T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning-derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology.

OBJECTIVES: To explore the associations between T1 and T2 magnetic resonance fingerprinting (MRF) measurements and corresponding tissue compartment ratios (TCRs) on whole mount histopathology of prostate cancer (PCa) and prostatitis. MATERIALS AND METHODS: A retrospective, IRB-approved, HIPAA-compliant cohort consisting of 14 PCa patients who underwent 3 T multiparametric MRI along with T1 and T2 MRF maps prior to radical prostatectomy was used. Correspondences between whole mount specimens and MRI and MRF were manually established. Prostatitis, PCa, and normal peripheral zone (PZ) regions of interest (ROIs) on pathology were segmented for TCRs of epithelium, lumen, and stroma using two U-net deep learning models. Corresponding ROIs were mapped to T2-weighted MRI (T2w), apparent diffusion coefficient (ADC), and T1 and T2 MRF maps. Their correlations with TCRs were computed using Pearson's correlation coefficient (R). Statistically significant differences in means were assessed using one-way ANOVA. RESULTS: Statistically significant differences (p < 0.01) in means of TCRs and T1 and T2 MRF were observed between PCa, prostatitis, and normal PZ. A negative correlation was observed between T1 and T2 MRF and epithelium (R = - 0.38, - 0.44, p < 0.05) of PCa. T1 MRF was correlated in opposite directions with stroma of PCa and prostatitis (R = 0.35, - 0.44, p < 0.05). T2 MRF was positively correlated with lumen of PCa and prostatitis (R = 0.57, 0.46, p < 0.01). Mean T2 MRF showed significant differences (p < 0.01) between PCa and prostatitis across both transition zone (TZ) and PZ, while mean T1 MRF was significant (p = 0.02) in TZ. CONCLUSION: Significant associations between MRF (T1 in the TZ and T2 in the PZ) and tissue compartments on corresponding histopathology were observed. KEY POINTS: • Mean T2 MRF measurements and ADC within cancerous regions of interest dropped with increasing ISUP prognostic groups (IPG). • Mean T1 and T2 MRF measurements were significantly different (p < 0.001) across IPGs, prostatitis, and normal peripheral zone (NPZ). • T2 MRF showed stronger correlations in the peripheral zone, while T1 MRF showed stronger correlations in the transition zone with histopathology for prostate cancer.

Diagnostic Yield of Incremental Biopsy Cores and Second Lesion Sampling for In-Gantry MRI-Guided Prostate Biopsy.

BACKGROUND. In-gantry MRI-guided biopsy (MRGB) of the prostate has been shown to be more accurate than other targeted prostate biopsy methods. However, the optimal number of cores to obtain during in-gantry MRGB remains undetermined. OBJECTIVE. The purpose of this study was to assess the diagnostic yield of obtaining an incremental number of cores from the primary lesion and of second lesion sampling during in-gantry MRGB of the prostate. METHODS. This retrospective study included 128 men with 163 prostate lesions who underwent in-gantry MRGB between 2016 and 2019. The men had a total of 163 lesions sampled with two or more cores, 121 lesions sampled with three or more cores, and 52 lesions sampled with four or more cores. A total of 40 men underwent sampling of a second lesion. Upgrade on a given core was defined as a greater International Society of Urological Pathology (ISUP) grade group (GG) relative to the previously obtained cores. Clinically significant prostate cancer (csPCa) was defined as ISUP GG 2 or greater. RESULTS. The frequency of any upgrade was 12.9% (21/163) on core 2 versus 10.7% (13/121) on core 3 (p = .29 relative to core 2) and 1.9% (1/52) on core 4 (p = .03 relative to core 3). The frequency of upgrade to csPCa was 7.4% (12/163) on core 2 versus 4.1% (5/121) on core 3 (p = .13 relative to core 2) and 0% (0/52) on core 4 (p = .07 relative to core 3). The frequency of upgrade on core 2 was higher for anterior lesions (p < .001) and lesions with a higher PI-RADS score (p = .007); the frequency of upgrade on core 3 was higher for apical lesions (p = .01) and lesions with a higher PI-RADS score (p = .01). Sampling of a second lesion resulted in an upgrade in a single patient (2.5%; 1/40); both lesions were PI-RADS category 4 and showed csPCa. CONCLUSION. When performing in-gantry MRGB of the prostate, obtaining three cores from the primary lesion is warranted to optimize csPCa diagnosis. Obtaining a fourth core from the primary lesion or sampling a second lesion has very low yield in upgrading cancer diagnoses. CLINICAL IMPACT. To reduce patient discomfort and procedure times, operators may refrain from obtaining more than three cores or second lesion sampling.

Variability in Prices for Erectile Dysfunction Medications-Are All Pharmacies the Same?

BACKGROUND: Variability in prices of medications is a well-known phenomenon; however, this variability has not been quantified in the realm of erectile dysfunction (ED) medications. ED medications are ideal for this quantification, because they are often not covered by insurances; therefore, the cost is the most direct reflection of price variability among pharmacies as they affect the patients. AIM: To evaluate the variability in cash prices for phosphodiesterase type 5 inhibitors (PDEIs) for ED. We also evaluated whether certain types of pharmacies consistently offer better pricing than others, and whether there was any correlation with demographic factors. METHODS: 331 pharmacies were contacted within a 25-mile radius of our institution to obtain the cash price for 4 commonly used ED medications with prespecified doses. After exclusion, 323 pharmacies were categorized as chain, independent, wholesale, or hospital-associated. Cash prices for the specified medications were evaluated. In addition, we identified demographic and socioeconomic factors to determine if these had an impact on median drug pricing within each zip code. MAIN OUTCOME MEASURE: The main outcome was the cost for patients to fill each prescription. RESULTS: Independent pharmacies provided the lowest cost for 3 of 4 of the PDEIs. The largest price difference for 10 tablets of 100 mg sildenafil between all pharmacies was 38,000%. The median cost difference between independent pharmacies and chain pharmacies for sildenafil was >900%, and >1,100% for independent pharmacies vs hospital-associated pharmacies. Demographic and socioeconomic factors had no impact on the cost. CLINICAL IMPLICATIONS: Our goal is to promote patient counseling among practitioners and to empower patients to shop for the best prices for their medications. STRENGTH AND LIMITATIONS: A strength of the study is the large cohort that was surveyed; however, a weakness is that the large majority of the cohort was comprised of chain pharmacies. Mail pharmacies could not be evaluated as they required a valid prescription before offering prices. CONCLUSION: The drastic differences in cash prices for the PDEIs give us an insight into the variability and cost-inflation of medications in the United States. These patterns hold true for other essential medications as well, and improved transparency will allow patients to make informed decisions when choosing where to purchase their medications. It may also encourage certain pharmacies to provide medications at more affordable prices. Mishra K, Bukavina L, Mahran A, et al. Variability in prices for erectile dysfunction medications-Are all pharmacies the same? J Sex Med 2018;15:1785-1791.

Bladder irrigation after transurethral resection of superficial bladder cancer: a systematic review of the literature.

INTRODUCTION: The vast majority of bladder cancer is non-muscle invasive with transurethral resection (TURBT) as the gold standard for surgical treatment. There is a high recurrence of bladder cancer post surgery, which adds to the frustration in current urologic practice. Current standard of care to further reduce bladder cancer recurrence is instillation of intravesical chemotherapy (ICT), a practice that is not routinely followed. Several studies point to similar effects with normal saline or water irrigation alone. Our objective is to review the current available literature and provide practicing urologist with an alternative to ICT. MATERIALS AND METHODS: A systematic search was performed through December 2017. Peer reviewed studies, which evaluated recurrence free survival (RFS) after bladder irrigation with saline or sterile water (SW) post-TURBT were included. Outcomes were analyzed in three groups: ICT, saline and sterile water. RESULTS: Six studies out of 981, including 1515 patients, were eligible. There was no significant difference between ICT, saline and SW groups regarding to the median RFS at 1 year [ICT: 81%, IQR (77.70, -81.00), SW: 74%, IQR (63.3-74.9), saline: 76.7% IQR (76.0, 77.7), p = 0.21]. While saline irrigation showed the highest median RFS among the groups, there was no statistically significant difference between the three groups [ICT: 70%, IQR (66.25, 73.75), SW: 64.1%, IQR (63.05, 65.15), saline: 73%, IQR (66.85, 74.50), p = 0.49]. Adverse events were more frequent amongst patients in the ICT group in comparison to the saline or water groups. CONCLUSION: Saline and sterile water irrigation provide an alternative to ICT with equivalent recurrence rate and lower incidence of adverse events.

Multi-scale statistical deformation based co-registration of prostate MRI and post-surgical whole mount histopathology.

BACKGROUND: Accurate delineations of regions of interest (ROIs) on multi-parametric magnetic resonance imaging (mpMRI) are crucial for development of automated, machine learning-based prostate cancer (PCa) detection and segmentation models. However, manual ROI delineations are labor-intensive and susceptible to inter-reader variability. Histopathology images from radical prostatectomy (RP) represent the "gold standard" in terms of the delineation of disease extents, for example, PCa, prostatitis, and benign prostatic hyperplasia (BPH). Co-registering digitized histopathology images onto pre-operative mpMRI enables automated mapping of the ground truth disease extents onto mpMRI, thus enabling the development of machine learning tools for PCa detection and risk stratification. Still, MRI-histopathology co-registration is challenging due to various artifacts and large deformation between in vivo MRI and ex vivo whole-mount histopathology images (WMHs). Furthermore, the artifacts on WMHs, such as tissue loss, may introduce unrealistic deformation during co-registration. PURPOSE: This study presents a new registration pipeline, MSERgSDM, a multi-scale feature-based registration (MSERg) with a statistical deformation (SDM) constraint, which aims to improve accuracy of MRI-histopathology co-registration. METHODS: In this study, we collected 85 pairs of MRI and WMHs from 48 patients across three cohorts. Cohort 1 (D1), comprised of a unique set of 3D printed mold data from six patients, facilitated the generation of ground truth deformations between ex vivo WMHs and in vivo MRI. The other two clinically acquired cohorts (D2 and D3) included 42 patients. Affine and nonrigid registrations were employed to minimize the deformation between ex vivo WMH and ex vivo T2-weighted MRI (T2WI) in D1. Subsequently, ground truth deformation between in vivo T2WI and ex vivo WMH was approximated as the deformation between in vivo T2WI and ex vivo T2WI. In D2 and D3, the prostate anatomical annotations, for example, tumor and urethra, were made by a pathologist and a radiologist in collaboration. These annotations included ROI boundary contours and landmark points. Before applying the registration, manual corrections were made for flipping and rotation of WMHs. MSERgSDM comprises two main components: (1) multi-scale representation construction, and (2) SDM construction. For the SDM construction, we collected N = 200 reasonable deformation fields generated using MSERg, verified through visual inspection. Three additional methods, including intensity-based registration, ProsRegNet, and MSERg, were also employed for comparison against MSERgSDM. RESULTS: Our results suggest that MSERgSDM performed comparably to the ground truth (p > 0.05). Additionally, MSERgSDM (ROI Dice ratio = 0.61, landmark distance = 3.26 mm) exhibited significant improvement over MSERg (ROI Dice ratio = 0.59, landmark distance = 3.69 mm) and ProsRegNet (ROI Dice ratio = 0.56, landmark distance = 4.00 mm) in local alignment. CONCLUSIONS: This study presents a novel registration method, MSERgSDM, for mapping ex vivo WMH onto in vivo prostate MRI. Our preliminary results demonstrate that MSERgSDM can serve as a valuable tool to map ground truth disease annotations from histopathology images onto MRI, thereby assisting in the development of machine learning models for PCa detection on MRI.

Novel radiomic analysis on bi-parametric MRI for characterizing differences between MR non-visible and visible clinically significant prostate cancer.

Background: around one third of clinically significant prostate cancer (CsPCa) foci are reported to be MRI non-visible (MRI─). Objective: To quantify the differences between MR visible (MRI+) and MRI─ CsPCa using intra- and peri-lesional radiomic features on bi-parametric MRI (bpMRI). Methods: This retrospective and multi-institutional study comprised 164 patients with pre-biopsy 3T prostate multi-parametric MRI from 2014 to 2017. The MRI─ CsPCa referred to lesions with PI-RADS v2 score < 3 but ISUP grade group > 1. Three experienced radiologists were involved in annotating lesions and PI-RADS assignment. The validation set (Dv) comprised 52 patients from a single institution, the remaining 112 patients were used for training (Dt). 200 radiomic features were extracted from intra-lesional and peri-lesional regions on bpMRI.Logistic regression with least absolute shrinkage and selection operator (LASSO) and 10-fold cross-validation was applied on Dt to identify radiomic features associated with MRI─ and MRI+ CsPCa to generate corresponding risk scores RMRI─ and RMRI+. RbpMRI was further generated by integrating RMRI─ and RMRI+. Statistical significance was determined using the Wilcoxon signed-rank test. Results: Both intra-lesional and peri-lesional bpMRI Haralick and CoLlAGe radiomic features were significantly associated with MRI─ CsPCa (p < 0.05). Intra-lesional ADC Haralick and CoLlAGe radiomic features were significantly different among MRI─ and MRI+ CsPCa (p < 0.05). RbpMRI yielded the highest AUC of 0.82 (95 % CI 0.72-0.91) compared to AUCs of RMRI+ 0.76 (95 % CI 0.63-0.89), and PI-RADS 0.58 (95 % CI 0.50-0.72) on Dv. RbpMRI correctly reclassified 10 out of 14 MRI─ CsPCa on Dv. Conclusion: Our preliminary results demonstrated that both intra-lesional and peri-lesional bpMRI radiomic features were significantly associated with MRI─ CsPCa. These features could assist in CsPCa identification on bpMRI.

Computational pathology improves risk stratification of a multi-gene assay for early stage ER+ breast cancer.

Prognostic markers currently utilized in clinical practice for estrogen receptor-positive (ER+) and lymph node-negative (LN-) invasive breast cancer (IBC) patients include the Nottingham grading system and Oncotype Dx (ODx). However, these biomarkers are not always optimal and remain subject to inter-/intra-observer variability and high cost. In this study, we evaluated the association between computationally derived image features from H&E images and disease-free survival (DFS) in ER+ and LN- IBC. H&E images from a total of n = 321 patients with ER+ and LN- IBC from three cohorts were employed for this study (Training set: D1 (n = 116), Validation sets: D2 (n = 121) and D3 (n = 84)). A total of 343 features relating to nuclear morphology, mitotic activity, and tubule formation were computationally extracted from each slide image. A Cox regression model (IbRiS) was trained to identify significant predictors of DFS and predict a high/low-risk category using D1 and was validated on independent testing sets D2 and D3 as well as within each ODx risk category. IbRiS was significantly prognostic of DFS with a hazard ratio (HR) of 2.33 (95% confidence interval (95% CI) = 1.02-5.32, p = 0.045) on D2 and a HR of 2.94 (95% CI = 1.18-7.35, p = 0.0208) on D3. In addition, IbRiS yielded significant risk stratification within high ODx risk categories (D1 + D2: HR = 10.35, 95% CI = 1.20-89.18, p = 0.0106; D1: p = 0.0238; D2: p = 0.0389), potentially providing more granular risk stratification than offered by ODx alone.

Deep computational image analysis of immune cell niches reveals treatment-specific outcome associations in lung cancer.

The tumor immune composition influences prognosis and treatment sensitivity in lung cancer. The presence of effective adaptive immune responses is associated with increased clinical benefit after immune checkpoint blockers. Conversely, immunotherapy resistance can occur as a consequence of local T-cell exhaustion/dysfunction and upregulation of immunosuppressive signals and regulatory cells. Consequently, merely measuring the amount of tumor-infiltrating lymphocytes (TILs) may not accurately reflect the complexity of tumor-immune interactions and T-cell functional states and may not be valuable as a treatment-specific biomarker. In this work, we investigate an immune-related biomarker (PhenoTIL) and its value in associating with treatment-specific outcomes in non-small cell lung cancer (NSCLC). PhenoTIL is a novel computational pathology approach that uses machine learning to capture spatial interplay and infer functional features of immune cell niches associated with tumor rejection and patient outcomes. PhenoTIL's advantage is the computational characterization of the tumor immune microenvironment extracted from H&E-stained preparations. Association with clinical outcome and major non-small cell lung cancer (NSCLC) histology variants was studied in baseline tumor specimens from 1,774 lung cancer patients treated with immunotherapy and/or chemotherapy, including the clinical trial Checkmate 057 (NCT01673867).

A machine learning model for separating epithelial and stromal regions in oral cavity squamous cell carcinomas using H&E-stained histology images: A multi-center, retrospective study.

OBJECTIVE: Tissue slides from Oral cavity squamous cell carcinoma (OC-SCC), particularly the epithelial regions, hold morphologic features that are both diagnostic and prognostic. Yet, previously developed approaches for automated epithelium segmentation in OC-SCC have not been independently tested in a multi-center setting. In this study, we aimed to investigate the effectiveness and applicability of a convolutional neural network (CNN) model to perform epithelial segmentation using digitized H&E-stained diagnostic slides from OC-SCC patients in a multi-center setting. METHODS: A CNN model was developed to segment the epithelial regions of digitized slides (n = 810), retrospectively collected from five different centers. Deep learning models were trained and validated using well-annotated tissue microarray (TMA) images (n = 212) at various magnifications. The best performing model was locked down and used for independent testing with a total of 478 whole-slide images (WSIs). Manually annotated epithelial regions were used as the reference standard for evaluation. We also compared the model generated results with IHC-stained epithelium (n = 120) as the reference. RESULTS: The locked-down CNN model trained on the TMA image training cohorts with 10x magnification achieved the best segmentation performance. The locked-down model performed consistently and yielded Pixel Accuracy, Recall Rate, Precision Rate, and Dice Coefficient that ranged from 95.8% to 96.6%, 79.1% to 93.8%, 85.7% to 89.3%, and 82.3% to 89.0%, respectively for the three independent testing WSI cohorts. CONCLUSION: The automated model achieved a consistently accurate performance for automated epithelial region segmentation compared to manual annotations. This model could be integrated into a computer-aided diagnosis or prognosis system.

An Imaging Biomarker of Tumor-Infiltrating Lymphocytes to Risk-Stratify Patients With HPV-Associated Oropharyngeal Cancer.

BACKGROUND: Human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC) has excellent control rates compared to nonvirally associated OPSCC. Multiple trials are actively testing whether de-escalation of treatment intensity for these patients can maintain oncologic equipoise while reducing treatment-related toxicity. We have developed OP-TIL, a biomarker that characterizes the spatial interplay between tumor-infiltrating lymphocytes (TILs) and surrounding cells in histology images. Herein, we sought to test whether OP-TIL can segregate stage I HPV-associated OPSCC patients into low-risk and high-risk groups and aid in patient selection for de-escalation clinical trials. METHODS: Association between OP-TIL and patient outcome was explored on whole slide hematoxylin and eosin images from 439 stage I HPV-associated OPSCC patients across 6 institutional cohorts. One institutional cohort (n = 94) was used to identify the most prognostic features and train a Cox regression model to predict risk of recurrence and death. Survival analysis was used to validate the algorithm as a biomarker of recurrence or death in the remaining 5 cohorts (n = 345). All statistical tests were 2-sided. RESULTS: OP-TIL separated stage I HPV-associated OPSCC patients with 30 or less pack-year smoking history into low-risk (2-year disease-free survival [DFS] = 94.2%; 5-year DFS = 88.4%) and high-risk (2-year DFS = 82.5%; 5-year DFS = 74.2%) groups (hazard ratio = 2.56, 95% confidence interval = 1.52 to 4.32; P < .001), even after adjusting for age, smoking status, T and N classification, and treatment modality on multivariate analysis for DFS (hazard ratio = 2.27, 95% confidence interval = 1.32 to 3.94; P = .003). CONCLUSIONS: OP-TIL can identify stage I HPV-associated OPSCC patients likely to be poor candidates for treatment de-escalation. Following validation on previously completed multi-institutional clinical trials, OP-TIL has the potential to be a biomarker, beyond clinical stage and HPV status, that can be used clinically to optimize patient selection for de-escalation.

Computational pathology reveals unique spatial patterns of immune response in H&E images from COVID-19 autopsies: preliminary findings.

Purpose: We used computerized image analysis and machine learning approaches to characterize spatial arrangement features of the immune response from digitized autopsied H&E tissue images of the lung in coronavirus disease 2019 (COVID-19) patients. Additionally, we applied our approach to tease out potential morphometric differences from autopsies of patients who succumbed to COVID-19 versus H1N1. Approach: H&E lung whole slide images from autopsy specimens of nine COVID-19 and two H1N1 patients were computationally interrogated. 606 image patches ( ∼ 55 per patient) of 1024 × 882 pixels were extracted from the 11 autopsied patient studies. A watershed-based segmentation approach in conjunction with a machine learning classifier was employed to identify two types of nuclei families: lymphocytes and non-lymphocytes (i.e., other nucleated cells such as pneumocytes, macrophages, and neutrophils). Based off the proximity of the individual nuclei, clusters for each nuclei family were constructed. For each of the resulting clusters, a series of quantitative measurements relating to architecture and density of nuclei clusters were calculated. A receiver operating characteristics-based feature selection method, violin plots, and the t-distributed stochastic neighbor embedding algorithm were employed to study differences in immune patterns. Results: In COVID-19, the immune response consistently showed multiple small-size lymphocyte clusters, suggesting that lymphocyte response is rather modest, possibly due to lymphocytopenia. In H1N1, we found larger lymphocyte clusters that were proximal to large clusters of non-lymphocytes, a possible reflection of increased prevalence of macrophages and other immune cells. Conclusion: Our study shows the potential of computational pathology to uncover immune response features that may not be obvious by routine histopathology visual inspection.

A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI.

BACKGROUND: We developed and validated an integrated radiomic-clinicopathologic nomogram (RadClip) for post-surgical biochemical recurrence free survival (bRFS) and adverse pathology (AP) prediction in men with prostate cancer (PCa). RadClip was further compared against extant prognostics tools like CAPRA and Decipher. METHODS: A retrospective study of 198 patients with PCa from four institutions who underwent pre-operative 3 Tesla MRI followed by radical prostatectomy, between 2009 and 2017 with a median 35-month follow-up was performed. Radiomic features were extracted from prostate cancer regions on bi-parametric magnetic resonance imaging (bpMRI). Cox Proportional-Hazards (CPH) model warped with minimum redundancy maximum relevance (MRMR) feature selection was employed to select bpMRI radiomic features for bRFS prediction in the training set (D1, N = 71). In addition, a bpMRI radiomic risk score (RadS) and associated nomogram, RadClip, were constructed in D1 and then compared against the Decipher, pre-operative (CAPRA), and post-operative (CAPRA-S) nomograms for bRFS and AP prediction in the testing set (D2, N = 127). FINDINGS: "RadClip yielded a higher C-index (0.77, 95% CI 0.65-0.88) compared to CAPRA (0.68, 95% CI 0.57-0.8) and Decipher (0.51, 95% CI 0.33-0.69) and was found to be comparable to CAPRA-S (0.75, 95% CI 0.65-0.85). RadClip resulted in a higher AUC (0.71, 95% CI 0.62-0.81) for predicting AP compared to Decipher (0.66, 95% CI 0.56-0.77) and CAPRA (0.69, 95% CI 0.59-0.79)." INTERPRETATION: RadClip was more prognostic of bRFS and AP compared to Decipher and CAPRA. It could help pre-operatively identify PCa patients at low risk of biochemical recurrence and AP and who therefore might defer additional therapy. FUNDING: The National Institutes of Health, the U.S. Department of Veterans Affairs, and the Department of Defense.

Superfluous Hospital Expenditure Associated with Unnecessary Renal Cyst Surveillance.

INTRODUCTION: To measure the cost expenditure associated with renal cyst surveillance, we examined renal cyst surveillance patterns at our institution and the associated surplus cost of unindicated imaging. METHODS: Patients with a renal cyst diagnosis between January 2017 and June 2018 were identified and their respective clinical and imaging data were reviewed for surveillance patterns. Unindicated renal cyst followup was defined by the Radiographic Society of North America and Canadian Urological Association. Total unnecessary expenditures from ultrasound, computerized tomography and magnetic resonance imaging were calculated using cost of services provided by FAIRHealth Consumer®. Univariate and multivariable analyses were performed with statistical significance defined as p <0.05. RESULTS: A total of 1,100 patients were identified, with a random sample of 292 selected for analysis. Of these patients 271 were diagnosed with Bosniak I and II renal cysts. Overall 52 (19%) of these patients underwent unindicated imaging, which totaled 60 ultrasound, 19 computerized tomography and 5 magnetic resonance imaging. A total superfluous cost of $347,501 was calculated when extrapolating to the entire nephrology cohort. Multivariable analysis showed higher unindicated imaging for Bosniak II renal cysts compared to Bosniak I renal cysts (OR 3.2, 95% CI 1.6-6.3, p <0.001) and decreased surveillance imaging for African American compared to Caucasian patients (OR 0.29, 95% CI 0.13-0.59, p <0.001). CONCLUSIONS: Among patients diagnosed with Bosniak I and II renal cysts, unnecessary surveillance imaging was associated with higher hospital costs. Adherence to strict renal imaging guidelines for renal cysts can significantly reduce unnecessary expenditures, patient anxiety and patient harm.

Observed racial disparity in the negative predictive value of multi-parametric MRI for the diagnosis for prostate cancer.

OBJECTIVE: To evaluate the trend that despite recent advances in the screening, diagnosis, and management of prostate cancer (PCa), African-Americans (AAs) continue to have poorer outcomes compared to their Caucasian (CAU) counterparts. The reason for this may be rooted in biological differences in the cancer between the two groups; however, there may be some inherent disparities within the efficacy of the screening modalities. In this study, we aim to evaluate the negative predictive value (NPV) of multi-parametric MRI (mpMRI) between AA compared to CAUs. METHODS: All mpMRI between January 2014 and June 2017 were evaluated. The MRIs were read by dedicated genitourinary radiologists. Subsequently, the readings were correlated to final pathology after the patients underwent radical prostatectomy. The NPV and negative likelihood ratios (-LR) of mpMRI were evaluated in AAs versus CAUs based on four cutoffs (≥ Grade I, ≥ Grade II, ≥ Grade III and ≥ Grade IV). RESULTS: The mpMRI was almost equally as effective between AAs and CAUs in excluding Grade III (NPV = 89 and 94, respectively), and Grade IV or above (NPV = 96 and 98, respectively) PCa; however, the NPV of mpMRI was significantly lower for Grade I (NPV = 32 and 52, respectively) and Grade II (NPV = 50 and 79, respectively) PCa. CONCLUSION: Despite advances in the screening for PCa, there are disparities noted in the efficacy of screening tools between AAs and CAUs. For this reason, patients should be risk stratified and their screening results should be evaluated with consideration given to their baseline risk.

Whole mount histopathological correlation with prostate MRI in Grade I and II prostatectomy patients.

BACKGROUND: Multiparametric magnetic resonance imaging (mpMRI) is increasingly used in detection and surveillance of prostate cancer. However, the co-localization of lower grade lesions between mpMRI and histopathologic specimen has not been well established. OBJECTIVE: We aim to determine the factors on final histopathological exam that correlate to tumor visibility for Grade I and II disease on mpMRI. METHODS: Fifty-five patients who underwent radical prostatectomy from July 2014 to June 2016 were analyzed for the study. Of the sample of 55 patients, 18 were found to have Gleason score (GS) of 3 + 3 or 3 + 4 disease, and then were re-reviewed and annotated by a pathologist. Lesion diameter, area, and distance from the prostate capsule were measured. The annotated lesions were co-localized to the MRI report. RESULTS: Of the 184 lesions identified on the whole mount histopathologic slides, 106 (57.6%), 62 (33.7%), 14 (7.6%), and 2 (1.1%) of the lesions had a GS of 3 + 3, 3 + 4, 4 + 3, and 4 + 4, respectively. On analysis, 27.3% (24/88) of GS 6 (< 1.5 cm in size), and 88.9% (16/18) of GS 6 (> 1.5 cm in size) were identified (p < 0.001). Additionally, when assessing lesion proximity to the prostatic capsule, 46.1% (41/89) of lesions closer (≤ 0.05 cm), and 30.5% (29/95) of lesions further (> 0.05 cm) from the capsule were visualized. CONCLUSION: Lesion diameter, area, and capsule proximity correlated with MRI visibility. Further studies are encouraged to validate the findings of our study.

Younger Men With Prostate Cancer Have Lower Risk of Upgrading While on Active Surveillance: A Meta-analysis and Systematic Review of the Literature.

Active surveillance has become a popular option for patients with low risk prostate cancer. Our objective was to examine the correlation between age and the risk of Gleason upgrading and biopsy progression. A systematic search was conducted. Eight studies met our eligibility criteria including 6522 patients with a median age of 65.8 (41-86) years. Per decade decrease in age, the pooled odds ratio and hazard ratio (CI 95%) for Gleason upgrading were 0.83 (0.73-0.94) and 0.87 (0.82-0.92), and for biopsy progression were 0.80 (0.74-0.86) and 0.88 (0.79-0.99), respectively. Overall, younger patients have a lower risk of GS upgrading and biopsy progression.