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Background: Non–muscle-invasive bladder cancer (NMIBC) is characterized by frequent recurrence of the disease, which is difficult to predict. Objective: To combine digital histopathology slides with clinical data to predict 1- and 5-yr recurrence-free survival of NMIBC patients using deep learning. Design, setting, and participants: Data of patients undergoing a transurethral resection of a bladder tumor between 2000 and 2018 at a Dutch academic medical center were selected. Corresponding histological slides were digitized. A three-step approach was used to predict 1- and 5-yr recurrence-free survival. First, a segmentation network was used to detect the urothelium on the digital histopathology slides. Second, a selection network was trained for the selection of patches associated with recurrence. Third, a classification network, combining the information of the selection network with clinical data, was trained to give the probability of 1- and 5-yr recurrence-free survival. Outcome measurements and statistical analysis: The accuracy of the deep learning–based model was compared with a multivariable logistic regression model using clinical data only. Results and limitations: In the 1- and 5-yr follow-up cohorts, 359 and 281 patients were included with recurrence rates of 27% and 63%, respectively. The areas under the curve (AUCs) of the model combining digital histopathology slide data with clinical data were 0.62 and 0.76 for 1- and 5-yr recurrence predictions, respectively, which were higher than those of the model using digital histopathology slide data only (AUCs of 0.56 and 0.72, respectively) and the multivariable logistic regression (AUCs of 0.58 and 0.57, respectively). Conclusions: In our population, the deep learning–based model combining digital histopathology slides and clinical data enhances the prediction of recurrence (within 5 yr) compared with models using clinical data or image data only. Patient summary: By combining histopathology images and patient record data using deep learning, the prediction of recurrence in bladder cancer patients is enhanced. The proposed deep learning method showed promising results for the 5-yr prediction of recurrence when combining clinical data and digital histopathology images. Although not yet validated for clinical practice, this deep learning approach has the potential to improve recurrence prediction.

Introduction: Urolithiasis in renal allografts is relatively rare with an incidence of 0.17–4.40%. It is nonetheless an important issue, as there is a risk of obstruction, sepsis and even loss of the renal allograft. The management of stones in renal allografts remains challenging because of the anatomy, the renal denervation and the use of immunosuppressive medication. Case presentation: This report discusses the ex-vivo treatment of asymptomatic nephrolithiasis in a living donor kidney allograft. A CT abdomen revealed a lower pole stone (5.9 × 5.5 × 5.0 mm; 920 HU) in the right kidney of the potential donor. After multidisciplinary discussion, it was decided to procure the right kidney despite the presence of a documented nephrolithiasis. After discussion with both donor and recipient, an ex-vivo flexible ureterorenoscopy for stone removal on the back table just before implantation of the allograft was planned. The stone was found in the lower pole covered by a thin film of the urothelium. The thin film of urothelium was opened with a laser and the stone fragments were retrieved with a basket. CT after one month showed no residual stones in the transplanted kidney. Conclusion: Back-table endoscopy in a renal allograft is a feasible technique and should be discussed as an option in case of urolithiasis in a kidney that is considered for transplantation. Furthermore, the appropriate treatment of donor kidney lithiasis is another, although rare, method to expand the living donor renal allograft pool.

INTRODUCTION AND OBJECTIVE:The EORTC and CUETO are used to assess the risk for recurrence in non-muscle invasive bladder cancer (NMIBC). Low concordance rates with recurrence are reported for those...

INTRODUCTION AND OBJECTIVE:The EORTC, CUETO and EAU are the three most commonly used risk assessment methods for the prediction of recurrence in non-muscle invasive bladder cancer (NMIBC). However,...

INTRODUCTION AND OBJECTIVE:Histological grade is an important predictor for recurrence and progression in non-muscle invasive bladder cancer (NMIBC). However, it is subjected to low interobserver a...

BACKGROUND: EORTC, CUETO and EAU are the most commonly used risk stratification models for recurrence and progression in non-muscle invasive bladder cancer (NMIBC). OBJECTIVE: We assessed the predictive value of the EORTC, CUETO and EAU risk group stratification methods for our population and explore options to improve the predictive value using Cox Proportional Hazards (CPH), Boosted Cox regression and a non-linear Random Survival Forest (RSF) model. MATERIALS: Our retrospective database included of 452 NMIBC patients who underwent a transurethral resection of bladder tumor (TURBT) between 2000 and 2018 in our hospital. The cumulative incidence of recurrence was calculated at one- and five-years for all risk stratification methods. A customized CPH, Boosted Cox and RSF models were trained in order to predict recurrence, and the performances were compared. RESULTS: Risk stratification using the EORTC, CUETO and EAU showed small differences in recurrence probabilities between the risk groups as determined by the risk stratification. The concordance indices (C-index) were low and ranged between 0.51 and 0.57. The predictive accuracies of CPH, Boosted Cox and RSF models were also moderate, with C-indices ranging from 0.61 to 0.64. CONCLUSIONS: Prediction of recurrence in patients with NMIBC based on patient characteristics is difficult. Alternative (non-linear) approaches have the potential to improve the predictive value. Nonetheless, the currently used characteristics are unable to properly stratify between the recurrence risks of patients.

BACKGROUND AND PURPOSE: Many studies have emphasized the relevance of collateral flow in patients presenting with acute ischemic stroke. Our aim was to evaluate the relationship of the quantitative collateral score on baseline CTA with the outcome of patients with acute ischemic stroke and test whether the timing of the CTA acquisition influences this relationship.MATERIALS AND METHODS: From the Multicenter Randomized Clinical Trial of Endovascular Treatment of Acute Ischemic Stroke in the Netherlands (MR CLEAN) data base, all baseline thin-slice CTA images of patients with acute ischemic stroke with intracranial large-vessel occlusion were retrospectively collected. The quantitative collateral score was calculated as the ratio of the vascular appearance of both hemispheres and was compared with the visual collateral score. Primary outcomes were 90-day mRS score and follow-up infarct volume. The relation with outcome and the association with treatment effect were estimated. The influence of the CTA acquisition phase on the relation of collateral scores with outcome was determined.RESULTS: A total of 442 patients were included. The quantitative collateral score strongly correlated with the visual collateral score (ρ = 0.75) and was an independent predictor of mRS (adjusted odds ratio = 0.81; 95% CI, .77-.86) and follow-up infarct volume (exponent β = 0.88; P < .001) per 10% increase. The quantitative collateral score showed areas under the curve of 0.71 and 0.69 for predicting functional independence (mRS 0-2) and follow-up infarct volume of >90 mL, respectively. We found significant interaction of the quantitative collateral score with the endovascular therapy effect in unadjusted analysis on the full ordinal mRS scale (P = .048) and on functional independence (P = .049). Modification of the quantitative collateral score by acquisition phase on outcome was significant (mRS: P = .004; follow-up infarct volume: P < .001) in adjusted analysis.CONCLUSIONS: Automated quantitative collateral scoring in patients with acute ischemic stroke is a reliable and user-independent measure of the collateral capacity on baseline CTA and has the potential to augment the triage of patients with acute stroke for endovascular therapy.

Due to the growing field of digital pathology, more and more digital histology slides are becoming available. This improves the accessibility, allows teleconsultations from specialized pathologists, improves education, and might give urologist the possibility to review the slides in patient management systems. Moreover, by stacking multiple two-dimensional (2D) digital slides, three-dimensional volumes can be created, allowing improved insight in the growth pattern of a tumor. With the addition of computer-aided diagnosis systems, pathologist can be guided to regions of interest, potentially reducing the workload and interobserver variation. Digital (3D) pathology has the potential to improve dialog between the pathologist and urologist, and, therefore, results in a better treatment selection for urologic patients. Electronic supplementary material The online version of this article (10.1007/s00345-018-2202-1) contains supplementary material, which is available to authorized users.

Introduction&Objectives:Theinfluenceofintravesicaltumorlocationondiseaseoutcomeisrarelystudiedinpatientswithnon-muscleinvasivebladdercancer(NMIBC).Ouraimwastodeterminetheassociationoftumorlocationonrecurrence-freesurvival(RFS)inpatientswithprimary,solitary NMIBC.Materials&Methods:AfterIRBapproval,dataof840patientswhounderwenttransurethralresectionofbladdertumor(TURBT)in1hospitalbetween2000-2018wasretrospectivelycollected.Patientswithaprimary,solitaryNMIBCwereincluded.Caseswithirradicalresection,CISorconcomitantuppertracttumorwereexcluded.Tumorlocationwasassessedbycheckingtheoperation,cystoscopyandpathologyreport.AreasoftumorlocationwerebasedontheEAUbladdermap.Incaseofoverlappingareas,theareaoftumororiginwasselected.Moreover,patientsweredichotomizedintodorsalvs.non-dorsaltumors.Thedorsalareawasdefinedasthediamondborderedbybladderneck,trigone,posteriorwallandorifices.Thenon-dorsalareasarethelateralwalls,domeandanteriorwall.TheassociationoftumorlocationwithRFSwasassessedusingCoxregression. Median RFS was estimated using the Kaplan-Meier method. Statistical significance was considered at p

Histopathologic grading of prostate cancer using Gleason patterns (GPs) is subject to a large inter-observer variability, which may result in suboptimal treatment of patients. With the introduction of digitization and whole-slide images of prostate biopsies, computer-aided grading becomes feasible. Computer-aided grading has the potential to improve histopathological grading and treatment selection for prostate cancer. Automated detection of GPs and determination of the grade groups (GG) using a convolutional neural network. In total, 96 prostate biopsies from 38 patients are annotated on pixel-level. Automated detection of GP 3 and GP ≥ 4 in digitized prostate biopsies is performed by re-training the Inception-v3 convolutional neural network (CNN). The outcome of the CNN is subsequently converted into probability maps of GP ≥ 3 and GP ≥ 4, and the GG of the whole biopsy is obtained according to these probability maps. Differentiation between non-atypical and malignant (GP ≥ 3) areas resulted in an accuracy of 92% with a sensitivity and specificity of 90 and 93%, respectively. The differentiation between GP ≥ 4 and GP ≤ 3 was accurate for 90%, with a sensitivity and specificity of 77 and 94%, respectively. Concordance of our automated GG determination method with a genitourinary pathologist was obtained in 65% (κ = 0.70), indicating substantial agreement. A CNN allows for accurate differentiation between non-atypical and malignant areas as defined by GPs, leading to a substantial agreement with the pathologist in defining the GG. Electronic supplementary material The online version of this article (10.1007/s00428-019-02577-x) contains supplementary material, which is available to authorized users.

Background Histopathological analysis is the cornerstone in bladder cancer (BCa) diagnosis. These analysis suffer from a moderate observer agreement in the staging of bladder cancer. Three-dimensional reconstructions have the potential to support the pathologists in visualizing spatial arrangements of structures, which may improve the interpretation of specimen. The aim of this study is to present three-dimensional (3D) reconstructions of histology images. Methods En-bloc specimens of transurethral bladder tumour resections were formalin fixed and paraffin embedded. Specimens were cut into sections of 4 μm and stained with Hematoxylin and Eosin (H&E). With a Phillips IntelliSite UltraFast scanner, glass slides were digitized at 20x magnification. The digital images were aligned by performing rigid and affine image alignment. The tumour and the muscularis propria (MP) were manually delineated to create 3D segmentations. In conjunction with a 3D display, the results were visualized with the Vesalius3D interactive visualization application for a 3D workstation. Results En-bloc resection was performed in 21 BCa patients. Per case, 26–30 sections were included for the reconstruction into a 3D volume. Five cases were excluded due to export problems, size of the dataset or condition of the tissue block. Qualitative evaluation suggested an accurate registration for 13 out of 16 cases. The segmentations allowed full 3D visualization and evaluation of the spatial relationship of the BCa tumour and the MP. Conclusion Digital scanning of en-bloc resected specimens allows a full-fledged 3D reconstruction and analysis and has a potential role to support pathologists in the staging of BCa.

Mass spectrometry imaging (MSI) has emerged as a powerful tool in biomedical research to reveal the localization of a broad scale of compounds ranging from metabolites to proteins in diseased tissues, such as malignant tumors. MSI is most commonly used for the two-dimensional imaging of tissues from multiple patients or for the three-dimensional (3D) imaging of tissue from a single patient. These applications are potentially introducing a sampling bias on a sample or patient level, respectively. The aim of this study is therefore to investigate the consequences of sampling bias on sample representativeness and on the precision of biomarker discovery for histological grading of human bladder cancers by MSI. We therefore submitted formalin-fixed paraffin-embedded tissues from 14 bladder cancer patients with varying histological grades to 3D analysis by matrix-assisted laser desorption/ionization (MALDI) MSI. We found that, after removing 20% of the data based on novel outlier detection routines for 3D-MSI data based on the evaluation of digestion efficacy and z-directed regression, on average 33% of a sample has to be measured in order to obtain sufficient coverage of the existing biological variance within a tissue sample.Significance: In this study, 3D MALDI-MSI is applied for the first time on a cohort of bladder cancer patients using formalin-fixed paraffin-embedded (FFPE) tissue of bladder cancer resections. This work portrays the reproducibility that can be achieved when employing an optimized sample preparation and subsequent data evaluation approach. Our data shows the influence of sampling bias on the variability of the results, especially for a small patient cohort. Furthermore, the presented data analysis workflow can be used by others as a 3D FFPE data-analysis pipeline working on multi-patient 3D-MSI studies.

The good, the bad and the ugly of catheterization practice among elite athletes with spinal cord injury

Attempts have been made to associate intracranial aneurysmal hemodynamics with aneurysm growth and rupture status. Hemodynamics in aneurysms is traditionally determined with computational fluid dynamics by using generalized inflow boundary conditions in a parent artery. Recently, patient-specific inflow boundary conditions are being implemented more frequently. Our purpose was to compare intracranial aneurysm hemodynamics based on generalized versus patient-specific inflow boundary conditions. For 36 patients, geometric models of aneurysms were determined by using 3D rotational angiography. 2D phase-contrast MR imaging velocity measurements of the parent artery were performed. Computational fluid dynamics simulations were performed twice: once by using patient-specific phase-contrast MR imaging velocity profiles and once by using generalized Womersley profiles as inflow boundary conditions. Resulting mean and maximum wall shear stress and oscillatory shear index values were analyzed, and hemodynamic characteristics were qualitatively compared. Quantitative analysis showed statistically significant differences for mean and maximum wall shear stress values between both inflow boundary conditions (P < .001). Qualitative assessment of hemodynamic characteristics showed differences in 21 cases: high wall shear stress location (n = 8), deflection location (n = 3), lobulation wall shear stress (n = 12), and/or vortex and inflow jet stability (n = 9). The latter showed more instability for the generalized inflow boundary conditions in 7 of 9 patients. Using generalized and patient-specific inflow boundary conditions for computational fluid dynamics results in different wall shear stress magnitudes and hemodynamic characteristics. Generalized inflow boundary conditions result in more vortices and inflow jet instabilities. This study emphasizes the necessity of patient-specific inflow boundary conditions for calculation of hemodynamics in cerebral aneurysms by using computational fluid dynamics techniques