Microwave ablation of synovial hypertrophy in recurrent monoarthritis: The results of extended cohort and long-term follow-up.

OBJECTIVES: Recurrent monoarthritis (RM) is a major challenge of many rheumatic diseases. Ablation is a well-known technique in the treatment of benign or malign lesions of different etiologies. We aimed to to investigate the success and safety of microwave ablation (MWA) as an adjunctive therapy in a cohort of medical treatment-resistant RM. METHODS: Patients with RM associated with different inflammatory diseases were included. MWA was performed after measuring the size of synovial hypertrophy with 15 or 20-watt power and different durations until microbubbles were shown indicating necrosis. Both clinical and radiologic data were recorded. RESULTS: We applied MWA in total of 24 knee joints of 10 female and 12 male patients aged between 22-71 years. Median intra-articular aspiration (IAA) need in the last 6 months before MWA was 5 (0-15). The median follow-up was 10 (3-16) months. Overall IAA count in the last 6 months before MWA in total of 144 months was 129 and decreased to 7 in post-MWA in total of 226 months (0.89 vs 0.03 per month, p< 0.001). The second MWA session was needed for 3 patients and a third session for 1. Functional disability and pain scores were improved significantly (median score from 9 to 1, p< 0.00001, in both). In magnetic resonance imaging, follow-up significant regression in synovial hypertrophy size was shown especially after 6th month. No complication was observed during the procedure or follow-up. CONCLUSION: As a less invasive technique compared with the surgical approach, MWA of synovial hypertrophy showed significant clinical improvement in RM safely. MWA seems promising as a treatment option candidate in the management of RM.

Effectiveness of Genicular Artery Embolization for Reducing Synovitis As Assessed by Contrast-Enhanced MR Imaging in Knee Osteoarthritis: A Pilot Study.

PURPOSE: To investigate the impact of Genicular Artery Embolization (GAE) on synovitis in knee osteoarthritis (OA) and assess its predictive role in pain response. MATERIALS AND METHODS: A single-center retrospective analysis was conducted on 35 contrast-enhanced MRI results from 33 patients treated with GAE for knee OA between December 2022 and March 2023. Assessments pre-procedure and at the 3-month post-embolization mark utilized a semi-quantitative scoring system for synovitis, referencing Guermazi et al.'s criteria from the MOST study. This included 11 knee points for comprehensive synovitis severity and distribution analysis, alongside evaluating the procedure's impact on pain and function through WOMAC and VAS scores. RESULTS: The study comprised 24 females (72.7%) and 9 males (27.3%), with a mean age of 59.1 years. Significant synovitis reduction was noted post-GAE, particularly in parapatellar and periligamentous areas. Synovial contrast scores significantly decreased from 5.1±2 to 2.9±2 at 3 months (p < 0.001), with a moderate negative correlation between synovial scores and pain levels (p = 0.005). CONCLUSION: GAE significantly reduces synovitis in knee OA, evidenced by CE-MRI score changes. The correlation between pre-procedural synovial contrast scores and pain relief post-procedure, while promising, requires careful interpretation due to the complex factors affecting pain in knee OA.

Spontaneous intracranial hypotension: Exploring the viability of non-contrast FLAIR as a substitute for contrast-enhanced T1WI in assessing pachymeningeal thickening.

PURPOSE: To avoid contrast administration in spontaneous intracranial hypotension (SIH), some studies suggest accepting diffuse pachymeningeal hyperintensity (DPMH) on non-contrast fluid-attenuated inversion recovery (FLAIR) as an equivalent sign to diffuse pachymeningeal enhancement (DPME) on contrast-enhanced T1WI (T1ce), despite lacking thorough performance metrics. This study aimed to comprehensively explore its feasibility. METHODS: In this single-center retrospective study, between April 2021 and November 2023, brain MRI examinations of 43 patients clinically diagnosed with SIH were assessed using 1.5 and 3.0 Tesla MRI scanners. Two radiologists independently assessed the presence or absence of DPMH on FLAIR and DPME on T1ce, with T1ce serving as a gold-standard for pachymeningeal thickening. The contribution of the subdural fluid collections to DPMH was investigated with quantitative measurements. Using Cohen's kappa statistics, interobserver agreement was assessed. RESULTS: In 39 out of 43 patients (90.7%), pachymeningeal thickening was observed on T1ce. FLAIR sequence produced an accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 72.1%, 71.8%, 75.0%, 96.6%, and 21.4% respectively, for determining pachymeningeal thickening. FLAIR identified pachymeningeal thickening in 28 cases; however, among these, 21 cases (75%) revealed that the pachymeningeal hyperintense signal was influenced by subdural fluid collections. False-negative rate for FLAIR was 28.2% (11/39). CONCLUSION: The lack of complete correlation between FLAIR and T1ce in identifying pachymeningeal thickening highlights the need for caution in removing contrast agent administration from the MRI protocol of SIH patients, as it reveals a major criterion (i.e., pachymeningeal enhancement) of Bern score.

Successful treatment of a rheumatoid arthritis patient with severe synovial hypertrophy and impaired shoulder function with microwave ablation technique.

Rheumatoid arthritis (RA) is a chronic, inflammatory systemic disorder of synovial joints and results in polyarthritis, chronical degeneration, and finally deformities and ankylosis in severe cases. Synovitis and pannus formation are results of inflammatory changes and lead into restriction in joint movement. Shoulders are among the later affected and larger joints and formation of synovitis in early active stages and pannus in later stages might be concluded with frozen shoulder and severe impairment in functionality. These late-term changes cannot be controlled with systemic or local anti-inflammatory agents and synovectomy is chosen in some cases. However, the results are not satisfactory and recurrence is common. In this case report, we presented a case of RA with severe shoulder pain, restricted movement due to synovial hypertrophy, and pannus formation which are resistant to local and systemic interventions and not suitable for surgical or chemical synovectomy. Microwave ablation (MWA) was performed successfully without any complication and she well responded in terms of DAS-28, functional, and pain scores. Range of motion and funcitonal restriction were recovered. This case report describes the use and promising results of MWA in RA with severe synovial hypertrophy and pannus formation even in the absence of active arthritis and effusion. MWA is a safe and minimally invasive technique that can be easily performed in coordinance of rheumatologists and interventional radiologists in proper cases.

A surgical method to be reminded for the treatment of symptomatic ipsilateral central venous occlusions in patients with hemodialysis access: Axillo-axillary venous bypass case report and review of the literature.

BACKGROUND: In this case report, we present two chronic hemodialysis patients with upper extremity swelling due to central venous occlusions together with their clinical presentation, surgical management and brief review of the literature. METHODS: The first patient who was a 63-year-old female patient with a history of multiple bilateral arteriovenous fistulas (AVFs) was referred to our clinic. Physical examination demonstrated a functioning right brachio-cephalic AVF, with severe edema of the right arm, dilated venous collaterals, facial edema, and unilateral breast enlargement. In her history, multiple ipsilateral subclavian venous catheterizations were present for sustaining temporary hemodialysis access. The second patient was a 47-year-old male with a history of failed renal transplant, CABG surgery, multiple AV fistula procedures from both extremities, leg amputation caused by peripheral arterial disease, and decreased myocardial functions. He was receiving 3/7 hemodialysis and admitted to our clinic with right arm edema, accompanied by pain, stiffness, and skin hyperpigmentation symptoms ipsilateral to a functioning brachio-basilic AVF. He was not able to flex his arms, elbow, or wrist due to severe edema. RESULTS: Venography revealed right subclavian vein stenosis with patent contralateral central veins in the first patient. She underwent percutaneous transluminal angioplasty (PTA) twice with subsequent re-occlusions. After failed attempts of PTA, the patient was scheduled for axillo-axillary venous bypass in order to preserve the AV access function. In second patient, venography revealed right subclavian vein occlusion caused secondary to the subclavian venous catheters. Previous attempts for percutaneously crossing the chronic subclavian lesion failed multiple times by different centers. Hence, the patient was scheduled for axillo-axillary venous bypass surgery. CONCLUSION: In case of chronic venous occlusions, endovascular procedures may be ineffective. Since preserving the vascular access function is crucial in this particular patient population, venous bypass procedures should be kept in mind as an alternative for central venous reconstruction, before deciding on ligation and relocation of the AVF.

How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts.

In recent years, there has been a dramatic increase in research papers about machine learning (ML) and artificial intelligence in radiology. With so many papers around, it is of paramount importance to make a proper scientific quality assessment as to their validity, reliability, effectiveness, and clinical applicability. Due to methodological complexity, the papers on ML in radiology are often hard to evaluate, requiring a good understanding of key methodological issues. In this review, we aimed to guide the radiology community about key methodological aspects of ML to improve their academic reading and peer-review experience. Key aspects of ML pipeline were presented within four broad categories: study design, data handling, modelling, and reporting. Sixteen key methodological items and related common pitfalls were reviewed with a fresh perspective: database size, robustness of reference standard, information leakage, feature scaling, reliability of features, high dimensionality, perturbations in feature selection, class balance, bias-variance trade-off, hyperparameter tuning, performance metrics, generalisability, clinical utility, comparison with traditional tools, data sharing, and transparent reporting.Key Points• Machine learning is new and rather complex for the radiology community.• Validity, reliability, effectiveness, and clinical applicability of studies on machine learning can be evaluated with a proper understanding of key methodological concepts about study design, data handling, modelling, and reporting.• Understanding key methodological concepts will provide a better academic reading and peer-review experience for the radiology community.

Radiogenomics of lower-grade gliomas: machine learning-based MRI texture analysis for predicting 1p/19q codeletion status.

OBJECTIVE: To evaluate the potential value of the machine learning (ML)-based MRI texture analysis for predicting 1p/19q codeletion status of lower-grade gliomas (LGG), using various state-of-the-art ML algorithms. MATERIALS AND METHODS: For this retrospective study, 107 patients with LGG were included from a public database. Texture features were extracted from conventional T2-weighted and contrast-enhanced T1-weighted MRI images, using LIFEx software. Training and unseen validation splits were created using stratified 10-fold cross-validation technique along with minority over-sampling. Dimension reduction was done using collinearity analysis and feature selection (ReliefF). Classifications were done using adaptive boosting, k-nearest neighbours, naive Bayes, neural network, random forest, stochastic gradient descent, and support vector machine. Friedman test and pairwise post hoc analyses were used for comparison of classification performances based on the area under the curve (AUC). RESULTS: Overall, the predictive performance of the ML algorithms were statistically significantly different, χ2(6) = 26.7, p < 0.001. There was no statistically significant difference among the performance of the neural network, naive Bayes, support vector machine, random forest, and stochastic gradient descent, adjusted p > 0.05. The mean AUC and accuracy values of these five algorithms ranged from 0.769 to 0.869 and from 80.1 to 84%, respectively. The neural network had the highest mean rank with mean AUC and accuracy values of 0.869 and 83.8%, respectively. CONCLUSIONS: The ML-based MRI texture analysis might be a promising non-invasive technique for predicting the 1p/19q codeletion status of LGGs. Using this technique along with various ML algorithms, more than four-fifths of the LGGs can be correctly classified. KEY POINTS: • More than four-fifths of the lower-grade gliomas can be correctly classified with machine learning-based MRI texture analysis. Satisfying classification outcomes are not limited to a single algorithm. • A few-slice-based volumetric segmentation technique would be a valid approach, providing satisfactory predictive textural information and avoiding excessive segmentation duration in clinical practice. • Feature selection is sensitive to different patient data set samples so that each sampling leads to the selection of different feature subsets, which needs to be considered in future works.

Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency.

OBJECTIVE. The objective of our study was to systematically review the literature about the application of artificial intelligence (AI) to renal mass characterization with a focus on the methodologic quality items. MATERIALS AND METHODS. A systematic literature search was conducted using PubMed to identify original research studies about the application of AI to renal mass characterization. Besides baseline study characteristics, a total of 15 methodologic quality items were extracted and evaluated on the basis of the following four main categories: modeling, performance evaluation, clinical utility, and transparency items. The qualitative synthesis was presented using descriptive statistics with an accompanying narrative. RESULTS. Thirty studies were included in this systematic review. Overall, the methodologic quality items were mostly favorable for modeling (63%) and performance evaluation (63%). Even so, the studies (57%) more frequently constructed their work on nonrobust features. Furthermore, only a few studies (10%) had a generalizability assessment with independent or external validation. The studies were mostly unsuccessful in terms of clinical utility evaluation (89%) and transparency (97%) items. For clinical utility, the interesting findings were lack of comparisons with both radiologists' evaluation (87%) and traditional models (70%) in most of the studies. For transparency, most studies (97%) did not share their data with the public. CONCLUSION. To bring AI-based renal mass characterization from research to practice, future studies need to improve modeling and performance evaluation strategies and pay attention to clinical utility and transparency issues.

Machine Learning in Radiomic Renal Mass Characterization: Fundamentals, Applications, Challenges, and Future Directions.

OBJECTIVE. The purpose of this study is to provide an overview of the traditional machine learning (ML)-based and deep learning-based radiomic approaches, with focus placed on renal mass characterization. CONCLUSION. ML currently has a very low barrier to entry into general medical practice because of the availability of many open-source, free, and easy-to-use toolboxes. Therefore, it should not be surprising to see its related applications in renal mass characterization. A wider picture of the previous works might be beneficial to move this field forward.

Radiomics of Renal Masses: Systematic Review of Reproducibility and Validation Strategies.

OBJECTIVE. The purpose of this study was to systematically review the radiomics literature on renal mass characterization in terms of reproducibility and validation strategies. MATERIALS AND METHODS. With use of PubMed and Google Scholar, a systematic literature search was performed to identify original research papers assessing the value of radiomics in characterization of renal masses. The data items were extracted on the basis of three main categories: baseline study characteristics, radiomic feature reproducibility strategies, and statistical model validation strategies. RESULTS. After screening and application of the eligibility criteria, a total of 41 papers were included in the study. Almost one-half of the papers (19 [46%]) presented at least one reproducibility analysis. Segmentation variability (18 [44%]) was the main theme of the analyses, outnumbering image acquisition or processing (3 [7%]). No single paper considered slice selection bias. The most commonly used statistical tool for analysis was intraclass correlation coefficient (14 of 19 [74%]), with no consensus on the threshold or cutoff values. Approximately one-half of the papers (22 [54%]) used at least one validation method, with a predominance of internal validation techniques (20 [49%]). The most frequently used internal validation technique was k-fold cross-validation (12 [29%]). Independent or external validation was used in only three papers (7%). CONCLUSION. Workflow characteristics described in the radiomics literature about renal mass characterization are heterogeneous. To bring radiomics from a mere research area to clinical use, the field needs many more papers that consider the reproducibility of radiomic features and include independent or external validation in their workflow.

Machine learning-based unenhanced CT texture analysis for predicting BAP1 mutation status of clear cell renal cell carcinomas.

BACKGROUND: BRCA1-associated protein 1 (BAP1) mutation is an unfavorable factor for overall survival in patients with clear cell renal cell carcinoma (ccRCC). Radiomics literature about BAP1 mutation lacks papers that consider the reliability of texture features in their workflow. PURPOSE: Using texture features with a high inter-observer agreement, we aimed to develop and internally validate a machine learning-based radiomic model for predicting the BAP1 mutation status of ccRCCs. MATERIAL AND METHODS: For this retrospective study, 65 ccRCCs were included from a public database. Texture features were extracted from unenhanced computed tomography (CT) images, using two-dimensional manual segmentation. Dimension reduction was done in three steps: (i) inter-observer agreement analysis; (ii) collinearity analysis; and (iii) feature selection. The machine learning classifier was random forest. The model was validated using 10-fold nested cross-validation. The reference standard was the BAP1 mutation status. RESULTS: Out of 744 features, 468 had an excellent inter-observer agreement. After the collinearity analysis, the number of features decreased to 17. Finally, the wrapper-based algorithm selected six features. Using selected features, the random forest correctly classified 84.6% of the labelled slices regarding BAP1 mutation status with an area under the receiver operating characteristic curve of 0.897. For predicting ccRCCs with BAP1 mutation, the sensitivity, specificity, and precision were 90.4%, 78.8%, and 81%, respectively. For predicting ccRCCs without BAP1 mutation, the sensitivity, specificity, and precision were 78.8%, 90.4%, and 89.1%, respectively. CONCLUSION: Machine learning-based unenhanced CT texture analysis might be a potential method for predicting the BAP1 mutation status of ccRCCs.

Influence of segmentation margin on machine learning-based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas.

OBJECTIVE: To determine the possible influence of segmentation margin on each step (feature reproducibility, selection, and classification) of the machine learning (ML)-based high-dimensional quantitative computed tomography (CT) texture analysis (qCT-TA) of renal clear cell carcinomas (RcCCs). MATERIALS AND METHODS: For this retrospective study, 47 patients with RcCC were included from a public database. Two segmentations were obtained by two radiologists for each tumour: (i) contour-focused and (ii) margin shrinkage of 2 mm. Texture features were extracted from original, filtered, and transformed CT images. Feature selection was done using a correlation-based algorithm. The ML classifier was k-nearest neighbours. Classifications were performed with and without using synthetic minority over-sampling technique. Reference standard was nuclear grade (low versus high). Intraclass correlation coefficient (ICC), Pearson's correlation coefficient, Wilcoxon signed-ranks test, and McNemar's test were used in the analysis. RESULTS: The segmentation with margin shrinkage of 2 mm (772 of 828; 93.2%) yielded more texture features with excellent reproducibility (ICC ≥ 0.9) than contour-focused segmentation (714 of 828; 86.2%), p < 0.0001. The feature selection algorithms resulted in different feature subsets for two segmentation datasets with only one common feature. All ML-based models based on contour-focused segmentation (area under the curve [AUC] range, 0.865-0.984) performed better than those with margin shrinkage of 2 mm (AUC range, 0.745-0.887), p < 0.05. CONCLUSIONS: Each step of the ML-based high-dimensional qCT-TA was susceptible to a slight change of 2 mm in segmentation margin. Despite yielding fewer features with excellent reproducibility, use of the contour-focused segmentation provided better classification performance for distinguishing nuclear grade. KEY POINTS: • Each step of a machine learning (ML)-based high-dimensional quantitative computed tomography texture analysis (qCT-TA) is sensitive to even a slight change of 2 mm in segmentation margin. • Despite yielding fewer texture features with excellent reproducibility, performing the segmentation focusing on the outermost boundary of the tumours provides better classification performance in ML-based qCT-TA of renal clear cell carcinomas for distinguishing nuclear grade. • Findings of an ML-based high-dimensional qCT-TA may not be reproducible in clinical practice even using the same feature selection algorithm and ML classifier unless the possible influence of the segmentation margin is considered.

Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade.

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.

Unenhanced CT Texture Analysis of Clear Cell Renal Cell Carcinomas: A Machine Learning-Based Study for Predicting Histopathologic Nuclear Grade.

OBJECTIVE. The purpose of this study is to investigate the predictive performance of machine learning (ML)-based unenhanced CT texture analysis in distinguishing low (grades I and II) and high (grades III and IV) nuclear grade clear cell renal cell carcinomas (RCCs). MATERIALS AND METHODS. For this retrospective study, 81 patients with clear cell RCC (56 high and 25 low nuclear grade) were included from a public database. Using 2D manual segmentation, 744 texture features were extracted from unenhanced CT images. Dimension reduction was done in three consecutive steps: reproducibility analysis by two radiologists, collinearity analysis, and feature selection. Models were created using artificial neural network (ANN) and binary logistic regression, with and without synthetic minority oversampling technique (SMOTE), and were validated using 10-fold cross-validation. The reference standard was histopathologic nuclear grade (low vs high). RESULTS. Dimension reduction steps yielded five texture features for the ANN and six for the logistic regression algorithm. None of clinical variables was selected. ANN alone and ANN with SMOTE correctly classified 81.5% and 70.5%, respectively, of clear cell RCCs, with AUC values of 0.714 and 0.702, respectively. The logistic regression algorithm alone and with SMOTE correctly classified 75.3% and 62.5%, respectively, of the tumors, with AUC values of 0.656 and 0.666, respectively. The ANN performed better than the logistic regression (p < 0.05). No statistically significant difference was present between the model performances created with and without SMOTE (p > 0.05). CONCLUSION. ML-based unenhanced CT texture analysis using ANN can be a promising noninvasive method in predicting the nuclear grade of clear cell RCCs.

Reliability of Single-Slice-Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility.

OBJECTIVE. The objective of our study was to investigate the potential influence of intra- and interobserver manual segmentation variability on the reliability of single-slice-based 2D CT texture analysis of renal masses. MATERIALS AND METHODS. For this retrospective study, 30 patients with clear cell renal cell carcinoma were included from a public database. For intra- and interobserver analyses, three radiologists with varying degrees of experience segmented the tumors from unenhanced CT and corticomedullary phase contrast-enhanced CT (CECT) in different sessions. Each radiologist was blind to the image slices selected by other radiologists and him- or herself in the previous session. A total of 744 texture features were extracted from original, filtered, and transformed images. The intraclass correlation coefficient was used for reliability analysis. RESULTS. In the intraobserver analysis, the rates of features with good to excellent reliability were 84.4-92.2% for unenhanced CT and 85.5-93.1% for CECT. Considering the mean rates of unenhanced CT and CECT, having high experience resulted in better reliability rates in terms of the intraobserver analysis. In the interobserver analysis, the rates were 76.7% for unenhanced CT and 84.9% for CECT. The gray-level cooccurrence matrix and first-order feature groups yielded higher good to excellent reliability rates on both unenhanced CT and CECT. Filtered and transformed images resulted in more features with good to excellent reliability than the original images did on both unenhanced CT and CECT. CONCLUSION. Single-slice-based 2D CT texture analysis of renal masses is sensitive to intra- and interobserver manual segmentation variability. Therefore, it may lead to nonreproducible results in radiomic analysis unless a reliability analysis is considered in the workflow.

Recurrent, Giant Renal Angiomyolipoma Treated with Selective Arterial Embolization: A Case Report.

Renal angiomyolipoma (AML), a rare benign mesenchymal neoplasm, is characterized by the presence of vessels, smooth muscle, and adipose tissue. Treatment should be considered for symptomatic patients or for those at risk for complications, in particular for retroperitoneal bleeding, which is correlated to the size of the tumor, grade of the angiogenic component, and presence of tuberous sclerosis complex. Herein, we report the case of a 39-year-old female with renal AML who was treated in a conservative approach by super-selective embolization.

Cerebral Protection with a Temporary Ascending Aorta-External Carotid Artery Bypass during Common Carotid Artery Revascularization.

Atherosclerosis is a systemic disease, and multiarterial involvement is common. Involvement of all the supra-aortic arteries may occur in the same patient making cerebral revascularization challenging. In this report, we present complete supra-aortic revascularization, that is, revascularization of the bilateral common carotid and subclavian arteries in a 51-year-old male patient with occluded brachiocephalic trunk, left subclavian artery, and proximally stenotic left common carotid artery. A temporary ascending aorta to left external carotid artery bypass provided meticulous cerebral protection with pulsatile cerebral flow in the presence of a proximal arterial clamp; hence, a neurologically uneventful procedure during bilateral common carotid artery revascularization.

Renal function and diffusion-weighted imaging: a new method to diagnose kidney failure before losing half function.

AIM: To diagnose earlier kidney failure, we investigated renal functions with diffusion-weighted imaging (DWI). METHODS: We evaluated the DWI of 62 patients with chronic kidney disease (CKD) and compared it with creatinine clearance provided by daily urine collection. The apparent diffusion coefficient (ADC) values were compared with the five stages of CKD. RESULTS: For each stage of CKD, the ADC values were found to be significantly different (p < 0.01) and allowed the differentiation of stage 1 of the disease from the other stages. CONCLUSION: Renal ADC values show a significant correlation with the clinical stages of CKD. DWI may detect renal failure prior to a rise in creatinine.

Effectiveness of polidocanol sclerotherapy in alleviating symptoms in patients with venous malformations.

OBJECTIVE: The objective of this study was to retrospectively evaluate the effectiveness of polidocanol in managing pain, swelling, functional limiting and cosmetic disorders in patients with venous malformations (VMs). METHODS: This retrospective study included patients who underwent sclerotherapy with polidocanol for VMs between 2020 and 2022. Patient records, imaging findings, and evaluation questionnaires used in the preprocedure and follow-up phases were reviewed. After sclerotherapy, patients were followed up at 1, 2, 3, and 6 months. During these visits, the previously used 11-point verbal numerical rating scale (from 0 [no pain] to 10 [worst pain thinkable]) was used to evaluate the severity of symptoms such as pain, swelling, cosmetic discomfort, and functional limitation, and patients were asked to report the number of days per week they experienced these symptoms owing to the VM. RESULTS: A total of 194 sclerotherapy procedures (mean, 1.6 ± 0.3 procedures) in 84 patients (55 female and 29 male patients; mean age, 22.45 ± 11.83 years) were conducted. The majority of these malformations (81%, or 68 patients) were located in the extremities. We found a significant decrease in pain, swelling, functional limitation, cosmetic appearance, and number of painful days between all time points, except for the comparison between months 3 and 6 (P < .001) CONCLUSIONS: Polidocanol sclerotherapy is a safe and effective treatment for VMs that significantly decreases patient complaints and has a very low complication rate. Particularly, following patients at short intervals and administering additional sclerotherapy sessions when necessary will significantly increase patient satisfaction.