Successful Surgical Management of Locally Advanced Renal Cell Carcinoma Invading Spleen and Pancreas

Over the last two decades, the treatment of metastatic RCC has changed significantly, and the role of surgery is being debated. A 50-year-old man presented with pain in his left loin. An ultrasound, followed by a CT scan, revealed a 17.5 cm left renal mass invading the left suprarenal gland, spleen, and pancreatic tail. Radical nephrectomy through chevron incision under epidural block with general anesthesia was performed. The entire mass was removed en bloc. The estimated blood loss was 300 mL, and no blood transfusions were performed. The operation took approximately 2 h. Histological examination revealed clear cell renal carcinoma with extension into the spleen, pancreatic tail, and diaphragmatic fibers with negative resection margin. The patient discharged after a 3-day uneventful hospital stay. Aggressive surgical removal of a locally invasive renal cell carcinoma is feasible and should be considered in patients with good performance status and no or minimal distant metastases

Segmentation of Infant Brain Using Nonnegative Matrix Factorization

This study develops an atlas-based automated framework for segmenting infants\u27 brains from magnetic resonance imaging (MRI). For the accurate segmentation of different structures of an infant\u27s brain at the isointense age (6-12 months), our framework integrates features of diffusion tensor imaging (DTI) (e.g., the fractional anisotropy (FA)). A brain diffusion tensor (DT) image and its region map are considered samples of a Markov-Gibbs random field (MGRF) that jointly models visual appearance, shape, and spatial homogeneity of a goal structure. The visual appearance is modeled with an empirical distribution of the probability of the DTI features, fused by their nonnegative matrix factorization (NMF) and allocation to data clusters. Projecting an initial high-dimensional feature space onto a low-dimensional space of the significant fused features with the NMF allows for better separation of the goal structure and its background. The cluster centers in the latter space are determined at the training stage by the K-means clustering. In order to adapt to large infant brain inhomogeneities and segment the brain images more accurately, appearance descriptors of both the first-order and second-order are taken into account in the fused NMF feature space. Additionally, a second-order MGRF model is used to describe the appearance based on the voxel intensities and their pairwise spatial dependencies. An adaptive shape prior that is spatially variant is constructed from a training set of co-aligned images, forming an atlas database. Moreover, the spatial homogeneity of the shape is described with a spatially uniform 3D MGRF of the second-order for region labels. In vivo experiments on nine infant datasets showed promising results in terms of the accuracy, which was computed using three metrics: the 95-percentile modified Hausdorff distance (MHD), the Dice similarity coefficient (DSC), and the absolute volume difference (AVD). Both the quantitative and visual assessments confirm that integrating the proposed NMF-fused DTI feature and intensity MGRF models of visual appearance, the adaptive shape prior, and the shape homogeneity MGRF model is promising in segmenting the infant brain DTI

The Role of 3D CT Imaging in the Accurate Diagnosis of Lung Function in Coronavirus Patients

Early grading of coronavirus disease 2019 (COVID-19), as well as ventilator support machines, are prime ways to help the world fight this virus and reduce the mortality rate. To reduce the burden on physicians, we developed an automatic Computer-Aided Diagnostic (CAD) system to grade COVID-19 from Computed Tomography (CT) images. This system segments the lung region from chest CT scans using an unsupervised approach based on an appearance model, followed by 3D rotation invariant Markov–Gibbs Random Field (MGRF)-based morphological constraints. This system analyzes the segmented lung and generates precise, analytical imaging markers by estimating the MGRF-based analytical potentials. Three Gibbs energy markers were extracted from each CT scan by tuning the MGRF parameters on each lesion separately. The latter were healthy/mild, moderate, and severe lesions. To represent these markers more reliably, a Cumulative Distribution Function (CDF) was generated, then statistical markers were extracted from it, namely, 10th through 90th CDF percentiles with 10% increments. Subsequently, the three extracted markers were combined together and fed into a backpropagation neural network to make the diagnosis. The developed system was assessed on 76 COVID-19-infected patients using two metrics, namely, accuracy and Kappa. In this paper, the proposed system was trained and tested by three approaches. In the first approach, the MGRF model was trained and tested on the lungs. This approach achieved 95.83% accuracy and 93.39% kappa. In the second approach, we trained the MGRF model on the lesions and tested it on the lungs. This approach achieved 91.67% accuracy and 86.67% kappa. Finally, we trained and tested the MGRF model on lesions. It achieved 100% accuracy and 100% kappa. The results reported in this paper show the ability of the developed system to accurately grade COVID-19 lesions compared to other machine learning classifiers, such as k-Nearest Neighbor (KNN), decision tree, naïve Bayes, and random forest

Partial Nephrectomy for T1b/T2 Renal Mass: An Added Shift from Radical Nephrectomy

The aim of our study was to show our short-term experience in managing large renal masses (cT1b/T2) through partial nephrectomy (PN) over the last 3 years. Retrospective data collection for all patients managed by PN for renal masses larger than 4 cm over the last 3 years. Epidemiological data were collected. Surgical data including surgical and ischemic times as well as intra and postoperative complications were collected. Pre- and postoperative estimated glomerular filtration rate (eGFR) data were collected and correlated as well as postoperative complications and recurrence. We could identify 47 patients managed by PN for radiologically confirmed >4 cm renal masses. The mean age of the patients was 55.7 ± 13.4, including 29 males and 18 females. Masses were T1b and T2 in 40 and 7 patients, respectively. The mean tumor size was 6.2 ± 1.5 cm. Using renal nephrometry score; 8, 28, and 11 had low, moderate, and high complexity, respectively. Renal cell carcinoma (RCC) was identified in 42 patients. Five patients out of 42 cancerous cases (12%) had pathological T3 RCC. The mean preoperative and postoperative eGFR were 89.09 ± 12.41 and 88.50 ± 10.50, respectively (P 0.2). The median follow-up was 14 months and within that short time, no patient had evidence for cancer recurrence. PN for large renal masses is safe in experienced hands and should be attempted in a higher percentage of patients, regardless of the tumor complexity. No cancer recurrence or deterioration of renal function was observed within our short-term follow-up

Role of Angular Interface Sign in Characterizing Small Exophytic Renal Masses in Computed Tomography; Prospective Study

The widespread use of computed tomography (CT) has increased the incidence of small renal cell masses. We aimed to evaluate the usefulness of the angular interface sign (ice cream cone sign) to differentiate a broad spectrum of small renal masses using CT. The prospective study included CT images of patients with exophytic renal masses ≤ 4 cm in maximal dimension. The presence or absence of an angular interface of the renal parenchyma with the deep part of the renal mass was assessed. Correlation with the final pathological diagnosis was performed. The study included 116 patients with renal parenchymal masses of a mean (± SD) diameter of 28 (± 8.8) mm and a mean age of 47.7 (±12.8) years. The final diagnosis showed 101 neoplastic masses [66 renal cell carcinomas (RCC), 29 angiomyolipomas (AML), 3 lymphomas, and 3 oncocytomas] and 15 non-neoplastic masses [11 small abscesses, 2 complicated renal cysts, and 2 granulomas]. Angular interface sign was statistically comparable in neoplastic versus non-neoplastic lesions (37.6% versus 13.3%, respectively, P = 0.065). There was a statistically higher incidence of the sign when comparing benign versus malignant neoplastic masses (56.25 vs. 29%, respectively, P = 0.009). Also, comparing the sign in AML versus RCC was statistically significant (52% of AML versus 29% of RCC, P = 0.032). The angular interface sign seems beneficial in predicting the nature of small renal masses. The sign suggests benign rather than malignant small renal masses

Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images

The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support

Role of Angular Interface Sign in Characterizing Small Exophytic Renal Masses in Computed Tomography; Prospective Study

The widespread use of computed tomography (CT) has increased the incidence of small renal cell masses. We aimed to evaluate the usefulness of the angular interface sign (ice cream cone sign) to differentiate a broad spectrum of small renal masses using CT. The prospective study included CT images of patients with exophytic renal masses ≤ 4 cm in maximal dimension. The presence or absence of an angular interface of the renal parenchyma with the deep part of the renal mass was assessed. Correlation with the final pathological diagnosis was performed. The study included 116 patients with renal parenchymal masses of a mean (± SD) diameter of 28 (± 8.8) mm and a mean age of 47.7 (±12.8) years. The final diagnosis showed 101 neoplastic masses [66 renal cell carcinomas (RCC), 29 angiomyolipomas (AML), 3 lymphomas, and 3 oncocytomas] and 15 non-neoplastic masses [11 small abscesses, 2 complicated renal cysts, and 2 granulomas]. Angular interface sign was statistically comparable in neoplastic versus non-neoplastic lesions (37.6% versus 13.3%, respectively, P = 0.065). There was a statistically higher incidence of the sign when comparing benign versus malignant neoplastic masses (56.25 vs. 29%, respectively, P = 0.009). Also, comparing the sign in AML versus RCC was statistically significant (52% of AML versus 29% of RCC, P = 0.032). The angular interface sign seems beneficial in predicting the nature of small renal masses. The sign suggests benign rather than malignant small renal masses

The value of extended good quality transurethral resection of bladder tumour in the treatment of the newly diagnosed bladder cancer

Objective: To report our experience for the initial management of patients with newly diagnosed bladder tumours using our team approach for each case and using an aggressive extended transurethral resection of bladder tumour (TURBT) in order to investigate the real need for a routine ‘second-look’ cystoscopy in the current era. Patients and methods: The study included 50 consecutive patients admitted to the urology department, of our tertiary care centre, for management of newly diagnosed bladder cancer. Exclusion criteria included past history of bladder tumour, palpable mass on bimanual examination under anaesthesia, presence of residual tumour at the end of resection, and patients with tumours of the bladder dome as thorough resection is difficult to achieve in this area without an attendant risk. Patients that had pathologically confirmed carcinoma in situ were also excluded. White-light cystoscopy was used in all of the cases. Extended TURBT was defined as resection of the whole tumour, resection of the tumour base and 1 cm of apparently normal bladder wall around the circumference of the tumour. Results: The median (range) age of the patients was 52 (39–60) years. After initial TURBT, 10 patients (20%) were identified as having muscle-invasive bladder cancer. Of the remaining 40 patients, three had low-grade Ta disease, and so second biopsies were not taken. The remaining 37 patients had T1, grade 2–3 disease and none of them had evident residual disease at the site of tumour resection. Conclusion: Re-staging TURBT could be safely omitted for select groups of patients. An experienced surgeon and teamwork, together with an extended TURBT can accurately achieve complete tumour resection, with accurate tumour staging, on initial resection

Precise Segmentation of COVID-19 Infected Lung from CT Images Based on Adaptive First-Order Appearance Model with Morphological/Anatomical Constraints

A new segmentation technique is introduced for delineating the lung region in 3D computed tomography (CT) images. To accurately model the distribution of Hounsfield scale values within both chest and lung regions, a new probabilistic model is developed that depends on a linear combination of Gaussian (LCG). Moreover, we modified the conventional expectation-maximization (EM) algorithm to be run in a sequential way to estimate both the dominant Gaussian components (one for the lung region and one for the chest region) and the subdominant Gaussian components, which are used to refine the final estimated joint density. To estimate the marginal density from the mixed density, a modified k-means clustering approach is employed to classify the Gaussian subdominant components to determine which components belong properly to a lung and which components belong to a chest. The initial segmentation, based on the LCG-model, is then refined by the imposition of 3D morphological constraints based on a 3D Markov–Gibbs random field (MGRF) with analytically estimated potentials. The proposed approach was tested on CT data from 32 coronavirus disease 2019 (COVID-19) patients. Segmentation quality was quantitatively evaluated using four metrics: Dice similarity coefficient (DSC), overlap coefficient, 95th-percentile bidirectional Hausdorff distance (BHD), and absolute lung volume difference (ALVD), and it achieved 95.67±1.83%, 91.76±3.29%, 4.86±5.01, and 2.93±2.39, respectively. The reported results showed the capability of the proposed approach to accurately segment healthy lung tissues in addition to pathological lung tissues caused by COVID-19, outperforming four current, state-of-the-art deep learning-based lung segmentation approaches

Detection of Diabetic Retinopathy Using Extracted 3D Features from OCT Images

Diabetic retinopathy (DR) is a major health problem that can lead to vision loss if not treated early. In this study, a three-step system for DR detection utilizing optical coherence tomography (OCT) is presented. First, the proposed system segments the retinal layers from the input OCT images. Second, 3D features are extracted from each retinal layer that include the first-order reflectivity and the 3D thickness of the individual OCT layers. Finally, backpropagation neural networks are used to classify OCT images. Experimental studies on 188 cases confirm the advantages of the proposed system over related methods, achieving an accuracy of 96.81%, using the leave-one-subject-out (LOSO) cross-validation. These outcomes show the potential of the suggested method for DR detection using OCT images