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Objective: To evaluate the performance of quantitative computed tomography (CT) texture analysis using different machine learning (ML) classifiers for discriminating low and high nuclear grade clear cell renal cell carcinomas (cc-RCCs).Materials and Methods: This retrospective study included 53 patients with pathologically proven 54 cc-RCCs (31 low-grade [grade 1 or 2]; 23 high-grade [grade 3 or 4]). In one patient, two synchronous cc-RCCs were included in the analysis. Mean age was 57.5 years. Thirty-four (64.1%) patients were male and 19 were female (35.9%). Mean tumour size based on the maximum diameter was 57.4 mm (range, 16-145 mm). Forty patients underwent radical nephrectomy and 13 underwent partial nephrectomy. Following pre-processing steps, two-dimensional CT texture features were extracted using portal-phase contrast-enhanced CT. Reproducibility of texture features was assessed with the intra-class correlation coefficient (ICC). Nested cross-validation with a wrapper-based algorithm was used in feature selection and model optimisation. The ML classifiers were support vector machine (SVM), multilayer perceptron (MLP, a sort of neural network), naïve Bayes, k-nearest neighbours, and random forest. The performance of the classifiers was compared by certain metrics.Results: Among 279 texture features, 241 features with an ICC equal to or higher than 0.80 (excellent reproducibility) were included in the further feature selection process. The best model was created using SVM. The selected subset of features for SVM included five co-occurrence matrix (ICC range, 0.885-0.998), three run-length matrix (ICC range, 0.889-0.992), one gradient (ICC = 0.998), and four Haar wavelet features (ICC range, 0.941-0.997). The overall accuracy, sensitivity (for detecting high-grade cc-RCCs), specificity (for detecting high-grade cc-RCCs), and overall area under the curve of the best model were 85.1%, 91.3%, 80.6%, and 0.860, respectively.Conclusions: The ML-based CT texture analysis can be a useful and promising non-invasive method for prediction of low and high Fuhrman nuclear grade cc-RCCs.Key Points: • Based on the percutaneous biopsy literature, ML-based CT texture analysis has a comparable predictive performance with percutaneous biopsy. • Highest predictive performance was obtained with use of the SVM. • SVM correctly classified 85.1% of cc-RCCs in terms of nuclear grade, with an AUC of 0.860. [ABSTRACT FROM AUTHOR]

Objective: To develop externally validated, reproducible, and generalizable models for distinguishing three major subtypes of renal cell carcinomas (RCCs) using machine learning-based quantitative computed tomography (CT) texture analysis (qCT-TA).Materials and Methods: Sixty-eight RCCs were included in this retrospective study for model development and internal validation. Another 26 RCCs were included from public databases (The Cancer Genome Atlas-TCGA) for independent external validation. Following image preparation steps (reconstruction, resampling, normalization, and discretization), 275 texture features were extracted from unenhanced and corticomedullary phase CT images. Feature selection was firstly done with reproducibility analysis by three radiologists, and; then, with a wrapper-based classifier-specific algorithm. A nested cross-validation was performed for feature selection and model optimization. Base classifiers were the artificial neural network (ANN) and support vector machine (SVM). Base classifiers were also combined with three additional algorithms to improve generalizability performance. Classifications were done with the following groups: (i), non-clear cell RCC (non-cc-RCC) versus clear cell RCC (cc-RCC) and (ii), cc-RCC versus papillary cell RCC (pc-RCC) versus chromophobe cell RCC (chc-RCC). Main performance metric for comparisons was the Matthews correlation coefficient (MCC).Results: Number of the reproducible features is smaller for the unenhanced images (93 out of 275) compared to the corticomedullary phase images (232 out of 275). Overall performance metrics of the machine learning-based qCT-TA derived from corticomedullary phase images were better than those of unenhanced images. Using corticomedullary phase images, ANN with adaptive boosting algorithm performed best for discrimination of non-cc-RCCs from cc-RCCs (MCC = 0.728) with an external validation accuracy, sensitivity, and specificity of 84.6%, 69.2%, and 100%, respectively. On the other hand, the performance of the machine learning-based qCT-TA is rather poor for distinguishing three major subtypes. The SVM with bagging algorithm performed best for discrimination of pc-RCC from other RCC subtypes (MCC = 0.804) with an external validation accuracy, sensitivity, and specificity of 69.2%, 71.4%, and 100%, respectively.Conclusions: Machine learning-based qCT-TA can distinguish non-cc-RCCs from cc-RCCs with a satisfying performance. On the other hand, the performance of the method for distinguishing three major subtypes is rather poor. Corticomedullary phase CT images provide much more valuable texture parameters than unenhanced images. [ABSTRACT FROM AUTHOR]

Purpose: Despite a considerable amount of literature that has been published about the use of intrathecal gadolinium-enhanced MR cisternography (IGE-MRC), there is still relatively lack of evidence as to its long-term effects. Our purpose in this study was twofold: firstly, to assess the long-term safety of the IGE-MRC; secondly, to evaluate the diagnostic performance of IGE-MRC for detecting cerebrospinal fluid (CSF) leak in otorhinorrhea patients.Methods: We retrospectively reviewed our imaging and clinical database for the patients admitted to our department for the evaluation of their otorhinorrhea between 2008 and 2017. Two radiologists evaluated the imaging studies independently. Consensus data was used in the analysis. Medical record review and phone call were used for the long-term follow-up.Results: The retrospective review yielded 166 patients. Rhinorrhea was present in 150 (90.4%) patients and otorrhea in 16 (9.6%) patients. Overall, 67 patients (40.5% of all patients) underwent operation for repair of the CSF leak site. Beta-transferrin test was available and positive in 57 (34.3%) patients. Overall sensitivity of IGE-MRC and paranasal high-resolution CT (HRCT) was 89.3 and 72%, respectively. Within the first 24 h after the procedure, none of the patients experienced a significant complication or adverse reaction. Ninety-nine patients (59.6%) had medical record and telephone follow-up. The mean follow-up duration with call was 37.1 months. Three (3%) patients complained about severe headache 3-4 weeks after the procedure.Conclusions: IGE-MRC is a minimally invasive and highly sensitive imaging technique. No adverse side effect during our long-term follow-up might strengthen and support the safety of IGE-MRC. [ABSTRACT FROM AUTHOR]

Objectives: The imaging and surgical literature has confusing association rates for the association between sporadic intracranial cavernous malformations (CMs) and developmental venous anomalies (DVAs). In this study, our purpose was to determine the association rate using ultra-high-resolution C-arm flat-detector CT angiography (FDCTA) and compare it with literature.Methods: Fifty-eight patients with 60 sporadic intracranial CMs that underwent an FDCTA study were included in our retrospective study. Re-evaluation of radiological data was performed based on the criteria defined by authors. Isotropic volumetric reconstructions with ultra-high resolution (voxel size of 102 μm3 for initial; 67 μm3 and 32 μm3 for further evaluation) were used for assessment. Sixteen patients underwent surgery for excision of their CMs.Results: Fifty-one of all patients (87.9 %) were associated with a DVA. Undefined local venous structures (UD-LVSs) were observed in the remaining 7 patients (12.1 %). The strength of interobserver agreement was excellent [kappa(k) coefficient = 0.923].Conclusions: Ultra-high-resolution FDCTA evaluation of CMs and DVAs reveals 3-fold higher association rate compared to the literature. FDCTA for patients with sporadic CMs could help identify the associated DVAs that remained undetected or unclear with other imaging modalities, which can be useful in decision-making processes, planning surgery, and during operation.Key Points: • FDCTA evaluation reveals the highest (3-fold) association rate in literature • FDCTA helps detect and define DVAs that remained unclear with other modalities • Ratio of DVAs/UD-LVSs ("variants" in MRI) increases dramatically with FDCTA • FDCTA reveals venous angioarchitecture of CMs in high anatomical detail • FDCTA can be useful in decision-making, planning surgery, and during operation. [ABSTRACT FROM AUTHOR]

Posterior communicating artery (PCoA) itself is an unusual location for intracranial aneurysms in that isolated dissections or dissecting aneurysms are extremely rare. In the way of correct diagnosis of dissecting aneurysms of PCoA itself, a proper understanding of (1) the anatomy of the PCoA and its perforator branches, (2) some particular diagnostic features, and (3) related clinical aspects is of significant importance. Although there are no established treatment strategies for this particular type of aneurysms, the endovascular approach might be considered as a plausible one. In this paper, our scope was to report five cases with dissecting aneurysm of the PCoA itself and to discuss this rare vascular pathology from anatomical, diagnostic, clinical, and therapeutical perspectives. [ABSTRACT FROM AUTHOR]

Discusses how gemfibrozil-statin and cerivastatin cause rhabdomyolysis and how rhabdomyolysis can cause renal failure. Severe case of rhabdomyolysis caused by cerivastatin-gemfibrozil combination; Drug-drug interaction of cervistatin.

Objective: To determine the incidence of adrenal incidentalomas (AIs) in a single-center series of living renal donors, to describe an evaluation algorithm for AIs in this patient population, and to compare the complication rates of hand-assisted laparoscopic donor nephrectomy (HALDN) with those of combined HALDN and adrenalectomy.Methods: We performed a single-center, retrospective study of consecutive living kidney donors who underwent laparoscopic nephrectomy for transplantation, with or without simultaneous ipsilateral adrenalectomy, between January 2008 and September 2014.Results: During the study period, AIs were detected in 18 of 1033 potential living renal donors who underwent computerized tomographic angiography. Application of additional donor selection criteria and of an adrenal mass assessment algorithm resulted in 849 HALDN, of which 13 were combined with simultaneous adrenalectomy. The hospital length of stay (2.4 vs 2.6 days), perioperative (0.025 vs 0.077), early (0.073 vs 0.077), and late (0.014 vs 0.077) postoperative complication rates, and conversion to open donor nephrectomy (0.008 vs 0.00) were not significantly different. Mean operative time was significantly longer in the adrenalectomy group. None of the adrenal masses were malignant.Conclusion: Here, we presented our algorithm to manage the living kidney donors with AIs. Although donor population with AIs was relatively small in number, simultaneous adrenalectomy and ipsilateral nephrectomy seemed to be technically safe and conferred no identifiable increased risk of malignancy for the kidney transplant donor, when the incidentaloma is nonfunctional and less than or equal to 4 cm as assessed by preoperative imaging. [ABSTRACT FROM AUTHOR]