Precise Prostate Cancer Assessment Using IVIM-Based Parametric Estimation of Blood Diffusion from DW-MRI.

Prostate cancer is a significant health concern with high mortality rates and substantial economic impact. Early detection plays a crucial role in improving patient outcomes. This study introduces a non-invasive computer-aided diagnosis (CAD) system that leverages intravoxel incoherent motion (IVIM) parameters for the detection and diagnosis of prostate cancer (PCa). IVIM imaging enables the differentiation of water molecule diffusion within capillaries and outside vessels, offering valuable insights into tumor characteristics. The proposed approach utilizes a two-step segmentation approach through the use of three U-Net architectures for extracting tumor-containing regions of interest (ROIs) from the segmented images. The performance of the CAD system is thoroughly evaluated, considering the optimal classifier and IVIM parameters for differentiation and comparing the diagnostic value of IVIM parameters with the commonly used apparent diffusion coefficient (ADC). The results demonstrate that the combination of central zone (CZ) and peripheral zone (PZ) features with the Random Forest Classifier (RFC) yields the best performance. The CAD system achieves an accuracy of 84.08% and a balanced accuracy of 82.60%. This combination showcases high sensitivity (93.24%) and reasonable specificity (71.96%), along with good precision (81.48%) and F1 score (86.96%). These findings highlight the effectiveness of the proposed CAD system in accurately segmenting and diagnosing PCa. This study represents a significant advancement in non-invasive methods for early detection and diagnosis of PCa, showcasing the potential of IVIM parameters in combination with machine learning techniques. This developed solution has the potential to revolutionize PCa diagnosis, leading to improved patient outcomes and reduced healthcare costs.

Functional MRI evaluation of blood oxygen dependent (BOLD) in renal allograft dysfunction: a prospective study.

BACKGROUND: Blood oxygen level dependent-magnetic resonance imaging (BOLD-MRI) is a non-invasive functional imaging technique that can be used to assess renal allograft dysfunction. PURPOSE: To evaluate the diagnostic performance of BOLD-MRI using a 3-T scanner in discriminating causes of renal allograft dysfunction in the post-transplant period. MATERIAL AND METHODS: This prospective study was conducted on 112 live donor-renal allograft recipients: 53 with normal graft function, as controls; 18 with biopsy-proven acute rejection (AR); and 41 with biopsy-proven acute tubular necrosis (ATN). Multiple fast-field echo sequences were performed to obtain T2*-weighted images. Cortical R2* (CR2*) level, medullary R2* (MR2*) level, and medullary over cortical R2* ratio (MCR) were measured in all participants. RESULTS: The mean MR2* level was significantly lower in the AR group (20.8 ± 2.8/s) compared to the normal group (24 ± 2.4/s, P <0.001) and ATN group (27.4 ± 1.7/s, P <0.001). The MCR was higher in ATN group (1.47 ± 0.18) compared to the AR group (1.18 ± 0.17) and normal functioning group (1.34 ± 0.2). Both MR2* (area under the curve [AUC] = 0.837, P <0.001) and MCR (AUC = 0.727, P = 0.003) can accurately discriminate ATN from AR, however CR2* (AUC = 0.590, P = 0.237) showed no significant difference between both groups. CONCLUSION: In early post-transplant renal dysfunction, BOLD-MRI is a valuable non-invasive diagnostic technique that can differentiate between AR and ATN by measuring changes in intra-renal tissue oxygenation.

Microscopic hematuria and pelvic ultrasonography could rule out flexible cystoscopy during surveillance for T1-low grade non-muscle invasive bladder cancer.

Purpose: Cystoscopy (rigid/flexible [FC]) is the standard surveillance tool for non-muscle invasive bladder cancer (NMIBC). Nevertheless, it has its drawbacks. The objective of this study is to evaluate the performance of microscopic hematuria (MH), abdominal ultrasonography (US), and urine cytology (UC) as potential substitutes for FC in patients with T1-low-grade (T1-LG) NMIBC. Methods: Over a 12-month period, patients attending our tertiary referral center for T1-LG NMIBC follow-up underwent urine analysis for MH and UC, and then US and FC were performed as outpatient surveillance procedures. Those with positive findings underwent inpatient rigid cystoscopy under anesthesia and biopsy. The negative predictive values (NPV) and sensitivity of different combinations of MH, UC, US, and FC were compared with the standard histopathology. Results: In 218 evaluated patients, FC had the highest NPV (97.9%). However, this figure showed no statistically significant difference if compared with the combination of negative MH and US (93.8%) (difference = 0.04, p = 0.1) or the combination of MH, US, and UC (94.9%) (difference = 0.03, p = 0.2). The reported sensitivity results were similarly comparable between FC (94.2%) and the aforementioned combinations (90.4% and 92.3%; differences: 0.038 and 0.019; p = 0.4 and 0.7, respectively). Conclusions: During the surveillance of NMIBC for patients diagnosed with T1-LG disease, the combination of MH/US has comparable sensitivity and NPV with FC. This non-invasive combination could be considered the first station that might preclude the need for FC in a considerable percentage of this group of patients.

Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review.

Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients' clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients' clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.

A New Framework for Precise Identification of Prostatic Adenocarcinoma.

Prostate cancer, which is also known as prostatic adenocarcinoma, is an unconstrained growth of epithelial cells in the prostate and has become one of the leading causes of cancer-related death worldwide. The survival of patients with prostate cancer relies on detection at an early, treatable stage. In this paper, we introduce a new comprehensive framework to precisely differentiate between malignant and benign prostate cancer. This framework proposes a noninvasive computer-aided diagnosis system that integrates two imaging modalities of MR (diffusion-weighted (DW) and T2-weighted (T2W)). For the first time, it utilizes the combination of functional features represented by apparent diffusion coefficient (ADC) maps estimated from DW-MRI for the whole prostate in combination with texture features with its first- and second-order representations, extracted from T2W-MRIs of the whole prostate, and shape features represented by spherical harmonics constructed for the lesion inside the prostate and integrated with PSA screening results. The dataset presented in the paper includes 80 biopsy confirmed patients, with a mean age of 65.7 years (43 benign prostatic hyperplasia, 37 prostatic carcinomas). Experiments were conducted using different well-known machine learning approaches including support vector machines (SVM), random forests (RF), decision trees (DT), and linear discriminant analysis (LDA) classification models to study the impact of different feature sets that lead to better identification of prostatic adenocarcinoma. Using a leave-one-out cross-validation approach, the diagnostic results obtained using the SVM classification model along with the combined feature set after applying feature selection (88.75% accuracy, 81.08% sensitivity, 95.35% specificity, and 0.8821 AUC) indicated that the system's performance, after integrating and reducing different types of feature sets, obtained an enhanced diagnostic performance compared with each individual feature set and other machine learning classifiers. In addition, the developed diagnostic system provided consistent diagnostic performance using 10-fold and 5-fold cross-validation approaches, which confirms the reliability, generalization ability, and robustness of the developed system.

A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens.

Prostate cancer is a significant cause of morbidity and mortality in the USA. In this paper, we develop a computer-aided diagnostic (CAD) system for automated grade groups (GG) classification using digitized prostate biopsy specimens (PBSs). Our CAD system aims to firstly classify the Gleason pattern (GP), and then identifies the Gleason score (GS) and GG. The GP classification pipeline is based on a pyramidal deep learning system that utilizes three convolution neural networks (CNN) to produce both patch- and pixel-wise classifications. The analysis starts with sequential preprocessing steps that include a histogram equalization step to adjust intensity values, followed by a PBSs' edge enhancement. The digitized PBSs are then divided into overlapping patches with the three sizes: 100 × 100 (CNNS), 150 × 150 (CNNM), and 200 × 200 (CNNL), pixels, and 75% overlap. Those three sizes of patches represent the three pyramidal levels. This pyramidal technique allows us to extract rich information, such as that the larger patches give more global information, while the small patches provide local details. After that, the patch-wise technique assigns each overlapped patch a label as GP categories (1 to 5). Then, the majority voting is the core approach for getting the pixel-wise classification that is used to get a single label for each overlapped pixel. The results after applying those techniques are three images of the same size as the original, and each pixel has a single label. We utilized the majority voting technique again on those three images to obtain only one. The proposed framework is trained, validated, and tested on 608 whole slide images (WSIs) of the digitized PBSs. The overall diagnostic accuracy is evaluated using several metrics: precision, recall, F1-score, accuracy, macro-averaged, and weighted-averaged. The (CNNL) has the best accuracy results for patch classification among the three CNNs, and its classification accuracy is 0.76. The macro-averaged and weighted-average metrics are found to be around 0.70-0.77. For GG, our CAD results are about 80% for precision, and between 60% to 80% for recall and F1-score, respectively. Also, it is around 94% for accuracy and NPV. To highlight our CAD systems' results, we used the standard ResNet50 and VGG-16 to compare our CNN's patch-wise classification results. As well, we compared the GG's results with that of the previous work.

Precise Identification of Prostate Cancer from DWI Using Transfer Learning.

Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: The proposed system first uses non-negative matrix factorization (NMF) to integrate three different types of features for the accurate segmentation of prostate regions. Then, discriminatory features in the form of apparent diffusion coefficient (ADC) volumes are estimated from the segmented regions. The ADC maps that constitute these volumes are labeled by a radiologist to identify the ADC maps with malignant or benign tumors. Finally, transfer learning is used to fine-tune two different previously-trained convolutional neural network (CNN) models (AlexNet and VGGNet) for detecting and identifying prostate cancer. Results: Multiple experiments were conducted to evaluate the accuracy of different CNN models using DWI datasets acquired at nine distinct b-values that included both high and low b-values. The average accuracy of AlexNet at the nine b-values was 89.2±1.5% with average sensitivity and specificity of 87.5±2.3% and 90.9±1.9%. These results improved with the use of the deeper CNN model (VGGNet). The average accuracy of VGGNet was 91.2±1.3% with sensitivity and specificity of 91.7±1.7% and 90.1±2.8%. Conclusions: The results of the conducted experiments emphasize the feasibility and accuracy of the developed system and the improvement of this accuracy using the deeper CNN.

Imaging of Bladder Cancer: Standard Applications and Future Trends.

The evolution in imaging has had an increasing role in the diagnosis, staging and follow up of bladder cancer. Conventional cystoscopy is crucial in the diagnosis of bladder cancer. However, a cystoscopic procedure cannot always depict carcinoma in situ (CIS) or differentiate benign from malignant tumors prior to biopsy. This review will discuss the standard application, novel imaging modalities and their additive role in patients with bladder cancer. Staging can be performed with CT, but distinguishing between T1 and T2 BCa (bladder cancer) cannot be assessed. MRI can distinguish muscle-invasive from non-muscle-invasive tumors with accurate local staging. Vesical Imaging-Reporting and Data System (VI-RADS) score is a new diagnostic modality used for the prediction of tumor aggressiveness and therapeutic response. Bone scintigraphy is recommended in patients with muscle-invasive BCa with suspected bony metastases. CT shows low sensitivity for nodal staging; however, PET (Positron Emission Tomography)/CT is superior and highly recommended for restaging and determining therapeutic effect. PET/MRI is a new imaging technique in bladder cancer imaging and its role is promising. Texture analysis has shown significant steps in discriminating low-grade from high-grade bladder cancer. Radiomics could be a reliable method for quantitative assessment of the muscle invasion of bladder cancer.

Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey.

Prostate cancer is one of the most identified cancers and second most prevalent among cancer-related deaths of men worldwide. Early diagnosis and treatment are substantial to stop or handle the increase and spread of cancer cells in the body. Histopathological image diagnosis is a gold standard for detecting prostate cancer as it has different visual characteristics but interpreting those type of images needs a high level of expertise and takes too much time. One of the ways to accelerate such an analysis is by employing artificial intelligence (AI) through the use of computer-aided diagnosis (CAD) systems. The recent developments in artificial intelligence along with its sub-fields of conventional machine learning and deep learning provide new insights to clinicians and researchers, and an abundance of research is presented specifically for histopathology images tailored for prostate cancer. However, there is a lack of comprehensive surveys that focus on prostate cancer using histopathology images. In this paper, we provide a very comprehensive review of most, if not all, studies that handled the prostate cancer diagnosis using histopathological images. The survey begins with an overview of histopathological image preparation and its challenges. We also briefly review the computing techniques that are commonly applied in image processing, segmentation, feature selection, and classification that can help in detecting prostate malignancies in histopathological images.

Dynamic Contrast-Enhanced Magnetic Resonance Imaging as a Diagnostic Tool in the Assessment of Tumour Angiogenesis in Urinary Bladder Cancer.

PURPOSE: The aim of study is to assess the role of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and correlation with tumour angiogenesis in evaluation of urinary bladder cancer. MATERIAL AND METHODS: The study included 81 patients with recent presumed diagnosis of bladder tumour or who came for follow up after management of histopathologically proven bladder cancer. All had DCE-MRI with time-signal intensity curve. The radiologic results then correlated with the histopathologic results using both haematoxylin and eosin stain and immuno-histochemical staining for localization and evaluation of CD34 immunoreactivity as a detector for the microvessel density (MVD) and tumour angiogenesis. RESULTS: Seventy-one cases were pathologically proven to be malignant: 41 cases (58%) showed type III time-signal intensity curve (descending); 22 cases (31%) showed type II (plateau); and 8 cases (11%) showed type I (ascending) curve. The sensitivity of DCE-MRI in stage T1 bladder tumour was 80%; in stage T2, it was (90.9%); and in stage T3, it was (96.9%). Overall accuracy of DCE-MRI in tumour staging was 89.5% and P = .001 (significant). Values more than the cutoff value = 76.13 MVD are cystitis with sensitivity (90%), specificity (91%), and P value is .001, which is statistically highly significant. CONCLUSION: There is a strong positive association between DCE-MRI (staging and washout slope of the time-signal intensity curve) with histopathologic grade, tumour stage, and MVD in bladder cancer. So, DCE-MRI can be used as reliable technique in preoperative predictions of tumour behavior and affect the planning of antiangiogenetic therapy.

Deep Learning Role in Early Diagnosis of Prostate Cancer.

The objective of this work is to develop a computer-aided diagnostic system for early diagnosis of prostate cancer. The presented system integrates both clinical biomarkers (prostate-specific antigen) and extracted features from diffusion-weighted magnetic resonance imaging collected at multiple b values. The presented system performs 3 major processing steps. First, prostate delineation using a hybrid approach that combines a level-set model with nonnegative matrix factorization. Second, estimation and normalization of diffusion parameters, which are the apparent diffusion coefficients of the delineated prostate volumes at different b values followed by refinement of those apparent diffusion coefficients using a generalized Gaussian Markov random field model. Then, construction of the cumulative distribution functions of the processed apparent diffusion coefficients at multiple b values. In parallel, a K-nearest neighbor classifier is employed to transform the prostate-specific antigen results into diagnostic probabilities. Finally, those prostate-specific antigen-based probabilities are integrated with the initial diagnostic probabilities obtained using stacked nonnegativity constraint sparse autoencoders that employ apparent diffusion coefficient-cumulative distribution functions for better diagnostic accuracy. Experiments conducted on 18 diffusion-weighted magnetic resonance imaging data sets achieved 94.4% diagnosis accuracy (sensitivity = 88.9% and specificity = 100%), which indicate the promising results of the presented computer-aided diagnostic system.

Statistical analysis of ADCs and clinical biomarkers in detecting acute renal transplant rejection.

OBJECTIVE: The main goal of this study is to determine which parameters [e.g. clinical biomarkers, demographics and image-markers using 4D (3D + b-value) diffusion-weighted MRI (DW-MRI)] are more correlated with transplanted kidney status in patients who have undergone kidney transplantation, and can be used for early assessment of acute renal rejection. METHODS: The study included 16 patients with stable graft function and 37 patients with acute rejection (AR), determined by renal biopsy post-transplantation. 3D DW-MRI of each allograft had been acquired using a series of b-values 50 and 100-1000 in steps of 100 smm-2. The kidney was automatically segmented and co-aligned across series for motion correction using geometric deformable models. Volume-averaged apparent diffusion coefficients (ADCs) at each b-value were calculated. All possible subsets of ADC were used, along with patient age, sex, serum plasma creatinine (SPCr) and creatinine clearance (CrCl), as predictors in 211 logistic regression models where AR was the outcome variable. Predictive value of ADC at each b-value was assessed using its Akaike weight. RESULTS: ANOVA of the saturated model found that odds of AR depended significantly on SPCr, CrCl and ADC at b = 500, 600, 700 and 900 smm-2. The model incorporating ADC at b = 100 and700 smm-2 had the lowest value of the Akaike information criterion; the same two b-values also had the greatest Akaike weights. For comparison, the top 10 submodels and the full model were reported. CONCLUSION: Preliminary findings suggest that ADC provides improved detection of AR than lab values alone. At least two non-zero gradient strengths should be used for optimal results. Advances in knowledge: This paper investigated possible correlations between image-based and clinical biomarkers, and the fusion of both with respect to biopsy diagnosis of AR.

Cloacal Duplication: Single-center Experience in the Management of a Rare Anomaly.

Cloacal duplication is an exceedingly rare group of anomalies with a limited number of cases reported so far. The anomaly may be confined to partial bladder duplication or it may involve complete duplication of the urogenital tract, hindgut, spine, lower limbs, and vascular structures. Every case is unique and ought to be approached individually. By means of imaging studies and endoscopy, anatomic details should be carefully defined before endorsing surgical correction. A satisfactory outcome can be achieved in the majority of cases. In this report, we describe 3 girls with cloacal duplication, and review pertinent imaging and surgical management.

Models and methods for analyzing DCE-MRI: a review.

PURPOSE: To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches. METHODS: DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases. RESULTS: Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors. CONCLUSIONS: Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.

Can computerized tomography accurately stage childhood renal tumors?

PURPOSE: Staging of childhood renal tumors is crucial for treatment planning and outcome prediction. We sought to identify whether computerized tomography could accurately predict the local stage of childhood renal tumors. MATERIALS AND METHODS: We retrospectively reviewed our database for patients diagnosed with childhood renal tumors and treated surgically between 1990 and 2013. Inability to retrieve preoperative computerized tomography, intraoperative tumor spillage and nonWilms childhood renal tumors were exclusion criteria. Local computerized tomography stage was assigned by a single experienced pediatric radiologist blinded to the pathological stage, using a consensus similar to the Children's Oncology Group Wilms tumor staging system. Tumors were stratified into up-front surgery and preoperative chemotherapy groups. The radiological stage of each tumor was compared to the pathological stage. RESULTS: A total of 189 tumors in 179 patients met inclusion criteria. Computerized tomography staging matched pathological staging in 68% of up-front surgery (70 of 103), 31.8% of pre-chemotherapy (21 of 66) and 48.8% of post-chemotherapy scans (42 of 86). Computerized tomography over staged 21.4%, 65.2% and 46.5% of tumors in the up-front surgery, pre-chemotherapy and post-chemotherapy scans, respectively, and under staged 10.7%, 3% and 4.7%. Computerized tomography staging was more accurate in tumors managed by up-front surgery (p <0.001) and those without extracapsular extension (p <0.001). CONCLUSIONS: The validity of computerized tomography staging of childhood renal tumors remains doubtful. This staging is more accurate for tumors treated with up-front surgery and those without extracapsular extension. Preoperative computerized tomography can help to exclude capsular breach. Treatment strategy should be based on surgical and pathological staging to avoid the hazards of inaccurate staging.

In-vitro and in-vivo diagnostic techniques for prostate cancer: a review.

This paper overviews one of the most important, interesting, and challenging problems in oncology, early diagnosis of prostate cancer. Developing effective diagnostic techniques for prostate cancer is of great clinical importance and can improve the effectiveness of treatment and increase the patient's chance of survival. The main focus of this study is to overview the different in-vitro and in-vivo technologies for diagnosing prostate cancer. This review discusses the current clinically used in-vitro cancer diagnostic tools, such as biomarker tests and needle biopsies and including their applications, advantages, and limitations. Moreover, the current in-vitro research tools that focus on the role of nanotechnology in prostate cancer diagnosis have been detailed. In addition to the in-vitro techniques, the current study discusses in detail developed in-vivo non-invasive state-of-the-art Computer-Aided Diagnosis (CAD) systems for prostate cancer based on analyzing Transrectal Ultrasound (TRUS) and different types of magnetic resonance imaging (MRI), e.g., T2-MRI, Diffusion Weighted Imaging (DWI), Dynamic Contrast Enhanced (DCE)-MRI, and multi-parametric MRI, focusing on their implementation, experimental procedures, and reported outcomes. Furthermore, the paper addresses the limitations of the current prostate cancer diagnostic techniques, outlines the challenges that these techniques face, and introduces the recent trends to solve these challenges, which include biomarkers used in in-vitro lab-on-a-chip nanotechnology-based methods.

A comprehensive non-invasive framework for automated evaluation of acute renal transplant rejection using DCE-MRI.

The objective was to develop a novel and automated comprehensive framework for the non-invasive identification and classification of kidney non-rejection and acute rejection transplants using 2D dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The proposed approach consists of four steps. First, kidney objects are segmented from the surrounding structures with a geometric deformable model. Second, a non-rigid registration approach is employed to account for any local kidney deformation. In the third step, the cortex of the kidney is extracted in order to determine dynamic agent delivery, since it is the cortex that is primarily affected by the perfusion deficits that underlie the pathophysiology of acute rejection. Finally, we use an analytical function-based model to fit the dynamic contrast agent kinetic curves in order to determine possible rejection candidates. Five features that map the data from the original data space to the feature space are chosen with a k-nearest-neighbor (KNN) classifier to distinguish between acute rejection and non-rejection transplants. Our study includes 50 transplant patients divided into two groups: 27 patients with stable kidney function and the remainder with impaired kidney function. All of the patients underwent DCE-MRI, while the patients in the impaired group also underwent ultrasound-guided fine needle biopsy. We extracted the kidney objects and the renal cortex from DCE-MRI for accurate medical evaluation with an accuracy of 0.97 ± 0.02 and 0.90 ± 0.03, respectively, using the Dice similarity metric. In a cohort of 50 participants, our framework classified all cases correctly (100%) as rejection or non-rejection transplant candidates, which is comparable to the gold standard of biopsy but without the associated deleterious side-effects. Both the 95% confidence interval (CI) statistic and the receiver operating characteristic (ROC) analysis document the ability to separate rejection and non-rejection groups. The average plateau (AP) signal magnitude and the gamma-variate model functional parameter α have the best individual discriminating characteristics.

Transurethral resection of ejaculatory duct in infertile men: outcome and predictors of success.

OBJECTIVES: To evaluate the outcome of transurethral resection of the ejaculatory duct (TURED) in the treatment for ejaculatory duct obstruction (EDO) and define predictors of success. MATERIALS AND METHODS: We retrospectively evaluated 23 infertile men between 2006 and 20011, who were diagnosed as having EDO. Inclusion criteria were azoospermia or oligozoospermia, low ejaculate volume, low ejaculate PH, little or no fructose in seminal plasma with normal serum levels of gonadotropins and testosterone and evidence of obstruction on transrectal ultrasonography (TRUS) or magnetic resonance images (MRI). Seventeen patients were diagnosed as complete EDO, and the remaining 6 were considered as having partial EDO. All patients were treated by TURED. RESULTS: Midline cysts were diagnosed in seven cases, and the remaining 16 patients had postinflammatory obstruction of ejaculatory ducts (ED). Overall, a significant improvement of semen quality was achieved after surgery. All patients with partial EDO showed improvements in semen parameters after TURED compared to 23.5% (4/17) in those with complete EDO. Improvement in sperm count was 71.5% and 31% for patients with midline cysts and patients with non-cystic causes of EDO, respectively. Six (26%) patients developed complications including epididymo-orchitis in 2, watery ejaculate in 3 and conversion to azoospermia in 1. Spontaneous pregnancies were achieved in 3 (13%) cases: 2 (33.3%) men with partial and 1 (5.9%) with complete obstruction. CONCLUSION: Partial EDO, whatever the etiology, has an excellent outcome after TURED. Complete EDO due to cysts appears to respond better than postinflammatory obstruction to TURED.

Diffusion-weighted magnetic resonance imaging in follow-up of superficial urinary bladder carcinoma after transurethral resection: initial experience.

UNLABELLED: What's known on the subject? and What does the study add? Diffusion-weighted (DW) MRI is a non-invasive technique measuring the microscopic mobility of water molecules in the tissues without contrast administration. It provides information on perfusion and diffusion simultaneously in any organ, so it can be used to differentiate normal and abnormal tissue structure, and it might help in the characterization of various abnormalities. In recent years, DW-MRI has been applied in the evaluation of urinary tract lesions, such as malignant renal, prostatic and bladder tumours; however, it has not previously been tested on its ability to distinguish residual cancer from fibrotic and inflammatory changes secondary to transurethral resection (TUR) and intravesical chemotherapy, both of which manifest as bladder-wall thickening on T2-weighted MRI. This is the first study to show the feasibility of DW-MRI in follow-up of patients with superficial bladder tumours after TUR. DW-MRI was highly reliable in differentiating post-TUR inflammatory changes from bladder tumours, with results similar to those of conventional cystoscopy. This non-invasive method could be used efficiently in future for follow-up of this patient group and may obviate the need for routine cystoscopy. OBJECTIVE: • To study the feasibility of using diffusion-weighted (DW) magnetic resonance imaging (MRI) in bladder cancer follow-up after transurethral resection (TUR). PATIENTS AND METHODS: • Included in the study were 47 patients with a history of TUR of superficial bladder carcinoma, who were admitted to our centre between January and December 2011 for follow-up cystoscopy. • Before cystoscopy, DW-MRI was performed and the apparent diffusion coefficient (ADC) value was measured in a circular region of interest within the carcinoma and normal bladder wall. • Two radiologists, blinded to the results of cystoscopy, independently interpreted the DW images. • A comparison of imaging findings with those of cystoscopy was performed using the McNemar test. RESULTS: • In our 47 patients, cystoscopy identified 34 bladder lesions in 24 patients and in the remaining 23 the bladder looked normal. • In the 24 patients with malignant bladders, DW-MRI detected 32/34 tumours with two false-negative findings of lesions in two patients. • In 23 patients with non-malignant bladders, the DW-MRI data were accurate for 21 patients, as two patients were misdiagnosed as malignant. • The sensitivity, specificity, accuracy, positive and negative predictive values of DW-MRI for identifying bladder tumours were 91.6% (22/24), 91.3% (21/23), 91.5% (43/47), 91.6 (22/24) and 91.3 (21/23), respectively. • Using the McNemar test there was no significant difference between DW-MRI and cystoscopy. CONCLUSIONS: • DW-MRI has a high reliability in differentiating post-TUR inflammatory changes from bladder tumours, which is similar to that of cystoscopy. • DW-MRI could be a first-line diagnostic test in follow-up of patients after TUR.

Radiological diagnosis of vesicouterine fistula: role of magnetic resonance imaging.

PURPOSE: To retrospectively assess the value of magnetic resonance imaging (MRI) in the diagnosis of vesicouterine fistula (VUF). MATERIALS AND METHODS: Between January 2003 and January 2011, 12 patients with a diagnosis of VUF were surgically managed at our center; among them, eight patients had MRI among their preoperative radiological investigations and those were included in our study. The clinical presentation, radiological investigations, and surgical findings of the patients were reviewed. RESULTS: The mean age of the patients was 31 years. Seven of the eight patients had complaints of cyclic hematuria and the remaining patient complained of urinary leakage through the vagina. The etiology of VUF was cesarean section in all patients. The preoperative radiological investigations included conventional cystography in five patients, intravenous urography in two, computed tomography (CT) urography in two, and MRI in eight. The sensitivities of diagnosis for these investigations were 40%, 0%, 50%, and 100%, respectively. CONCLUSION: In our small retrospective series, pelvic MRI was reliable and sensitive for diagnosis of VUF. It should be considered in the work-up of patients with suspected VUF.