High throughput assessment of biomarkers in tissue microarrays using artificial intelligence: PTEN loss as a proof-of-principle in multi-center prostate cancer cohorts.

Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and has clinical potential as a prognostic biomarker. The objective of this work was to develop an artificial intelligence (AI) system for automated detection and localization of PTEN loss on immunohistochemically (IHC) stained sections. PTEN loss was assessed using IHC in two prostate tissue microarrays (TMA) (internal cohort, n = 272 and external cohort, n = 129 patients). TMA cores were visually scored for PTEN loss by pathologists and, if present, spatially annotated. Cores from each patient within the internal TMA cohort were split into 90% cross-validation (N = 2048) and 10% hold-out testing (N = 224) sets. ResNet-101 architecture was used to train core-based classification using a multi-resolution ensemble approach (×5, ×10, and ×20). For spatial annotations, single resolution pixel-based classification was trained from patches extracted at ×20 resolution, interpolated to ×40 resolution, and applied in a sliding-window fashion. A final AI-based prediction model was created from combining multi-resolution and pixel-based models. Performance was evaluated in 428 cores of external cohort. From both cohorts, a total of 2700 cores were studied, with a frequency of PTEN loss of 14.5% in internal (180/1239) and external 13.5% (43/319) cancer cores. The final AI-based prediction of PTEN status demonstrated 98.1% accuracy (95.0% sensitivity, 98.4% specificity; median dice score = 0.811) in internal cohort cross-validation set and 99.1% accuracy (100% sensitivity, 99.0% specificity; median dice score = 0.804) in internal cohort test set. Overall core-based classification in the external cohort was significantly improved in the external cohort (area under the curve = 0.964, 90.6% sensitivity, 95.7% specificity) when further trained (fine-tuned) using 15% of cohort data (19/124 patients). These results demonstrate a robust and fully automated method for detection and localization of PTEN loss in prostate cancer tissue samples. AI-based algorithms have potential to streamline sample assessment in research and clinical laboratories.

Data Augmentation and Transfer Learning to Improve Generalizability of an Automated Prostate Segmentation Model.

OBJECTIVE. Deep learning applications in radiology often suffer from overfitting, limiting generalization to external centers. The objective of this study was to develop a high-quality prostate segmentation model capable of maintaining a high degree of performance across multiple independent datasets using transfer learning and data augmentation. MATERIALS AND METHODS. A retrospective cohort of 648 patients who underwent prostate MRI between February 2015 and November 2018 at a single center was used for training and validation. A deep learning approach combining 2D and 3D architecture was used for training, which incorporated transfer learning. A data augmentation strategy was used that was specific to the deformations, intensity, and alterations in image quality seen on radiology images. Five independent datasets, four of which were from outside centers, were used for testing, which was conducted with and without fine-tuning of the original model. The Dice similarity coefficient was used to evaluate model performance. RESULTS. When prostate segmentation models utilizing transfer learning were applied to the internal validation cohort, the mean Dice similarity coefficient was 93.1 for whole prostate and 89.0 for transition zone segmentations. When the models were applied to multiple test set cohorts, the improvement in performance achieved using data augmentation alone was 2.2% for the whole prostate models and 3.0% for the transition zone segmentation models. However, the best test-set results were obtained with models fine-tuned on test center data with mean Dice similarity coefficients of 91.5 for whole prostate segmentation and 89.7 for transition zone segmentation. CONCLUSION. Transfer learning allowed for the development of a high-performing prostate segmentation model, and data augmentation and fine-tuning approaches improved performance of a prostate segmentation model when applied to datasets from external centers.

The Cancer of the Bladder Risk Assessment (COBRA) score: Estimating mortality after radical cystectomy.

BACKGROUND: Risk stratification of patients with urothelial carcinoma of the bladder (UCB) after cystectomy has important clinical and research implications. The authors assessed the relative effect of tumor stage and lymph node status on cancer-specific survival (CSS) after cystectomy and developed a simplified risk-assessment tool. METHODS: In total, 14,828 patients who underwent cystectomy with lymph node dissection for UCB were identified from the Surveillance, Epidemiology, and End Results database (1988-2011). The relative importance of tumor stage and lymph node status with regard to CSS was assessed using stratified Kaplan-Meier and Cox proportional-hazards analyses. The patients were split randomly into development and validation cohorts. Additional validation using overall survival was performed on 19,362 patients from the National Cancer Data Base. The Cancer of Bladder Risk Assessment (COBRA) tool was created using a Cox model incorporating age, tumor stage, and lymph node density. Performance was validated using observed versus expected survival plots and the Harrell concordance index. RESULTS: Patients with muscle invasive (T2), lymph node-positive disease had a survival curve similar to that in patients with extravesical (T3 and T4), lymph node-negative disease (2-year CSS, 67% and 70%, respectively). Each point increase in the COBRA score (range, 0-7) was associated with a 1.61-fold increase (95% confidence interval, 1.56-fold to 1.65-fold increase) in the risk of bladder cancer death in the development cohort. The model accurately stratified patients across risk levels in the development cohort and the 2 validation cohorts (C-index, 0.712, 0.705, and 0.68, respectively). CONCLUSIONS: The COBRA score offers a straightforward, validated risk-stratification tool that incorporates the relative contribution of tumor stage and lymph node involvement to patient prognosis after cystectomy for UCB. Cancer 2017;123:4574-4582. © 2017 American Cancer Society.

Urethral Stricture Score is Associated with Anterior Urethroplasty Complexity and Outcome.

PURPOSE: Several surgical techniques are available to treat anterior urethral stricture. The choice of surgical technique largely depends on the severity of stricture disease. The U-score (urethral stricture score) is based on urethral stricture characteristics, namely length (1 to 3 points), number (1 or 2 points), location (1 or 2 points) and etiology (1 or 2 points), which are tallied to provide a total score of 4 to 9 points. Our aim was to identify whether the U-score system is predictive of the surgical complexity and outcome of anterior urethroplasty. MATERIALS AND METHODS: We retrospectively reviewed the records of all patients who underwent anterior urethroplasty from 2002 to 2012 by examining our prospectively collected urethroplasty database. We calculated the U-score and looked for an association with surgical complexity, recurrent stricture and time to recurrence. We defined recurrent stricture as the need for a secondary procedure. RESULTS: There were 341 patients who underwent low complexity urethroplasty (anastomotic, buccal mucosal graft and augmented anterior urethroplasty) with a mean U-score of 4.7 while 48 underwent high complexity urethroplasty (double buccal mucosal graft, flap and graft/flap combination) with a mean score of 6.9. Higher U-score was predictive of higher surgical complexity (p <0.001). U-score was also significantly associated with recurrence. There was a consistent increase in the risk of recurrence with each additional U-score point. However, there was no association of U-score with time to recurrence. CONCLUSIONS: We confirmed the validity of U-score to predict the complexity of surgery for anterior urethral strictures. For the first time to our knowledge we report an association between higher U-score and anterior urethroplasty outcome. The U-score could be used to risk stratify patients and help with perioperative counseling.

Automated prostate gland segmentation in challenging clinical cases: comparison of three artificial intelligence methods.

OBJECTIVE: Automated methods for prostate segmentation on MRI are typically developed under ideal scanning and anatomical conditions. This study evaluates three different prostate segmentation AI algorithms in a challenging population of patients with prior treatments, variable anatomic characteristics, complex clinical history, or atypical MRI acquisition parameters. MATERIALS AND METHODS: A single institution retrospective database was queried for the following conditions at prostate MRI: prior prostate-specific oncologic treatment, transurethral resection of the prostate (TURP), abdominal perineal resection (APR), hip prosthesis (HP), diversity of prostate volumes (large ≥ 150 cc, small ≤ 25 cc), whole gland tumor burden, magnet strength, noted poor quality, and various scanners (outside/vendors). Final inclusion criteria required availability of axial T2-weighted (T2W) sequence and corresponding prostate organ segmentation from an expert radiologist. Three previously developed algorithms were evaluated: (1) deep learning (DL)-based model, (2) commercially available shape-based model, and (3) federated DL-based model. Dice Similarity Coefficient (DSC) was calculated compared to expert. DSC by model and scan factors were evaluated with Wilcox signed-rank test and linear mixed effects (LMER) model. RESULTS: 683 scans (651 patients) met inclusion criteria (mean prostate volume 60.1 cc [9.05-329 cc]). Overall DSC scores for models 1, 2, and 3 were 0.916 (0.707-0.971), 0.873 (0-0.997), and 0.894 (0.025-0.961), respectively, with DL-based models demonstrating significantly higher performance (p < 0.01). In sub-group analysis by factors, Model 1 outperformed Model 2 (all p < 0.05) and Model 3 (all p < 0.001). Performance of all models was negatively impacted by prostate volume and poor signal quality (p < 0.01). Shape-based factors influenced DL models (p < 0.001) while signal factors influenced all (p < 0.001). CONCLUSION: Factors affecting anatomical and signal conditions of the prostate gland can adversely impact both DL and non-deep learning-based segmentation models.

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

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

Outcomes and timing for intervention of partial adrenalectomy in patients with a solitary adrenal remnant and history of bilateral phaeochromocytomas.

OBJECTIVE: To evaluate the outcomes and timing of intervention for adrenal-sparing surgery in patients left with a solitary adrenal remnant after bilateral adrenal surgeries. PATIENTS AND METHODS: Patients were included in the study if they had undergone bilateral adrenal surgery as a treatment for phaeochromocytoma and were left with a solitary adrenal remnant. Perioperative, functional and oncological outcomes were evaluated in 21 patients who met the inclusion criteria. RESULTS: There was minimal perioperative morbidity and no perioperative mortality. After a median (range) follow-up of 21 (3-143) months, there were two cases of persistent disease. Ten patients (48%) required steroid supplementation upon discharge, with four subsequently discontinuing this treatment. Patients were more likely to require steroid supplementation after surgery if they underwent simultaneous adrenalectomy and contralateral partial adrenalectomy, rather than staged procedures (86 vs 40%, P = 0.02). Patients who underwent surgery for tumours > 4 cm were more likely to require long-term steroids than patients who underwent surgery for lesions < 4 cm (75 vs 18%, P = 0.05). CONCLUSIONS: Patients left with a solitary adrenal remnant after bilateral adrenal surgery have low surgical morbidity, reasonable functional outcomes and low rates of recurrence at an intermediate follow-up period. A staged approach could decrease the immediate postoperative need for steroids, and intervention before the largest tumour reaches 4 cm could decrease the rate of long-term steroid dependence.

Rapidly Progressing Urothelial Carcinoma Due to a Rare TP53 (p.Arg110Pro) Mutation: A Case Report and Review of the Literature.

We present a case of a 69-year-old male patient diagnosed with high grade (T1 HG) urothelial carcinoma of the bladder who progressed rapidly towards muscle invasive disease and eventually death despite neoadjuvant chemotherapy and radical cystectomy. We postulate that this may be due to a deleterious underlying somatic gene mutation. Molecular pathologic data obtained on the initial, non-muscle invasive tumor and the final cystectomy specimen, revealed the same TP53 mutation (p.Arg110Pro) in both specimens with a variant allele frequency of 44%. The tumor was tested for 50 common gene mutations in urothelial carcinoma and no other identifiable DNA repair mutations were found, suggesting that this specific TP53 aberration, one that has never been reported in the bladder cancer literature, could be particularly deleterious. Knowing that bladder cancer cell lines that lack TP53 are more resistant to cisplatin and because the tumor lacked any other DNA mutation, this patient may have been a candidate for upfront surgery without neoadjuvant chemotherapy. In addition to histological analysis of the tumor, early molecular and cytogenetic characterization of resected tissue is essential in predicting progression and eventual prognosis of the disease based on identifiable gene mutations. Further comparative prospective studies are required to clarify the importance of molecular heterogeneity and subtyping in bladder cancer.

Quantitative Characterization of the Prostatic Urethra Using MRI: Implications for Lower Urinary Tract Symptoms in Patients with Benign Prostatic Hyperplasia.

INTRODUCTION: The aim of this study was to perform a quantitative assessment of the prostate anatomy with a focus on the relation of prostatic urethral anatomic variation to urinary symptoms. METHODS: This retrospective study involved patients undergoing magnetic resonance imaging for prostate cancer who were also assessed for lower urinary tract symptoms. Volumetric segmentations were utilized to derive the in vivo prostatic urethral length and urethral trajectory in coronal and sagittal planes using a piece-wise cubic spline function to derive the angle of the urethra within the prostate. Association of anatomical factors with urinary symptoms was evaluated using ordinal univariable and multivariable logistic regression with IPSS score cutoffs of ≤7, 8-19, and >20 to define mild, moderate, and severe symptoms, respectively. RESULTS: A total of 423 patients were included. On univariable analysis, whole prostate volume, transition zone volume, prostatic urethral length, urethral angle, and retrourethral volume were all significantly associated with worse urinary symptoms. On multivariable analysis prostatic urethral length was associated with urinary symptoms with a normalized odds ratio of 1.5 (95% confidence interval 1.0-2.2, p = 0.04). In a subset analysis of patients on alpha blockers, maximal urethral angle, transition zone volume as well as urethral length were all associated with worse urinary symptoms. CONCLUSION: Multiple parameters were associated with worse urinary symptoms on univariable analysis, but only prostatic urethral length was associated with worse urinary symptoms on multivariable analysis. This study demonstrates the ability of quantitative assessment of prostatic urethral anatomy to predict lower urinary tract symptoms.

Comprehensive genomic profiling of metastatic collecting duct carcinoma, renal medullary carcinoma, and clear cell renal cell carcinoma.

INTRODUCTION AND OBJECTIVE: Unlike clear cell renal cell carcinoma (CCRCC), collecting duct carcinoma (CDC) and renal medullary carcinoma (RMC) are rare tumors that progress rapidly and appear resistant to current systemic therapies. We queried comprehensive genomic profiling to uncover opportunities for targeted therapy and immunotherapy. MATERIAL AND METHODS: DNA was extracted from 40 microns of formalin-fixed, paraffin-embedded specimen from relapsed, mCDC (n = 46), mRMC (n = 24), and refractory and metastatic (m) mCCRCC (n = 626). Comprehensive genomic profiling was performed, and Tumor mutational burden (TMB) and microsatellite instability (MSI) were calculated. We analyzed all classes of genomic alterations. RESULTS: mCDC had 1.7 versus 2.7 genomic alterations/tumor in mCCRCC ( = 0.04). Mutations in VHL (P < 0.0001) and TSC1 (P = 0.04) were more frequent in mCCRCC. SMARCB1 (P < 0.0001), NF2 (P = 0.0007), RB1 (P = 0.02) and RET (P = 0.0003) alterations were more frequent in mCDC versus mCCRCC. No VHL alterations in mRMC and mCDC were identified. SMARCB1 genomic alterations were significantly more frequent in mRMC than mCDC (P = 0.0002), but were the most common alterations in both subtypes. Mutations to EGFR, RET, NF2, and TSC2 were more frequently identified in mCDC versus mRMC. The median TMB and MSI-High status was low with <1% of mCCRC, mCDC, and mRMC having ≥ 20 mut/Mb. CONCLUSION: Genomic alteration patterns in mCDC and mRMC differ significantly from mCCRCC. Targeted therapies for mCDC and mRMC appear limited with rare opportunities to target alterations in receptor tyrosine kinase and MTOR pathways. Similarly, TMB and absence of MSI-High status in mCDC and mRMC suggest resistance to immunotherapies.

Long-term Functional and Oncologic Outcomes of Partial Adrenalectomy for Pheochromocytoma.

OBJECTIVE: To evaluate the recurrence and functional outcomes in a primarily hereditary cohort of patients undergoing partial adrenalectomy for pheochromocytoma. METHODS: A retrospective review from a prospectively managed database of patients undergoing partial adrenalectomy from 1995 to 2018 at the National Cancer Institute was performed. Local recurrence was defined as imaging evidence of a recurrent or de novo lesion on the operative side. Steroid dependency was defined as requiring daily steroid replacement at time of last follow-up. RESULTS: One hundred and twenty-four partial adrenalectomies, removing 162 tumors, were performed in 107 patients. Most patients had a known hereditary predisposition to develop bilateral, multifocal, and recurrent pheochromocytoma. Median tumor size was 2 cm (interquartile range (IQR) 1.5-2.8). Median follow-up was 60 months (IQR 13-131). Local recurrence occurred in 17 patients (15.8%) and were managed with active surveillance or surgery. A single patient (1/106, 0.9%) developed metastatic spread of pheochromocytoma approximately 14 years after his first of 2 partial adrenalectomies and remains alive under active surveillance. Median time to recurrence was 71 months (IQR 26-127) with 10 patients (9.3%) requiring daily steroid replacement at time of last follow-up. CONCLUSION: Partial adrenalectomy offers excellent oncologic and functional outcomes, sparing most patients from lifelong steroid replacement therapy. Recurrences can be easily managed with repeat surgery or active surveillance via functional work-up and imaging. Partial adrenalectomy remains the recommended surgical management for patients pre-disposed to development of bilateral, multifocal and recurrent pheochromocytoma.

Multiresolution Application of Artificial Intelligence in Digital Pathology for Prediction of Positive Lymph Nodes From Primary Tumors in Bladder Cancer.

PURPOSE: To develop an artificial intelligence (AI)-based model for identifying patients with lymph node (LN) metastasis based on digital evaluation of primary tumors and train the model using cystectomy specimens available from The Cancer Genome Atlas (TCGA) Project; patients from our institution were included for validation of the leave-out test cohort. METHODS: In all, 307 patients were identified for inclusion in the study (TCGA, n = 294; in-house, n = 13). Deep learning models were trained from image patches at 2.5×, 5×, 10×, and 20× magnifications, and spatially resolved prediction maps were combined with microenvironment (lymphocyte infiltration) features to derive a final patient-level AI score (probability of LN metastasis). Training and validation included 219 patients (training, n = 146; validation, n = 73); 89 patients (TCGA, n = 75; in-house, n = 13) were reserved as an independent testing set. Multivariable logistic regression models for predicting LN status based on clinicopathologic features alone and a combined model with AI score were fit to training and validation sets. RESULTS: Several patients were determined to have positive LN metastasis in TCGA (n = 105; 35.7%) and in-house (n = 3; 23.1%) cohorts. A clinicopathologic model that considered using factors such as age, T stage, and lymphovascular invasion demonstrated an area under the curve (AUC) of 0.755 (95% CI, 0.680 to 0.831) in the training and validation cohorts compared with the cross validation of the AI score (likelihood of positive LNs), which achieved an AUC of 0.866 (95% CI, 0.812 to 0.920; P = .021). Performance in the test cohort was similar, with a clinicopathologic model AUC of 0.678 (95% CI, 0.554 to 0.802) and an AI score of 0.784 (95% CI, 0.702 to 0.896; P = .21). In addition, the AI score remained significant after adjusting for clinicopathologic variables (P = 1.08 × 10-9), and the combined model significantly outperformed clinicopathologic features alone in the test cohort with an AUC of 0.807 (95% CI, 0.702 to 0.912; P = .047). CONCLUSION: Patients who are at higher risk of having positive LNs during cystectomy can be identified on primary tumor samples using novel AI-based methodologies applied to digital hematoxylin and eosin-stained slides.

Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.

Emphysematous pyelonephritis: the impact of urolithiasis on disease severity.

BACKGROUND: Emphysematous pyelonephritis is a severe infection of the kidney associated with formation of gas in the renal parenchyma and/or collecting system. The purpose of this study was to evaluate outcomes of patients with emphysematous pyelonephritis in a contemporary cohort and to evaluate the impact of urolithiasis on disease severity. METHODS: A search of all imaging reports at University of California San Francisco (UCSF) for the term "emphysematous pyelonephritis" was undertaken from 2003-2014. Patients were included if there was clinical evidence of infection, no recent urologic instrumentation, and computerized tomography (CT) demonstrating gas in the renal parenchyma or collecting system. Clinical and laboratory variables were obtained from medical records. RESULTS: A total of 14 cases were identified. The majority of patients (57%) had gas confined to the collecting system. Three patients (21%) had gas in the renal parenchyma and 3 patients (21%) had gas extending into perirenal tissues. A total of 8 patients (57%) had concomitant urolithiasis. Seven patients (50%) were managed with antibiotic therapy alone while 6 patients (43%) required percutaneous drainage. No patients required immediate nephrectomy. There were no deaths. Patients with urolithiasis had less severe emphysematous pyelonephritis than patients without urolithiasis (P<0.05). CONCLUSIONS: The majority of patients in this study had gas contained within the collecting system and were treated successfully with antibiotics alone. Percutaneous drainage was successfully utilized in patients with more advanced disease. No patients required emergent nephrectomy. Emphysematous pyelonephritis in patients with urolithiasis was less severe than in patients without urolithiasis.

The Decline of Inpatient Penile Prosthesis over the 10-Year Period, 2000-2010.

INTRODUCTION: Across all specialties, economic pressure is driving increased utilization of outpatient surgery when feasible. AIMS: Our aims were to analyze national trends of penile prosthesis (PP) surgery and to examine patient and hospital characteristics, and perioperative complications in the inpatient setting. METHODS: We analyzed data from National Inpatient Sample. Patients in NIS who underwent PP insertion between 2000 and 2010 were included. MAIN OUTCOME MEASURES: Our main outcomes were the number of inpatient PP procedures, type of prosthesis, patient demographics, comorbidities, hospital characteristics, and immediate perioperative complications. RESULTS: There was a progressive and dramatic decline by nearly half in the number of both inflatable (IPP) and noninflatable (NIPP) inpatient insertions performed from 2000 to 2010 (P = 0.0001). The overall rate of inpatient complications for PP insertion was 13.5%. Patients with three or more comorbidities were found to have a higher risk of complications than patients with no comorbidities (OR = 1.45, 95% CI = 1.18-1.78) (P = 0.0001). Surgeries performed in high-volume hospitals (10 or more PP cases per year) were associated with reduced risk of complications (OR = 0.6) (P < 0.0001). There was a dramatic decrease in inpatient setting for PP placement in high-volume hospitals (32% in 2000 compared with 6% in 2010; P < 0.0001), and when compared with lower volume hospitals. NIPP was more likely performed in younger patients and in community hospitals, and less likely in white patients. Medicaid health insurance was associated with much higher rate of NIPP insertion than other types of insurance. CONCLUSIONS: The number of PP procedures performed in the inpatient setting declined between 2000 and 2010, likely reflecting a shift toward increasing outpatient procedures. Our data also suggest a better outcome for patients having the procedure done at a high-volume center in terms of inpatient complications. Alwaal A, Harris CR, Hussein AA, Sanford TH, McCulloch CE, Shindel AW, and Breyer BN. The decline of inpatient penile prosthesis over the 10-year period, 2000-2010. Sex Med 2015;3:280-286.

Xanthogranulomatous pyelonephritis presenting with a left flank mass.

We present a case of a patient with xanthogranulomatous pyelonephritis (XGP) presenting with a large (18 × 12 cm) left-sided flank mass with worsening left flank pain. CT abdomen/pelvis demonstrated a left kidney with parenchyma replaced by multiple large hypodense collections containing fluid and gas, a left staghorn calculus, and a communication between the kidney and large flank collection. About 4.5 weeks after initial presentation, the patient underwent operative intervention. Pathology revealed an end-stage kidney with scar consistent with xanthogranulomatous pyelonephritis.