. Eccentricity calculation used moments of inertia from the blobs. Tumor volume was computed by counting pixels within multi parametric MRI blobs and tumor outlines based on pathologist assessment of whole mount histology. Pathology assessment of GS was performed on whole mount prostatectomy. The covariance matrix and mean of normal tissue background was computed from normal prostate. Using signatures and normal tissue statistics, the z-score, noise corrected SCR [principal component (PC), modified regularization] from each patient was computed. Eccentricity, tumor volume, and SCR were fitted to GS. Analysis of variance assesses the relationship among the variables. RESULTS: A multivariate analysis generated correlation coefficient (0.60 to 0.784) and P value (0.00741 to

Near-infrared photoimmunotherapy (NIR-PIT) is a novel molecularly-targeted therapy that selectively kills cancer cells by systemically injecting an antibody-photoabsorber conjugate (APC) that binds to cancer cells, followed by the application of NIR light that drives photochemical transformations of the APC. APCs are synthesized by selecting a monoclonal antibody that binds to a receptor on a cancer cell and conjugating it to IRDye700DX silica-phthalocyanine dye. Approximately 24 h after APC administration, NIR light is delivered to the tumor, resulting in nearly-immediate necrotic cell death of cancer cells while causing no harm to normal tissues. In addition, NIR-PIT induces a strong immunologic effect, activating anti-cancer immunity that can be further boosted when combined with either immune checkpoint inhibitors or immune suppressive cell-targeted (e.g., regulatory T cells) NIR-PIT. Currently, a global phase III study of NIR-PIT in recurrent head and neck squamous cell carcinoma is ongoing. The first APC and NIR laser systems were approved for clinical use in September 2020 in Japan. In the near future, the clinical applications of NIR-PIT will expand to other cancers, including urologic cancers. In this review, we provide an overview of NIR-PIT and its possible applications in urologic cancers.

PurposeThe aim of this work is to assess the feasibility of novel targeted methods for fresh tissue procurement that facilitate accurate identification of lesion location and tissue sample extraction in an excised prostate gland, while minimizing the tissue deformation or damage.Methods and ResultsProposed fresh tissue procurement methods are based on utilizing a custom 3D printed patient-specific prostate mold. In the semi-freehand method, the access cut is guided by a set of markers on the bottom of the mold that define the boundary of a lesion in two orthogonal directions. The procurement site is identified by palpation. In the guided cut with-locator card method the procurement sites are identified by a cutout in the locator cards inserted into the access cuts. The two approaches were utilized to procure >50 samples. In the biopsy needle method guiding channels were designed into the prostate mold to procure fresh tissue. This method was used 4 times. Finally, a mixed-reality biopsy needle method was developed for phantom testing.ConclusionsPreliminary results of fresh tissue procurement utilizing novel methods showed improve precision of obtaining cancerous fresh tissue for molecular and genomic cancer research compared to current methods which rely on surgical estimation of lesion location.

Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e.g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI). However, due to possible data corruption and different imaging protocols, the availability of images for each domain could vary amongst multiple data sources in practice, which makes it challenging to build a universal model with a varied set of input data. To tackle this problem, we propose a general approach to complete the random missing domain(s) data in real applications. Specifically, we develop a novel multi-domain image completion method that utilizes a generative adversarial network (GAN) with a representational disentanglement scheme to extract shared skeleton encoding and separate flesh encoding across multiple domains. We further illustrate that the learned representation in multi-domain image completion could be leveraged for high-level tasks, e.g., segmentation, by introducing a unified framework consisting of image completion and segmentation with a shared content encoder. The experiments demonstrate consistent performance improvement on three datasets for brain tumor segmentation, prostate segmentation, and facial expression image completion respectively.

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

A better understanding of temporal relationships between chest CT and labs may provide a reference for disease severity over the disease course. Generalized curves of lung opacity volume and density over time can be used as standardized references from well before symptoms develop to over a month after recovery, when residual lung opacities remain. 739 patients with COVID-19 underwent CT and RT-PCR in an outbreak setting between January 21st and April 12th, 2020. 29 of 739 patients had serial exams (121 CTs and 279 laboratory measurements) over 50 ± 16 days, with an average of 4.2 sequential CTs each. Sequential volumes of total lung, overall opacity and opacity subtypes (ground glass opacity [GGO] and consolidation) were extracted using deep learning and manual segmentation. Generalized temporal curves of CT and laboratory measurements were correlated. Lung opacities appeared 3.4 ± 2.2 days prior to symptom onset. Opacity peaked 1 day after symptom onset. GGO onset was earlier and resolved later than consolidation. Lactate dehydrogenase, and C-reactive protein peaked earlier than procalcitonin and leukopenia. The temporal relationships of quantitative CT features and clinical labs have distinctive patterns and peaks in relation to symptom onset, which may inform early clinical course in patients with mild COVID-19 pneumonia, or may shed light upon chronic lung effects or mechanisms of medical countermeasures in clinical trials.

Objective To demonstrate enabling multi-institutional training without centralizing or sharing the underlying physical data via federated learning (FL). Materials and Methods Deep 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. Results We 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. Discussion The power of FL was successfully demonstrated across 3 academic institutions while avoiding the privacy risk associated with the transfer and pooling of patient data. Conclusion Federated learning is an effective methodology that merits further study to enable accelerated development of models across institutions, enabling greater generalizability in clinical use.

390 Background: PCa has inherited risk factors including high genetic risk variants such as BRCA1/2, HOXB13, and DNA mismatch repair genes. mpMRI has been shown to be effective for detection and staging of localized PCa. This study follows participants (prts), born biologically male, without a diagnosis of PCa with known germline pathogenic or likely pathogenic variants (PV) in BRCA1/2, MLH1, MSH2, MSH6, PMS2, EPCAM, HOXB13, ATM, NBN, TP53, CHEK2, PALB2, RAD51C/D, BRIP1, or FANCA-FANCM (NCT03805919). Methods: Up to 500 eligible prts 30-75 years old (yo) with a documented germline PV will enroll. Prts undergo biennial clinical exam and mpMRI, and annual PSA monitoring and are followed at 12-month intervals to determine PSA, prostate cancer diagnosis, and/or disease/survival status until death. Indication for prostate biopsy includes clinical or imaging findings. Biopsy specimens undergo molecular analyses. Results: To date, 175 prts have been enrolled: 169 (97%) White, 3 Hispanic (2%), 1 African American (1%), 1 Asian (1%), and 1 biethnic (1%). Median age is 47 yo. The most common monoallelic PV are: 48.6% BRCA2, 25.1% BRCA1, 6.3% CHEK2 and 5.7% MSH2. PVs in ATM, PALB2, HOXB13, PMS2, MLH1, MSH6, BRIP1, EPCAM and RAD51D are ≤4%. One subject carries three distinct PVs ( BRCA2, CHEK2, BRIP1). Indication for biopsy was found in 26.3% of prts with 22/46 (47.8%) with a PIRADS 4 lesion, 6/46 (13.0%) PIRADS 3 lesion, 12/46 (26.1%) elevated PSA (median=2.8 ng/mL) or 6/46 (13.0%) due to clinical discretion. Adenocarcinoma was diagnosed on 13/39 (33.3%) biopsies with median age at diagnosis=59 yo. 9/13 (69%) prts had a PSA

Purpose: For high-risk prostate cancer, standard treatment options include radical prostatectomy (RP) or radiotherapy plus androgen deprivation therapy (ADT). Despite definitive therapy, many patients will have disease recurrence. Imaging has the potential to better define characteristics of response and resistance. In this study, we evaluated prostate multiparametric MRI (mpMRI) before and after neoadjuvant enzalutamide plus ADT. Patients and Methods: Men with localized intermediate- or high-risk prostate cancer underwent a baseline mpMRI and mpMRI-targeted biopsy followed by a second mpMRI after 6 months of enzalutamide and ADT prior to RP. Specimens were sectioned in the same plane as mpMRI using patient-specific 3D-printed molds to permit mpMRI-targeted biopsies to be compared with the same lesion from the RP. Specimens were analyzed for imaging and histologic correlates of response. Results: Of 39 patients enrolled, 36 completed imaging and RP. Most patients (92%) had high-risk disease. Fifty-eight lesions were detected on baseline mpMRI, of which 40 (69%) remained measurable at 6-month follow-up imaging. Fifty-five of 59 lesions (93%) demonstrated >50% volume reduction on posttreatment mpMRI. Three of 59 lesions (5%) demonstrated growth in size at follow-up imaging, with two lesions increasing more than 3-fold in volume. On whole-mount pathology, 15 patients demonstrated minimal residual disease (MRD) of Conclusions: Low relative lesion volume at baseline mpMRI was predictive of pathologic response. A subset of patients had limited response. Selection of patients based on these metrics may improve outcomes in high-risk disease.

Abstract Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent high specificities, at a range of disease prevalences. Since all current artificial intelligence / computer-aided detection systems for prostate cancer detection are experimental, multiple developmental efforts are still needed to bring the vision to fruition. Initial work needs to focus on developing systems as diagnostic supporting aids so their results can be integrated into the radiologists’ workflow including gland and target outlining tasks for fusion biopsies. Developing AI systems as clinical decision-making tools will require greater efforts. The latter encompass larger multicentric, multivendor datasets where the different needs of patients stratified by diagnostic settings, disease prevalence, patient preference, and clinical setting are considered. AI-based, robust, standard operating procedures will increase the confidence of patients and payers, thus enabling the wider adoption of the MRI-directed approach for prostate cancer diagnosis. Key Points • AI systems need to ensure that the benefits of biopsy avoidance are delivered with consistent high specificities, at a range of disease prevalence. • Initial work has focused on developing systems as diagnostic supporting aids for outlining tasks, so they can be integrated into the radiologists’ workflow to support MRI-directed biopsies. • Decision support tools require a larger body of work including multicentric, multivendor studies where the clinical needs, disease prevalence, patient preferences, and clinical setting are additionally defined.

Background Prostate tumor volume correlates with critical components of cancer staging such as Gleason score (GS) grade, predicted disease progression, and metastasis. Therefore, non-invasive tumor volume measurement may elevate clinical management. Radiology assessments of multi-parametric MRI (MP-MRI) commonly visually examine individual images to determine possible tumor presence. This study combines registered MP-MRI into a single image that display normal tissue and possible lesions. This study tests and exploits the vector nature of spatially registered MP-MRI by using supervised target detection algorithms (STDA) and color display and psychovisual analysis (CIELAB) to non-invasively estimate prostate tumor volume. Methods MRI, including T1, T2, diffusion [apparent diffusion coefficient (ADC)], dynamic contrast enhanced (DCE) images, were resampled, rescaled, translated, and stitched to form spatially registered Multi-parametric cubes. The multi-parametric or multi-spectral signatures (7-component or T1, T2, ADC, etc.) that characterize the prostate tumors were inserted into target detection algorithms with conical decision surfaces (adaptive cosine estimator, ACE). Various detection thresholds were applied to discriminate tumor from normal tissue. In addition, tumor appeared as yellow in color images that were created by assigning red to washout from DCE, green to high B from diffusion, and blue to autonomous diffusion image. The yellow voxels in the three-channel hypercube were visually identified by a reader and recording voxels that exceed a threshold in the b* component of the CIELAB algorithm. The number of reported tumor voxels were converted to volume based on spatial resolution and slice separation. The tumor volume measurements were quantitatively validated by comparing the tumor volume computations to the pathologist's assessment of the histology of sectioned whole mount prostates from 26 consecutive patients with prostate adenocarcinoma who underwent radical prostatectomy. This study analyzed tumors exceeding 1 cc and that also took up contrast material (18 patients). Results High correlation coefficients for tumor volume measurements using supervised target detection and color analysis vs. histology from wholemount prostatectomy were computed (R=0.83 and 0.91, respectively). A linear fit for tumor volume measurements using for supervised target detection and color analysis vs. tumor measurements from radical prostatectomy (after correcting for shrinkage from the radical prostatectomy) results in a slope of 1.02 and 3.02, respectively. A polynomial fit for the color analysis to the histology found (R=0.95). Voxels exceeding a threshold in the b* part of the CIELAB algorithm yielded correlation coefficients (0.71, 0.80) offsets (0.01 cc, -0.63 cc) and slopes (1.99, 0.89) against the wholemount prostatectomy and color analysis, respectively. Conclusions Supervised target detection and color display and analysis applied to registered MP-MRI non-invasively estimates prostate tumor volumes >1 cc and displaying angiogenesis.

Objectives The early infection dynamics of patients with SARS-CoV-2 are not well understood. We aimed to investigate and characterize associations between clinical, laboratory, and imaging features of asymptomatic and pre-symptomatic patients with SARS-CoV-2. Methods Seventy-four patients with RT-PCR-proven SARS-CoV-2 infection were asymptomatic at presentation. All were retrospectively identified from 825 patients with chest CT scans and positive RT-PCR following exposure or travel risks in outbreak settings in Japan and China. CTs were obtained for every patient within a day of admission and were reviewed for infiltrate subtypes and percent with assistance from a deep learning tool. Correlations of clinical, laboratory, and imaging features were analyzed and comparisons were performed using univariate and multivariate logistic regression. Results Forty-eight of 74 (65%) initially asymptomatic patients had CT infiltrates that pre-dated symptom onset by 3.8 days. The most common CT infiltrates were ground glass opacities (45/48; 94%) and consolidation (22/48; 46%). Patient body temperature (p < 0.01), CRP (p < 0.01), and KL-6 (p = 0.02) were associated with the presence of CT infiltrates. Infiltrate volume (p = 0.01), percent lung involvement (p = 0.01), and consolidation (p = 0.043) were associated with subsequent development of symptoms. Conclusions COVID-19 CT infiltrates pre-dated symptoms in two-thirds of patients. Body temperature elevation and laboratory evaluations may identify asymptomatic patients with SARS-CoV-2 CT infiltrates at presentation, and the characteristics of CT infiltrates could help identify asymptomatic SARS-CoV-2 patients who subsequently develop symptoms. The role of chest CT in COVID-19 may be illuminated by a better understanding of CT infiltrates in patients with early disease or SARS-CoV-2 exposure. Key Points • Forty-eight of 74 (65%) pre-selected asymptomatic patients with SARS-CoV-2 had abnormal chest CT findings. • CT infiltrates pre-dated symptom onset by 3.8 days (range 1–5). • KL-6, CRP, and elevated body temperature identified patients with CT infiltrates. Higher infiltrate volume, percent lung involvement, and pulmonary consolidation identified patients who developed symptoms.

Multiparametric magnetic resonance imaging (mpMRI) has fundamentally changed how intraprostatic lesions are visualized, serving as a highly sensitive means for detecting clinically significant prostate cancer (csPCa) via image-targeted biopsy. However, limitations associated with mpMRI have led to the development of new imaging technologies with the goal of better characterizing intraprostatic disease burden to more accurately guide treatment planning and surveillance for prostate cancer focal therapy. Herein, we review several novel imaging modalities with an emphasis on clinical data reported within the past two years.7T MRI, artificial intelligence applied to mpMRI, positron emission tomography combined with either computerized tomography or MRI, contrast-enhanced ultrasound, and micro-ultrasound are novel imaging modalities with the potential to further improve intraprostatic lesion localization for applications in focal therapy for prostate cancer. Many of these technologies have demonstrated equivalent or favorable diagnostic accuracy compared to contemporary mpMRI for identifying csPCa and some have even shown improved capabilities to define lesion borders, to provide volumetric estimates of lesions, and to assess the adequacy of focal ablation of planned treatment zones.Novel imaging modalities with capabilities to better characterize intraprostatic lesions have the potential to improve accuracy in treatment planning, real-time assessment of the ablation zone, and posttreatment surveillance; however, many of these technologies require further validation to determine their clinical utility.

PURPOSE: Neoadjuvant intense androgen deprivation therapy (iADT) can exert a wide range of histologic responses, which in turn are reflected in the final prostatectomy specimen. Accurate identification and measurement of residual tumor volumes are critical for tracking and stratifying patient outcomes. MATERIALS AND METHODS: The goal of this current study was to evaluate the ability of antibodies against prostate-specific membrane antigen (PSMA) to specifically detect residual tumor in a cohort of 35 patients treated with iADT plus enzalutamide for six months prior to radical prostatectomy. RESULTS: Residual carcinoma was detected in 31 patients, and PSMA reacted positively with tumor in all cases. PSMA staining was 96% sensitive for tumor, with approximately 82% of benign regions showing no reactivity. By contrast, PSMA positively reacted with 72% of benign regions in a control cohort of 37 untreated cases, resulting in 28% specificity for tumor. PSMA further identified highly dedifferentiated prostate carcinomas including tumors with evidence of neuroendocrine differentiation. CONCLUSIONS: We propose that anti-PSMA immunostaining be a standardized marker for identifying residual cancer in the setting of neoadjuvant intense androgen deprivation therapy.

Prostate cancer (PCa) is the most common cancer type in males in the Western World. MRI has an established role in diagnosis of PCa through guiding biopsies. Due to multistep complex nature of the MRI-guided PCa diagnosis pathway, diagnostic performance has a big variation. Developing artificial intelligence (AI) models using machine learning, particularly deep learning, has an expanding role in radiology. Specifically, for prostate MRI, several AI approaches have been defined in the literature for prostate segmentation, lesion detection and classification with the aim of improving diagnostic performance and interobserver agreement. In this review article, we summarize the use of radiology applications of AI in prostate MRI.

OBJECTIVE: To evaluate the efficacy of combined MRI-targeted plus systematic 12-core biopsy (Cbx) to aid in the selection of patients for active surveillance (AS). METHODS: From July 2007 to January 2020, patients with Gleason Grade Group (GG) 1 or GG 2 prostate cancer were referred to our center for AS consideration. All patients underwent an MRI and confirmatory combined MRI-targeted plus systematic biopsy (Cbx), and AS outcomes based on Cbx results were compared. Cox regression was used to identify predictors of AS failure, defined as progression to ≥ GG3 disease on follow-up biopsies. RESULTS: Of 579 patients referred for AS, 79.3% (459/579) and 20.7% (120/579) had an initial diagnosis of GG1 and GG2 disease, respectively. Overall, 43.2% of patients (250/579) were upgraded on confirmatory Cbx, with 19.2% (111/579) upgraded to ≥ GG3. For the 226 patients followed on AS, 32.7% (74/226) had benign, 45.6% (103/226) had GG1, and 21.7% (49/226) had GG2 results on confirmatory Cbx. In total, 28.8% (65/226) of patients eventually progressed to ≥ GG3, with a median time to AS failure of 89 months. The median time from confirmatory Cbx to AS failure for the negative, GG1, and GG2 groups were 97, 97, and 32 months, respectively (p < .001). On multivariable regression, only age (hazard ratio 1.06 [1.02–1.11], p < .005) and GG on confirmatory Cbx (hazard ratio 2.75 [1.78–4.26], p < .005) remained as positive predictors of AS failure. CONCLUSION: The confirmatory combined MRI-targeted plus systematic biopsy provides useful information for the risk stratification of patients at the time of AS enrollment.

OBJECTIVE. The purpose of this study was to assess the utility of PET with (2S)-2-[[(1S)-1-carboxy-5-[(6-(18F)fluoranylpyridine-3-carbonyl)amino]pentyl]carbamoylamino]pentanedioic acid (18F-DCFPyL)...

Background Prostate cancer recurrence is found in up to 40% of men with prior definitive (total prostatectomy or whole-prostate radiation) treatment. Prostate-specific membrane antigen PET agents such as 2-(3-{1-carboxy-5-[(6-[18F]fluoro-pyridine 3-carbonyl)-amino]-pentyl}-ureido)-pentanedioic acid (18F-DCFPyL) may improve detection of recurrence compared with multiparametric MRI; however, histopathologic validation is lacking. Purpose To determine the sensitivity, specificity, and positive predictive value (PPV) of 18F-DCFPyL PET/CT based on histologic analysis and to compare with pelvic multiparametric MRI in men with biochemically recurrent prostate cancer. Materials and Methods Men were prospectively recruited after prostatectomy and/or radiation therapy with rising prostate-specific antigen level (median, 2.27 ng/mL; range, 0.2-27.45 ng/mL) and a negative result at conventional imaging (bone scan and/or CT). Participants underwent 18F-DCFPyL PET/CT imaging and 3.0-T pelvic multiparametric MRI. Statistical analysis included Wald and modified χ2 tests. Results A total of 323 lesions were visualized in 77 men by using 18F-DCFPyL or multiparametric MRI, with imaging detection concordance of 25% (82 of 323) when including all lesions in the MRI field of view and 53% (52 of 99) when only assessing prostate bed lesions. 18F-DCFPyL depicted more pelvic lymph nodes than did MRI (128 vs 23 nodes). Histologic validation was obtained in 80 locations with sensitivity, specificity, and PPV of 69% (25 of 36; 95% confidence interval [CI]: 51%, 88%), 91% (40 of 44; 95% CI: 74%, 98%), and 86% (25 of 29; 95% CI: 73%, 97%) for 18F-DCFPyL and 69% (24 of 35; 95% CI: 50%, 86%), 74% (31 of 42; 95% CI: 42%, 89%), and 69% (24 of 35; 95% CI: 50%, 88%) for multiparametric MRI (P = .95, P = .14, and P = .07, respectively). In the prostate bed, sensitivity, specificity, and PPV were 57% (13 of 23; 95% CI: 32%, 81%), 86% (18 of 21; 95% CI: 73%, 100%), and 81% (13 of 16; 95% CI: 59%, 100%) for 18F-DCFPyL and 83% (19 of 23; 95% CI: 59%, 100%), 52% (11 of 21; 95% CI: 29%, 74%), and 66% (19 of 29; 95% CI: 44%, 86%) for multiparametric MRI (P = .19, P = .02, and P = .17, respectively). The addition of 18F-DCFPyL to multiparametric MRI improved PPV by 38% overall (P = .02) and by 30% (P = .09) in the prostate bed. Conclusion Findings with 2-(3-{1-carboxy-5-[(6-[18F]fluoro-pyridine 3-carbonyl)-amino]-pentyl}-ureido)-pentanedioic acid (18F-DCFPyL) were histologically validated and demonstrated high specificity and positive predictive value. In the pelvis, 18F-DCFPyL depicted more lymph nodes and improved positive predictive value and specificity when added to multiparametric MRI. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Zukotynski and Rowe in this issue.

Prostate magnetic resonance (MR) imaging is a widely used imaging technique to detect intraprostatic lesions and guide prostate biopsies, with continuous technical advances for better accuracy in prostate cancer diagnosis. Current evaluation of prostate multiparametric MR imaging mainly depends on qualitative evaluation, which is prone to inter-reader variation. Recent advances in prostate MR imaging, such as quantitative T2 mapping and abbreviated MR imaging protocols (eg, biparametric MR imaging), are designed to simplify prostate MR imaging acquisition and interpretation.

Preparedness for the ongoing coronavirus disease 2019 (COVID-19) and its spread in Italy called for setting up of adequately equipped and dedicated health facilities to manage sick patients while protecting healthcare workers, uninfected patients, and the community. In our country, in a short time span, the demand for critical care beds exceeded supply. A new sequestered hospital completely dedicated to intensive care (IC) for isolated COVID-19 patients needed to be designed, constructed, and deployed. Along with this new initiative, the new concept of "Pandemic Radiology Unit" was implemented as a practical solution to the emerging crisis, born out of a critical and urgent acute need. The present article describes logistics, planning, and practical design issues for such a pandemic radiology and critical care unit (e.g., space, infection control, safety of healthcare workers, etc.) adopted in the IC Hospital Unit for the care and management of COVID-19 patients.

ecent advances in deep learning for medical image segmentation demonstrate expert-level accuracy.ecent advances in deep learning for medical image segmentation demonstrate expert-level accuracy.R However, application of these models in clinically realistic environments can result in poor generalization and decreased accuracy, mainly due to the domain shift across different hospitals, scanner vendors, imaging protocols, and patient populations etc. Common transfer learning and domain adaptation techniques are proposed to address this bottleneck. However, these solutions require data (and annotations) from the target domain to retrain the model, and is therefore restrictive in practice for widespread model deployment. Ideally, we wish to have a trained (locked) model that can work uniformly well across unseen domains without further training. In this paper, we propose a deep stacked transformation approach for domain generalization. Specifically, a series of n stacked transformations are applied to each image during network training. The underlying assumption is that the “expected” domain shift for a specific medical imaging modality could be simulated by applying extensive data augmentation on a single source domain, and consequently, a deep model trained on the augmented “big” data (BigAug) could generalize well on unseen domains. We exploit four surprisingly effective, but previously understudied, image-based characteristics for data augmentation to overcome the domain generalization problem. We train and evaluate the BigAug model (with n = 9 transformations) on three different 3D segmentation tasks (prostate gland, left atrial, left ventricle) covering two medical imaging modalities (MRI and ultrasound) involving eight publicly available challenge datasets. The results show that when training on relatively small dataset (n=10~32 volumes, depending on the size of the available datasets) from a single source domain: (i) BigAug models degrade an average of 11% (Dice score change) from source to unseen domain, substantially better than conventional augmentation (degrading 39%) and CycleGAN-based domain adaptation method (degrading 25%), (ii) BigAug is better than “shallower” stacked transforms (i.e. those with fewer transforms) on unseen domains and demonstrates modest improvement to conventional augmentation on the source domain, (iii) after training with BigAug on one source domain, performance on an unseen domain is similar to training a model from scratch on that domain when using the same number of training samples. When training on large datasets (n=465 volumes) with BigAug, (iv) application to unseen domains reaches the performance of state-of-the-art fully supervised models that are trained and tested on their source domains. These findings establish a strong benchmark for the study of domain generalization in medical imaging, and can be generalized to the design of highly robust deep segmentation models for clinical deployment.

Introduction Prostate cancer has traditionally been diagnosed by an elevation in PSA or abnormal exam leading to a systematic transrectal ultrasound (TRUS)-guided biopsy. This diagnostic pathway underdiagnoses clinically significant disease while over diagnosing clinically insignificant disease. In this review, we aim to provide an overview of the recent literature regarding the role of multiparametric MRI (mpMRI) in the management of prostate cancer. Materials and Methods A thorough literature review was performed using PubMed to identify articles discussing use of mpMRI of the prostate in management of prostate cancer. Conclusion The incorporation of mpMRI of the prostate addresses the shortcomings of the prostate biopsy while providing several other advantages. mpMRI allows some men to avoid an immediate biopsy and permits visualization of areas likely to harbor clinically significant cancer prior to biopsy to facilitate use of MR-targeted prostate biopsies. This allows for reduction in diagnosis of clinically insignificant disease as well as improved detection and better characterization of higher risk cancers, as well as the improved selection of patients for active surveillance. In addition, mpMRI can be used for selection and monitoring of patients for active surveillance and treatment planning during surgery and focal therapy. United States Department of Health & Human Services National Institutes of Health (NIH) - USA ; Doris Duke Charitable Foundation (DDCF) ; American Association for Dental Research ; Colgate-Palmolive Company ; Roche Holding ; Genentech

BACKGROUND: The Prostate Imaging Reporting and Data System (PI-RADS) provides guidelines for risk stratification of lesions detected on multiparametric MRI (mpMRI) of the prostate but suffers from high intra/interreader variability. PURPOSE: To develop an artificial intelligence (AI) solution for PI-RADS classification and compare its performance with an expert radiologist using targeted biopsy results. STUDY TYPE: Retrospective study including data from our institution and the publicly available ProstateX dataset. POPULATION: In all, 687 patients who underwent mpMRI of the prostate and had one or more detectable lesions (PI-RADS score >1) according to PI-RADSv2. FIELD STRENGTH/SEQUENCE: T(2)-weighted, diffusion-weighted imaging (DWI; five evenly spaced b values between b = 0–750 s/mm(2)) for apparent diffusion coefficient (ADC) mapping, high b-value DWI (b = 1500 or 2000 s/mm(2)), and dynamic contrast-enhanced T(1)-weighted series were obtained at 3.0T. ASSESSMENT: PI-RADS lesions were segmented by a radiologist. Bounding boxes around the T(2)/ADC/high-b value segmentations were stacked and saved as JPEGs. These images were used to train a convolutional neural network (CNN). The PI-RADS scores obtained by the CNN were compared with radiologist scores. The cancer detection rate was measured from a subset of patients who underwent biopsy. STATISTICAL TESTS: Agreement between the AI and the radiologist-driven PI-RADS scores was assessed using a kappa score, and differences between categorical variables were assessed with a Wald test. RESULTS: For the 1034 detection lesions, the kappa score for the AI system vs. the expert radiologist was moderate, at 0.40. However, there was no significant difference in the rates of detection of clinically significant cancer for any PI-RADS score in 86 patients undergoing targeted biopsy (P = 0.4–0.6). DATA CONCLUSION: We developed an AI system for assignment of a PI-RADS score on segmented lesions on mpMRI with moderate agreement with an expert radiologist and a similar ability to detect clinically significant cancer. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2204

OBJECTIVE. Multiparametric MRI (mpMRI) has become the main imaging modality for the detection, localization, and local staging of prostate cancer over the past decade. For radiologists to achieve consistent and reproducible reporting of prostate mpMRI, a comprehensive evaluation of the gland including detailed knowledge of anatomy, pathology, and clinical data is required. This article familiarizes radiologists with common pitfalls and conditions that affect mpMRI performance during readouts. CONCLUSION. Consistent, accurate, and reproducible reporting of prostate mpMRI is vital. Additionally, radiologists should be aware of common diagnostic pitfalls that can hinder mpMRI performance.

Prostate MRI is reported in clinical practice using the Prostate Imaging and Data Reporting System (PI-RADS). PI-RADS aims to standardize, as much as possible, the acquisition, interpretation, reporting, and ultimately the performance of prostate MRI. PI-RADS relies upon mainly subjective analysis of MR imaging findings, with very few incorporated quantitative features. The shortcomings of PI-RADS are mainly: low-to-moderate interobserver agreement and modest accuracy for detection of clinically significant tumors in the transition zone. The use of a more quantitative analysis of prostate MR imaging findings is therefore of interest. Quantitative MR imaging features including: tumor size and volume, tumor length of capsular contact, tumor apparent diffusion coefficient (ADC) metrics, tumor T1 and T2 relaxation times, tumor shape, and texture analyses have all shown value for improving characterization of observations detected on prostate MRI and for differentiating between tumors by their pathological grade and stage. Quantitative analysis may therefore improve diagnostic accuracy for detection of cancer and could be a noninvasive means to predict patient prognosis and guide management. Since quantitative analysis of prostate MRI is less dependent on an individual users' assessment, it could also improve interobserver agreement. Semi- and fully automated analysis of quantitative (radiomic) MRI features using artificial neural networks represent the next step in quantitative prostate MRI and are now being actively studied. Validation, through high-quality multicenter studies assessing diagnostic accuracy for clinically significant prostate cancer detection, in the domain of quantitative prostate MRI is needed. This article reviews advances in quantitative prostate MRI, highlighting the strengths and limitations of existing and emerging techniques, as well as discussing opportunities and challenges for evaluation of prostate MRI in clinical practice when using quantitative assessment. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Introduction: Prostate magnetic resonance imaging (MRI) is commonly used for localized disease mainly to detect intraprostatic lesions and to guide biopsies. Despite its documented success in clini...

PURPOSE: We update the prior standard operating procedure for magnetic resonance imaging of the prostate, and summarize the available data about the technique and clinical use for the diagnosis and management of prostate cancer. This update includes practical recommendations on the use of magnetic resonance imaging for screening, diagnosis, staging, treatment and surveillance of prostate cancer. MATERIALS AND METHODS: A panel of clinicians from the American Urological Association and Society of Abdominal Radiology with expertise in the diagnosis and management of prostate cancer evaluated the current published literature on the use and technique of magnetic resonance imaging for this disease. When adequate studies were available for analysis, recommendations were made on the basis of data and when adequate studies were not available, recommendations were made on the basis of expert consensus. RESULTS: Prostate magnetic resonance imaging should be performed according to technical specifications and standards, and interpreted according to standard reporting. Data support its use in men with a previous negative biopsy and ongoing concerns about increased risk of prostate cancer. Sufficient data now exist to support the recommendation of magnetic resonance imaging before prostate biopsy in all men who have no history of biopsy. Currently, the evidence is insufficient to recommend magnetic resonance imaging for screening, staging or surveillance of prostate cancer. CONCLUSIONS: Use of prostate magnetic resonance imaging in the risk stratification, diagnosis and treatment pathway of men with prostate cancer is expanding. When quality prostate imaging is obtained, current evidence now supports its use in men at risk of harboring prostate cancer and who have not undergone a previous biopsy, as well as in men with an increasing prostate specific antigen following an initial negative standard prostate biopsy procedure.

AIM: To investigate the effects of cleansing Fleet’s™ enema (FE) on rectal distention and image quality of diffusion-weighted imaging (DWI) in prostate magnetic resonance imaging (MRI). METHODS: This study included 117 prospectively accrued active surveillance patients who underwent prostate MRI both without (prep−) and with bowel preparation consisting of FE (prep+) obtained within 12 months of each other. The anterior–posterior (AP) diameter of the rectum, degree of perceived distention in the rectum and image quality scores were assessed by two independent readers for both (prep− and prep+) scans. DWI distortion was assessed quantitatively using the degree of anatomic mismatches between images obtained at different b values and the T2-weighted MRI. DWI artifact was qualitatively scored based on the presence of blurring, poor signal-to-noise, and artifact lines. The difference in rectal AP diameters between the two methods was tested by the paired Wilcoxon rank test. Stuart Maxell test was used in compar-ing rectal distention, DWI distortion, and artifact. Reader agreement was estimated by kappa statistics. p values < 0.05 were considered statistically significant. RESULTS: Mean rectal AP diameter was significantly larger in prep− compared with prep+ scans (p = 0.002). Subjective scores demonstrated inter-reader variability. For instance, the rectal distention score was significantly lower in prep+ for reader 2 (p < 0.001) whereas it was not significant for reader 1 (p = 0.09). Reader 2 also found significant improvement in DWI distortion (p = 0.02) in prep+ scans. There was no significant difference between prep− and prep+ in DWI distortion and artifacts for reader 1 (p = 0.17 and p = 0.49, respectively), or DWI artifacts for reader 2 (p = 0.55). Kappa scores were moderate for rectal distension, but weak for DWI distortion, and artifacts. CONCLUSION: Bowel preparation with enema prior to prostate MRI may diminish rectal gas but has modest effects on DWI distortion and overall image quality. The value of bowel prep is not conclusively validated in this study.

Background The use of 12-core systematic prostate biopsy is associated with diagnostic inaccuracy that contributes to both overdiagnosis and underdiagnosis of prostate cancer. Biopsies per...

Imaging plays an increasing role in prostate cancer diagnosis and staging. Accurate staging of prostate cancer is required for optimal treatment planning. In detecting extraprostatic cancer and sites of early recurrence, traditional imaging methods (computed tomography, magnetic resonance imaging, radionuclide bone scan) have suboptimal performance. This leaves a gap between known disease recurrence as indicated by rising prostate-specific antigen and the ability to localize the recurrence on imaging. Novel positron emission tomography (PET) agents including radiolabeled choline, fluciclovine (18F-FACBC), and agents targeting prostate-specific membrane antigen are being developed and tested to increase diagnostic performance of non-invasive prostate cancer localization. When combined with CT or MRI, these tracers offer a combination of functional information and anatomic localization that is superior to conventional imaging methods. These PET radiotracers have varying mechanisms and excretion patterns affecting their pharmacokinetics and diagnostic performance, which will be reviewed in this article.