Abdominal computed tomography measurements of body composition and waitlist mortality in kidney transplant candidates.

Body mass index is often used to determine kidney transplant (KT) candidacy. However, this measure of body composition (BC) has several limitations, including the inability to accurately capture dry weight. Objective computed tomography (CT)-based measures may improve pre-KT risk stratification and capture physiological aging more accurately. We quantified the association between CT-based BC measurements and waitlist mortality in a retrospective study of 828 KT candidates (2010-2022) with clinically obtained CT scans using adjusted competing risk regression. In total, 42.5% of candidates had myopenia, 11.4% had myopenic obesity (MO), 68.8% had myosteatosis, 24.8% had sarcopenia (probable = 11.2%, confirmed = 10.5%, and severe = 3.1%), and 8.6% had sarcopenic obesity. Myopenia, MO, and sarcopenic obesity were not associated with mortality. Patients with myosteatosis (adjusted subhazard ratio [aSHR] = 1.62, 95% confidence interval [CI]: 1.07-2.45; after confounder adjustment) or sarcopenia (probable: aSHR = 1.78, 95% CI: 1.10-2.88; confirmed: aSHR = 1.68, 95% CI: 1.01-2.82; and severe: aSHR = 2.51, 95% CI: 1.12-5.66; after full adjustment) were at increased risk of mortality. When stratified by age, MO (aSHR = 2.21, 95% CI: 1.28-3.83; P interaction = .005) and myosteatosis (aSHR = 1.95, 95% CI: 1.18-3.21; P interaction = .038) were associated with elevated risk only among candidates <65 years. MO was only associated with waitlist mortality among frail candidates (adjusted hazard ratio = 2.54, 95% CI: 1.28-5.05; P interaction = .021). Transplant centers should consider using BC metrics in addition to body mass index when a CT scan is available to improve pre-KT risk stratification at KT evaluation.

Association between Abdominal CT Measurements of Body Composition before Deceased Donor Liver Transplant with Posttransplant Outcomes.

Background Pre-liver transplant (LT) sarcopenia is associated with poor survival. Methods exist for measuring body composition with use of CT scans; however, it is unclear which components best predict post-LT outcomes. Purpose To quantify the association between abdominal CT-based body composition measurements and post-LT mortality in a large North American cohort. Materials and Methods This was a retrospective cohort of adult first-time deceased-donor LT recipients from 2009 to 2018 who underwent pre-LT abdominal CT scans, including at the L3 vertebral level, at Johns Hopkins Hospital. Measurements included sarcopenia (skeletal muscle index [SMI] <50 in men and <39 in women), sarcopenic obesity, myosteatosis (skeletal muscle CT attenuation <41 mean HU for body mass index [BMI] <25 and <33 mean HU for BMI ≥25), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and VAT/SAT ratio. Covariates in the adjusted models were selected with use of least absolute shrinkage and selection operator regression with lambda chosen by means of 10-fold cross-validation. Cox proportional hazards models were used to quantify associations with post-LT mortality. Model discrimination was quantified using the Harrell C-statistic. Results A total of 454 recipients (median age, 57 years [IQR, 50-62 years]; 294 men) were evaluated. In the adjusted model, pre-LT sarcopenia was associated with a higher hazard ratio (HR) of post-LT mortality (HR, 1.6 [95% CI: 1.1, 2.4]; C-statistic, 0.64; P = .02). SMI was significantly negatively associated with survival after adjustment for covariates. There was no evidence that myosteatosis was associated with mortality (HR, 1.3 [95% CI: 0.86, 2.1]; C-statistic, 0.64; P = .21). There was no evidence that BMI (HR, 1.2 [95% CI: 0.95, 1.4]), VAT (HR, 1.0 [95% CI: 0.98, 1.1]), SAT (HR, 1.0 [95% CI: 0.97, 1.0]), and VAT/SAT ratio (HR, 1.1 [95% CI: 0.90, 1.4]) were associated with mortality (P = .15-.77). Conclusions Sarcopenia, as assessed on routine pre-liver transplant (LT) abdominal CT scans, was the only factor significantly associated with post-LT mortality. © RSNA, 2022 See also the editorial by Ruehm in this issue.

Revision of frailty assessment in kidney transplant recipients: Replacing unintentional weight loss with CT-assessed sarcopenia in the physical frailty phenotype.

Kidney transplantation (KT) experts did not support the use of subjective unintentional weight loss to measure shrinking in the physical frailty phenotype (PFP); a clinically feasible and predictive measure of shrinking is needed. To test whether unintentional weight loss could be replaced by an assessment of sarcopenia using existing CT scans, we performed a prospective cohort study of adult KT recipients with original PFP (oPFP) measured at admission (December 2008-February 2020). We ascertained sarcopenia by calculating skeletal muscle index from available, clinically obtained CTs within 1-year pre-KT (male < 50 cm2 /m2 ; female < 39 cm2 /m2 ) and combined it with the original four components to determine new PFP (nPFP) scores. Frailty was classified by frailty score: 0: non-frail; 1-2: pre-frail; ≥3: frail. Mortality and graft loss hazard ratios (HRs) were estimated using adjusted Cox proportional hazard models. Model discrimination was quantified using Harrell's C-statistic. Among 1113 recipients, 18.6% and 17.1% were frail by oPFP and nPFP, respectively. Compared to non-frail recipients, frail patients by either PFP had higher risks of mortality (oPFP HR = 1.67, 95% CI: 1.07-2.62, C = 0.710; nPFP HR = 1.68, 95% CI: 1.06-2.66, C = 0.710) and graft loss (oPFP HR = 1.67, 95% CI: 1.17-2.40, C = 0.631; nPFP HR = 1.66, 95% CI: 1.15-2.40, C = 0.634) with similar discriminations. oPFP and nPFP are equally useful in risk prediction for KT recipients; oPFP may aid in screening patients for pre-KT interventions, while nPFP may assist in nuanced clinical decision-making.

Clinical applications of diffusion-weighted sequence in brain imaging: beyond stroke.

Diffusion-weighted imaging (DWI) is a well-established MRI sequence for diagnosing early stroke and provides therapeutic implications. However, DWI yields pertinent information in various other brain pathologies and helps establish a specific diagnosis and management of other central nervous system disorders. Some of these conditions can present with acute changes in neurological status and mimic stroke. This review will focus briefly on diffusion imaging techniques, followed by a more comprehensive description of the utility of DWI in common neurological entities beyond stroke.

Leveraging artificial intelligence in ischemic stroke imaging.

Artificial intelligence (AI) is having a disruptive and transformative effect on clinical medicine. Prompt clinical diagnosis and imaging are critical for minimizing the morbidity and mortality associated with ischemic strokes. Clinicians must understand the current strengths and limitations of AI to provide optimal patient care. Ischemic stroke is one of the medical fields that have been extensively evaluated by artificial intelligence. Presented herein is a review of artificial intelligence applied to clinical management of stroke, geared toward clinicians. In this review, we explain the basic concept of AI and machine learning. This review is without coding and mathematical details and targets the clinicians involved in stroke management without any computer or mathematics' background. Here the AI application in ischemic stroke is summarized and classified into stroke imaging (automated diagnosis of brain infarction, automated ASPECT score calculation, infarction segmentation), prognosis prediction, and patients' selection for treatment.

Clinical and molecular characterization of patients with hereditary hemorrhagic telangiectasia: Experience from an HHT Center of Excellence.

In this retrospective single-center study, we evaluated whether/how pathogenic/likely pathogenic variants of three hereditary hemorrhagic telangiectasia (HHT)-associated genes (ENG, ACVRL1, and SMAD4) are associated with specific clinical presentations of HHT. We also characterized the morphological features of pulmonary arteriovenous malformations (AVMs) in patients with these variants. Pathogenic or likely pathogenic variants were detected in 64 patients. Using nonparametric statistical tests, we compared the type and prevalence of specific HHT diagnostic features associated with these three variants. Pathogenic variants in these genes resulted in gene-specific HHT clinical presentations. Epistaxis was present in 93%, 94%, and 100% of patients with ENG, ACVRL1, and SMAD4 variants, respectively (p = 0.79). Pulmonary AVMs were more common in patients with the ENG variant (p = 0.034) compared with other subgroups. ACVRL1 variant was associated with the lowest frequency of pulmonary AVMs (p = 0.034) but the highest frequency of hepatic AVMs (p = 0.015). Patients with the ACVRL1 variant did not have significantly more pancreatic AVMs compared with the other groups (p = 0.72). ENG, ACVRL1, and SMAD4 pathogenic or likely pathogenic variants are associated with gene-specific HHT presentations, which is consistent with results from other HHT centers.

Interventional Radiology Obesity Therapeutics: Proceedings from the Society of Interventional Radiology Foundation Research Consensus Panel.

The Society of Interventional Radiology Foundation commissioned a Research Consensus Panel to establish a research agenda on "Obesity Therapeutics" in interventional radiology (IR). The meeting convened a multidisciplinary group of physicians and scientists with expertise in obesity therapeutics. The meeting was intended to review current evidence on obesity therapies, familiarize attendees with the regulatory evaluation process, and identify research deficiencies in IR bariatric interventions, with the goal of prioritizing future high-quality research that would move the field forward. The panelists agreed that a weight loss of >8%-10% from baseline at 6-12 months is a desirable therapeutic endpoint for future IR weight loss therapies. The final consensus on the highest priority research was to design a blinded randomized controlled trial of IR weight loss interventions versus sham control arms, with patients receiving behavioral therapy.

Misleading CT perfusion in subacute ischemic stroke.

The RAPID© software is the most commonly used computed tomography perfusion (CTP) software in stroke centers. It is estimated that about 1300 hospitals in the world are using this software for decision-making in ischemic stroke. The software provides the estimated volume of infarction and ischemic penumbra, so it is the backbone of treatment planning in these patients. In this manuscript, we present two cases of subacute infarction with misleading CTP using RAPID© software. We believe that given the popularity of this software and increasing application of CTP in subacute infarction, this pitfall is likely underdiagnosed in many patients. In a subacute phase of infarction, we recommend diffusion-weighted imaging magnetic resonance imaging (DWI-MRI) for estimation of infarction to avoid this pitfall and possible mismanagement.

How may Doppler indices help in the differentiation of obstructive from nonobstructive hydronephrosis?

BACKGROUND: We assess the potency of different Doppler indices in the differentiation of obstructive and nonobstructive hydronephrosis. MATERIALS AND METHODS: In this study, infants and children who were referred for the evaluation of unilateral hydronephrosis were enrolled. Ultrasonography for the assessment of the degree of hydronephrosis and a voiding cystourethrogram for the exclusion of vesicoureteral reflux was performed. Then, Doppler ultrasonography was done for both kidneys of each patient using four classic Doppler indices as well as the difference (delta) of each index between to kidneys. Diuretic renography with 99 mTc-ethylene dicysteine (99 mTc-EC) was performed for each patient. RESULTS: Thirty-nine patients met the inclusion criteria. After diuretic renography, 29 (74.35%) patients had shown a nonobstructive pattern, and ten (25.65%) patients had a partial (intermediate) or complete obstruction. Using receiver operating characteristic (ROC) curve, none of the classic indices of Doppler duplex (i.e., resistive index [RI], resistance index, end diastolic velocity, and peak systolic velocity) had the ability to make a difference between obstructive and nonobstructive hydronephrosis. However, by calculating the difference (delta) of these indices between two kidneys of each patient, delta RI could differentiate the nonobstructive condition, significantly (P = 0.006). A cutoff value of 0.055 has 60% sensitivity and 82.8% specificity. The area under the ROC curve for delta RI is 0.795 (standard error: 0.086, 95% confidence interval [CI]: 0.626, 0.964). Furthermore, RI ratio between two kidneys of each patient could differentiate the nonobstructive condition, significantly (P = 0.012). A cutoff point of 1.075 has 70% sensitivity and 82.8% specificity. The area under the ROC curve for RI ratio was 0.769 (standard error: 0.104, 95% CI: 0.565, 0.973). CONCLUSION: This study shows that RI ratio and delta RI with a high specificity could differentiate nonobstructive hydronephrosis and therefore it is a promising way to use especially in the follow-up of children with hydronephrosis.

Improved subcutaneous edema segmentation on abdominal CT using a generated adipose tissue density prior.

PURPOSE: Edema, or swelling, is a common symptom of kidney, heart, and liver disease. Volumetric edema measurement is potentially clinically useful. Edema can occur in various tissues. This work focuses on segmentation and volume measurement of one common site, subcutaneous adipose tissue. METHODS: The density distributions of edema and subcutaneous adipose tissue are represented as a two-class Gaussian mixture model (GMM). In previous work, edema regions were segmented by selecting voxels with density values within the edema density distribution. This work improves upon the prior work by generating an adipose tissue mask without edema through a conditional generative adversarial network. The density distribution of the generated mask was imported into a Chan-Vese level set framework. Edema and subcutaneous adipose tissue are separated by iteratively updating their respective density distributions. RESULTS: Validation results on 25 patients with edema showed that the segmentation accuracy significantly improved. Compared to GMM, the average Dice Similarity Coefficient increased from 56.0 to 61.7% ([Formula: see text]) and the relative volume difference decreased from 36.5 to 30.2% ([Formula: see text]). CONCLUSION: The generated adipose tissue density prior improved edema segmentation accuracy. Accurate edema volume measurement may prove clinically useful.

Application of the Intracranial Arterial Pulsatility Index for Determination of Prognosis after Lacunar Infarct.

Background: We aimed to investigate the usefulness of intracranial arterial pulsatility index to assess the prognosis of lacunar infarcts. Materials and Methods: Forty-nine patients with confirmed acute lacunar infarct were enrolled in the study. A transcranial color-coded sonography was performed to assess the pulsatility index of bilateral middle cerebral, posterior cerebral, vertebral, and proximal internal carotid arteries. Patients' clinical status was assessed using a modified Rankin scale. Spearman correlation was used for reporting the relation between quantitative data. Statistical significance was defined as a two-tail p-value of less than 0.05. Results: The mean age ± standard deviation was 64.1 ± 9.07 years old, and 57.1% of the patients were male. Upon discharge, only 8.2% of the patients were ranked as 0 on the modified Rankin scale; however, after a 6-month follow-up period, this number increased to 49%. There were no significant differences between the left and right pulsatility index measurements in any of the assessed arteries. Patients with vertebral artery pulsatility indexes >1 on their primary assessment had significantly worse outcomes during the first, third, and sixth months follow-up (all r > 0.3, p-values < 0.01). Pulsatility indexes from other arteries did not predict the prognosis. Conclusion: Sonography-assisted assessment of the vertebral artery blood flow during the early stage of lacunar infarct provides a reliable reference for prognosis estimation.

Radiological and Clinical Findings of Non-bifurcating Carotid Artery With Aberrant Course: A Case Report and Review of the Literature.

A non-bifurcating carotid artery is a rare variation in the carotid circulation. Here we present a rare case of a non-bifurcating carotid artery with an aberrant course of the internal carotid artery incidentally discovered in a patient who presented to the trauma center after a fall. To our knowledge, this is the first reported case of a non-bifurcating carotid artery with an aberrant course of the internal carotid artery. The embryonic mechanisms of this variation and the available literature regarding this condition are also reviewed. Knowing this variation is necessary before considering vascular intervention of the neck and ear surgery to avoid vascular injury and complications.

Heavily calcified parapharyngeal space mesenchymal chondrosarcoma: Imaging and pathological findings and a review of the literature.

Parapharyngeal space (PPS) masses are relatively rare lesions of the head and neck, and account for 0.5-1.5% of head and neck lesions. The most common lesion to occur in the PPS is a benign salivary neoplasm, typically pleomorphic adenoma either from the deep parotid or from ectopic parotid tissue rests within the PPS. A calcified or ossified mass in this location is exceedingly rare, but a calcified variant of pleomorphic adenoma has been reported. In this study, we present a patient with a heavily calcified PPS mesenchymal chondrosarcoma with an unusual presentation. We discuss the imaging and pathologic findings followed by a review of the current literature.

Accuracy of Machine Learning Models to Predict Mortality in COVID-19 Infection Using the Clinical and Laboratory Data at the Time of Admission.

Aim This study aimed to develop a predictive model to predict patients' mortality with coronavirus disease 2019 (COVID-19) from the basic medical data on the first day of admission. Methods The medical data including the demographic, clinical, and laboratory features on the first day of admission of clinically diagnosed COVID-19 patients were documented. The outcome of patients was also recorded as discharge or death. Feature selection models were then implemented and different machine learning models were developed on top of the selected features to predict discharge or death. The trained models were then tested on the test dataset. Results A total of 520 patients were included in the training dataset. The feature selection demonstrated 22 features as the most powerful predictive features. Among different machine learning models, the naive Bayes demonstrated the best performance with an area under the curve of 0.85. The ensemble model of the naive Bayes and neural network combination had slightly better performance with an area under the curve of 0.86. The models had relatively the same performance on the test dataset. Conclusion Developing a predictive machine learning model based on the basic medical features on the first day of admission in COVID-19 infection is feasible with acceptable performance.

Brainstem Encephalitis. The Role of Imaging in Diagnosis.

Encephalitis is a relatively challenging rare condition caused by a diverse group of etiologies. Brainstem encephalitis/Rhombencephalitis (BE), which affects the cerebellum, pons, and medulla, is even less common and more challenging for diagnosis and treatment. At this time, there is scattered data about BE in the literature, mainly in the form of case reports and case series. In this manuscript, the imaging presentation of BE is reviewed with the help of case examples. Many imaging presentations are not pathognomonic for BE; however, in many cases, clinical presentation, the spatial distribution of lesions, and other associated radiological lesions can provide the radiologists and clinician the clues to an accurate diagnosis.

Emerging Applications of Radiomics in Neurological Disorders: A Review.

Radiomics has achieved significant momentum in radiology research and can reveal image information invisible to radiologists' eyes. Radiomics first evolved for oncologic imaging. Oncologic applications (histopathology, tumor grading, gene mutation analysis, patient survival, and treatment response prediction) of radiomics are widespread. However, it is not limited to oncologic analysis, and any digital medical images can benefit from radiomics analysis. This article reviews the current literature on radiomics in non-oncologic, neurological disorders including ischemic strokes, hemorrhagic stroke, cerebral aneurysms, and demyelinating disorders.

The Role of Machine Learning and Radiomics for Treatment Response Prediction in Idiopathic Normal Pressure Hydrocephalus.

Introduction Ventricular shunting remains the standard of care for patients with idiopathic normal pressure hydrocephalus (iNPH); however, not all patients benefit from the shunting. Prediction of response in advance can result in improved patient selection for ventricular shunting. This study aims to develop a machine learning predictive model for treatment response after shunt placement using the clinical and radiomics features. Methods In this retrospective pilot study, the medical records of iNPH patients who underwent ventricular shunting were evaluated. In each patient, the "idiopathic normal pressure hydrocephalus grading scale" (iNPHGS) and a "Modified Rankin Scale" were calculated before and after surgery. The subsequent treatment response was calculated as the difference between the iNPHGS scores before and after surgery. iNPHGS score reduction of two or more than two were considered as treatment response. The presurgical MRI scans were evaluated by radiologists, the ventricular systems were segmented on the T2-weighted images, and the radiomics features were extracted from the segmented ventricular system. Using Orange data mining open-source platform, different machine learning models were then developed based on the presurgical clinical features and the selected radiomics features to predict treatment response after shunt placement. Results After the implementation of the inclusion criteria, 78 patients were included in this study. One hundred twenty radiomics features were extracted, and the 12 best predictive radiomics features were selected. Using only clinical data (iNPHGS and Modified Rankin Scale), the random forest model achieved the best performance in treatment prediction with an area under the curve (AUC) of 0.71. Adding the Radiomics analysis to the clinical data improved the prediction performance, with the support vector machine (SVM) achieving the highest rank in treatment prediction with an AUC of 0.8. Adding age and sex to the analysis did not improve the prediction. Conclusion Using machine learning models for treatment response prediction in patients with iNPH is feasible with acceptable accuracy. Adding the Radiomics analysis to the clinical features can further improve the predictive performance. SVM is likely the best model for this task.

Susceptibility-Weighted Imaging in Neurodegenerative Disorders: A Review.

As human life expectancy increases, there is an increased prevalence of neurodegenerative disorders and dementia. There are many ongoing research trials for early diagnosis and management of dementia, and neuroimaging is a critical part of such studies. However, conventional neuroimaging often fails to provide enough diagnostic findings in patients with neurodegenerative disorders. In this context, different MRI sequences are currently under investigation to facilitate the accurate diagnosis of such disorders. Susceptibility-weighted imaging (SWI) is an innovative MRI technique that utilizes "magnitude" and "phase" images to produce an image contrast that is sensitive for the detection of susceptibility differences of the tissues. As many neurodegenerative disorders are associated with accelerated iron deposition and/or microhemorrhages in different parts of the brain, SWI can be applied to detect these diagnostic clues. For instance, in cerebral amyloid angiopathy, SWI can demonstrate cortical microhemorrhages, which are predominantly in the frontal and parietal regions. Or in Parkinson disease, abnormal swallow-tail sign on high-resolution SWI is highly diagnostic. Also, SWI is a useful sequence to detect the low signal intensity of precentral cortices in patients with amyotrophic lateral sclerosis. Being familiar with SWI findings in neurodegenerative disorders is critical for an accurate diagnosis. In this paper, the authors review the technical parameters of SWI, physiologic, and pathologic iron deposition in the brain, and the role of SWI in the evaluation of neurodegenerative disorders in daily practice.