Diagnostic value of PET imaging in clinically unresponsive patients.

Rapid advancements in the critical care management of acute brain injuries have facilitated the survival of numerous patients who may have otherwise succumbed to their injuries. The probability of conscious recovery hinges on the extent of structural brain damage and the level of metabolic and functional cerebral impairment, which remain challenging to assess via laboratory, clinical, or functional tests. Current research settings and guidelines highlight the potential value of fluorodeoxyglucose-PET (FDG-PET) for diagnostic and prognostic purposes, emphasizing its capacity to consistently illustrate a metabolic reduction in cerebral glucose uptake across various disorders of consciousness. Crucially, FDG-PET might be a pivotal tool for differentiating between patients in the minimally conscious state and those in the unresponsive wakefulness syndrome, a persistent clinical challenge. In patients with disorders of consciousness, PET offers utility in evaluating the degree and spread of functional disruption, as well as identifying irreversible neural damage. Further, studies that capture responses to external stimuli can shed light on residual or revived brain functioning. Nevertheless, the validity of these findings in predicting clinical outcomes calls for additional long-term studies with larger patient cohorts suffering from consciousness impairment. Misdiagnosis of conscious illnesses during bedside clinical assessments remains a significant concern. Based on the clinical research settings, current clinical guidelines recommend PET for diagnostic and/or prognostic purposes. This review article discusses the clinical categories of conscious disorders and the diagnostic and prognostic value of PET imaging in clinically unresponsive patients, considering the known limitations of PET imaging in such contexts.

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.

Biomarkers for Kidney-Transplant Rejection: A Short Review Study.

Kidney transplantation is the preferred treatment for end-stage renal failure, but the limited availability of donors and the risk of immune rejection pose significant challenges. Early detection of acute renal rejection is a critical step to increasing the lifespan of the transplanted kidney. Investigating the clinical, genetic, and histopathological markers correlated to acute renal rejection, as well as finding noninvasive markers for early detection, is urgently needed. It is also crucial to identify which markers are associated with different types of acute renal rejection to manage treatment effectively. This short review summarizes recent studies that investigated various markers, including genomics, histopathology, and clinical markers, to differentiate between different types of acute kidney rejection. Our review identifies the markers that can aid in the early detection of acute renal rejection, potentially leading to better treatment and prognosis for renal-transplant patients.

Kidney Segmentation from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Integrating Deep Convolutional Neural Networks and Level Set Methods.

The dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technique has taken on a significant and increasing role in diagnostic procedures and treatments for patients who suffer from chronic kidney disease. Careful segmentation of kidneys from DCE-MRI scans is an essential early step towards the evaluation of kidney function. Recently, deep convolutional neural networks have increased in popularity in medical image segmentation. To this end, in this paper, we propose a new and fully automated two-phase approach that integrates convolutional neural networks and level set methods to delimit kidneys in DCE-MRI scans. We first develop two convolutional neural networks that rely on the U-Net structure (UNT) to predict a kidney probability map for DCE-MRI scans. Then, to leverage the segmentation performance, the pixel-wise kidney probability map predicted from the deep model is exploited with the shape prior information in a level set method to guide the contour evolution towards the target kidney. Real DCE-MRI datasets of 45 subjects are used for training, validating, and testing the proposed approach. The valuation results demonstrate the high performance of the two-phase approach, achieving a Dice similarity coefficient of 0.95 ± 0.02 and intersection over union of 0.91 ± 0.03, and 1.54 ± 1.6 considering a 95% Hausdorff distance. Our intensive experiments confirm the potential and effectiveness of that approach over both UNT models and numerous recent level set-based methods.

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.

Level-Set-Based Kidney Segmentation from DCE-MRI Using Fuzzy Clustering with Population-Based and Subject-Specific Shape Statistics.

The segmentation of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) of the kidney is a fundamental step in the early and noninvasive detection of acute renal allograft rejection. In this paper, a new and accurate DCE­MRI kidney segmentation method is proposed. In this method, fuzzy c-means (FCM) clustering is embedded into a level set method, with the fuzzy memberships being iteratively updated during the level set contour evolution. Moreover, population­based shape (PB-shape) and subject-specific shape (SS-shape) statistics are both exploited. The PB-shape model is trained offline from ground-truth kidney segmentations of various subjects, whereas the SS-shape model is trained on the fly using the segmentation results that are obtained for a specific subject. The proposed method was evaluated on the real medical datasets of 45 subjects and reports a Dice similarity coefficient (DSC) of 0.953 ± 0.018, an intersection-over-union (IoU) of 0.91 ± 0.033, and 1.10 ± 1.4 in the 95-percentile of Hausdorff distance (HD95). Extensive experiments confirm the superiority of the proposed method over several state-of-the-art level set methods, with an average improvement of 0.7 in terms of HD95. It also offers an HD95 improvement of 9.5 and 3.8 over two deep neural networks based on the U-Net architecture. The accuracy improvements have been experimentally found to be more prominent on low-contrast and noisy images.

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.

Variational Approach for Joint Kidney Segmentation and Registration from DCE-MRI Using Fuzzy Clustering with Shape Priors.

The dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technique has great potential in the diagnosis, therapy, and follow-up of patients with chronic kidney disease (CKD). Towards that end, precise kidney segmentation from DCE-MRI data becomes a prerequisite processing step. Exploiting the useful information about the kidney's shape in this step mandates a registration operation beforehand to relate the shape model coordinates to those of the image to be segmented. Imprecise alignment of the shape model induces errors in the segmentation results. In this paper, we propose a new variational formulation to jointly segment and register DCE-MRI kidney images based on fuzzy c-means clustering embedded within a level-set (LSet) method. The image pixels' fuzzy memberships and the spatial registration parameters are simultaneously updated in each evolution step to direct the LSet contour toward the target kidney. Results on real medical datasets of 45 subjects demonstrate the superior performance of the proposed approach, reporting a Dice similarity coefficient of 0.94 ± 0.03, Intersection-over-Union of 0.89 ± 0.05, and 2.2 ± 2.3 in 95-percentile of Hausdorff distance. Extensive experiments show that our approach outperforms several state-of-the-art LSet-based methods as well as two UNet-based deep neural models trained for the same task in terms of accuracy and consistency.

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.

3D kidney segmentation from abdominal diffusion MRI using an appearance-guided deformable boundary.

A new technique for more accurate automatic segmentation of the kidney from its surrounding abdominal structures in diffusion-weighted magnetic resonance imaging (DW-MRI) is presented. This approach combines a new 3D probabilistic shape model of the kidney with a first-order appearance model and fourth-order spatial model of the diffusion-weighted signal intensity to guide the evolution of a 3D geometric deformable model. The probabilistic shape model was built from labeled training datasets to produce a spatially variant, independent random field of region labels. A Markov-Gibbs random field spatial model with up to fourth-order interactions was adequate to capture the inhomogeneity of renal tissues in the DW-MRI signal. A new analytical approach estimated the Gibbs potentials directly from the DW-MRI data to be segmented, in order that the segmentation procedure would be fully automatic. Finally, to better distinguish the kidney object from the surrounding tissues, marginal gray level distributions inside and outside of the deformable boundary were modeled with adaptive linear combinations of discrete Gaussians (first-order appearance model). The approach was tested on a cohort of 64 DW-MRI datasets with b-values ranging from 50 to 1000 s/mm2. The performance of the presented approach was evaluated using leave-one-subject-out cross validation and compared against three other well-known segmentation methods applied to the same DW-MRI data using the following evaluation metrics: 1) the Dice similarity coefficient (DSC); 2) the 95-percentile modified Hausdorff distance (MHD); and 3) the percentage kidney volume difference (PKVD). High performance of the new approach was confirmed by the high DSC (0.95±0.01), low MHD (3.9±0.76) mm, and low PKVD (9.5±2.2)% relative to manual segmentation by an MR expert (a board certified radiologist).

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.

Accurate Lungs Segmentation on CT Chest Images by Adaptive Appearance-Guided Shape Modeling.

To accurately segment pathological and healthy lungs for reliable computer-aided disease diagnostics, a stack of chest CT scans is modeled as a sample of a spatially inhomogeneous joint 3D Markov-Gibbs random field (MGRF) of voxel-wise lung and chest CT image signals (intensities). The proposed learnable MGRF integrates two visual appearance sub-models with an adaptive lung shape submodel. The first-order appearance submodel accounts for both the original CT image and its Gaussian scale space (GSS) filtered version to specify local and global signal properties, respectively. Each empirical marginal probability distribution of signals is closely approximated with a linear combination of discrete Gaussians (LCDG), containing two positive dominant and multiple sign-alternate subordinate DGs. The approximation is separated into two LCDGs to describe individually the lungs and their background, i.e., all other chest tissues. The second-order appearance submodel quantifies conditional pairwise intensity dependencies in the nearest voxel 26-neighborhood in both the original and GSS-filtered images. The shape submodel is built for a set of training data and is adapted during segmentation using both the lung and chest appearances. The accuracy of the proposed segmentation framework is quantitatively assessed using two public databases (ISBI VESSEL12 challenge and MICCAI LOLA11 challenge) and our own database with, respectively, 20, 55, and 30 CT images of various lung pathologies acquired with different scanners and protocols. Quantitative assessment of our framework in terms of Dice similarity coefficients, 95-percentile bidirectional Hausdorff distances, and percentage volume differences confirms the high accuracy of our model on both our database (98.4±1.0%, 2.2±1.0 mm, 0.42±0.10%) and the VESSEL12 database (99.0±0.5%, 2.1±1.6 mm, 0.39±0.20%), respectively. Similarly, the accuracy of our approach is further verified via a blind evaluation by the organizers of the LOLA11 competition, where an average overlap of 98.0% with the expert's segmentation is yielded on all 55 subjects with our framework being ranked first among all the state-of-the-art techniques compared.

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.