Radiogenomic Associations Clear Cell Renal Cell Carcinoma: An Exploratory Study.

INTRODUCTION: This study investigates how quantitative texture analysis can be used to non-invasively identify novel radiogenomic correlations with clear cell renal cell carcinoma (ccRCC) biomarkers. METHODS: The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma open-source database was used to identify 190 sets of patient genomic data that had corresponding multiphase contrast-enhanced CT images in The Cancer Imaging Archive. 2,824 radiomic features spanning fifteen texture families were extracted from CT images using a custom-built MATLAB software package. Robust radiomic features with strong inter-scanner reproducibility were selected. Random forest, AdaBoost, and elastic net machine learning (ML) algorithms evaluated the ability of the selected radiomic features to predict the presence of 12 clinically relevant molecular biomarkers identified from the literature. ML analysis was repeated with cases stratified by stage (I/II vs. III/IV) and grade (1/2 vs. 3/4). 10-fold cross validation was used to evaluate model performance. RESULTS: Before stratification by tumor grade and stage, radiomics predicted the presence of several biomarkers with weak discrimination (AUC 0.60-0.68). Once stratified, radiomics predicted KDM5C, SETD2, PBRM1, and mTOR mutation status with acceptable to excellent predictive discrimination (AUC ranges from 0.70 to 0.86). CONCLUSIONS: Radiomic texture analysis can potentially identify a variety of clinically relevant biomarkers in patients with ccRCC and may have a prognostic implication.

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke.

The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.

CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma.

OBJECTIVES: To evaluate the utility of CT-based radiomics signatures in discriminating low-grade (grades 1-2) clear cell renal cell carcinomas (ccRCC) from high-grade (grades 3-4) and low TNM stage (stages I-II) ccRCC from high TNM stage (stages III-IV). METHODS: A total of 587 subjects (mean age 60.2 years ± 12.2; range 22-88.7 years) with ccRCC were included. A total of 255 tumors were high grade and 153 were high stage. For each subject, one dominant tumor was delineated as the region of interest (ROI). Our institutional radiomics pipeline was then used to extract 2824 radiomics features across 12 texture families from the manually segmented volumes of interest. Separate iterations of the machine learning models using all extracted features (full model) as well as only a subset of previously identified robust metrics (robust model) were developed. Variable of importance (VOI) analysis was performed using the out-of-bag Gini index to identify the top 10 radiomics metrics driving each classifier. Model performance was reported using area under the receiver operating curve (AUC). RESULTS: The highest AUC to distinguish between low- and high-grade ccRCC was 0.70 (95% CI 0.62-0.78) and the highest AUC to distinguish between low- and high-stage ccRCC was 0.80 (95% CI 0.74-0.86). Comparable AUCs of 0.73 (95% CI 0.65-0.8) and 0.77 (95% CI 0.7-0.84) were reported using the robust model for grade and stage classification, respectively. VOI analysis revealed the importance of neighborhood operation-based methods, including GLCM, GLDM, and GLRLM, in driving the performance of the robust models for both grade and stage classification. CONCLUSION: Post-validation, CT-based radiomics signatures may prove to be useful tools to assess ccRCC grade and stage and could potentially add to current prognostic models. Multiphase CT-based radiomics signatures have potential to serve as a non-invasive stratification schema for distinguishing between low- and high-grade as well as low- and high-stage ccRCC. KEY POINTS: • Radiomics signatures derived from clinical multiphase CT images were able to stratify low- from high-grade ccRCC, with an AUC of 0.70 (95% CI 0.62-0.78). • Radiomics signatures derived from multiphase CT images yielded discriminative power to stratify low from high TNM stage in ccRCC, with an AUC of 0.80 (95% CI 0.74-0.86). • Models created using only robust radiomics features achieved comparable AUCs of 0.73 (95% CI 0.65-0.80) and 0.77 (95% CI 0.70-0.84) to the model with all radiomics features in classifying ccRCC grade and stage, respectively.

Whole-tumor 3D volumetric MRI-based radiomics approach for distinguishing between benign and malignant soft tissue tumors.

OBJECTIVES: Our purpose was to differentiate between malignant from benign soft tissue neoplasms using a combination of MRI-based radiomics metrics and machine learning. METHODS: Our retrospective study identified 128 histologically diagnosed benign (n = 36) and malignant (n = 92) soft tissue lesions. 3D ROIs were manually drawn on 1 sequence of interest and co-registered to other sequences obtained during the same study. One thousand seven hundred eight radiomics features were extracted from each ROI. Univariate analyses with supportive ROC analyses were conducted to evaluate the discriminative power of predictive models constructed using Real Adaptive Boosting (Adaboost) and Random Forest (RF) machine learning approaches. RESULTS: Univariate analyses demonstrated that 36.89% of individual radiomics varied significantly between benign and malignant lesions at the p ≤ 0.05 level. Adaboost and RF performed similarly well, with AUCs of 0.77 (95% CI 0.68-0.85) and 0.72 (95% CI 0.63-0.81), respectively, after 10-fold cross-validation. Restricting the machine learning models to only sequences extracted from T2FS and STIR sequences maintained comparable performance, with AUCs of 0.73 (95% CI 0.64-0.82) and 0.75 (95% CI 0.65-0.84), respectively. CONCLUSION: Machine learning decision classifiers constructed from MRI-based radiomics features show promising ability to preoperatively discriminate between benign and malignant soft tissue masses. Our approach maintains applicability even when the dataset is restricted to T2FS and STIR fluid-sensitive sequences, which may bolster practicality in clinical application scenarios by eliminating the need for complex co-registrations for multisequence analysis. KEY POINTS: • Predictive models constructed from MRI-based radiomics data and machine learning-augmented approaches yielded good discriminative power to correctly classify benign and malignant lesions on preoperative scans, with AUCs of 0.77 (95% CI 0.68-0.85) and 0.72 (95% CI 0.63-0.81) for Real Adaptive Boosting (Adaboost) and Random Forest (RF), respectively. • Restricting the models to only use metrics extracted from T2 fat-saturated (T2FS) and Short-Tau Inversion Recovery (STIR) sequences yielded similar performance, with AUCs of 0.73 (95% CI 0.64-0.82) and 0.75 (95% CI 0.65-0.84) for Adaboost and RF, respectively. • Radiomics-based machine learning decision classifiers constructed from multicentric data more closely mimic the real-world practice environment and warrant additional validation ahead of prospective implementation into clinical workflows.

Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses.

OBJECTIVES: Using a radiomics framework to quantitatively analyze tumor shape and texture features in three dimensions, we tested its ability to objectively and robustly distinguish between benign and malignant renal masses. We assessed the relative contributions of shape and texture metrics separately and together in the prediction model. MATERIALS AND METHODS: Computed tomography (CT) images of 735 patients with 539 malignant and 196 benign masses were segmented in this retrospective study. Thirty-three shape and 760 texture metrics were calculated per tumor. Tumor classification models using shape, texture, and both metrics were built using random forest and AdaBoost with tenfold cross-validation. Sensitivity analyses on five sub-cohorts with respect to the acquisition phase were conducted. Additional sensitivity analyses after multiple imputation were also conducted. Model performance was assessed using AUC. RESULTS: Random forest classifier showed shape metrics featuring within the top 10% performing metrics regardless of phase, attaining the highest variable importance in the corticomedullary phase. Convex hull perimeter ratio is a consistently high-performing shape feature. Shape metrics alone achieved an AUC ranging 0.64-0.68 across multiple classifiers, compared with 0.67-0.75 and 0.68-0.75 achieved by texture-only and combined models, respectively. CONCLUSION: Shape metrics alone attain high prediction performance and high variable importance in the combined model, while being independent of the acquisition phase (unlike texture). Shape analysis therefore should not be overlooked in its potential to distinguish benign from malignant tumors, and future radiomics platforms powered by machine learning should harness both shape and texture metrics. KEY POINTS: • Current radiomics research is heavily weighted towards texture analysis, but quantitative shape metrics should not be ignored in their potential to distinguish benign from malignant renal tumors. • Shape metrics alone can attain high prediction performance and demonstrate high variable importance in the combined shape and texture radiomics model. • Any future radiomics platform powered by machine learning should harness both shape and texture metrics, especially since tumor shape (unlike texture) is independent of the acquisition phase and more robust from the imaging variations.

Texture Analysis of Imaging: What Radiologists Need to Know.

OBJECTIVE: Radiologic texture is the variation in image intensities within an image and is an important part of radiomics. The objective of this article is to discuss some parameters that affect the performance of texture metrics and propose recommendations that can guide both the design and evaluation of future radiomics studies. CONCLUSION: A variety of texture-extraction techniques are used to assess clinical imaging data. Currently, no consensus exists regarding workflow, including acquisition, extraction, or reporting of variable settings leading to poor reproducibility.

Differentiation of Predominantly Solid Enhancing Lipid-Poor Renal Cell Masses by Use of Contrast-Enhanced CT: Evaluating the Role of Texture in Tumor Subtyping.

OBJECTIVE: The purpose of this study was to assess the accuracy of a panel of texture features extracted from clinical CT in differentiating benign from malignant solid enhancing lipid-poor renal masses. MATERIALS AND METHODS: In a retrospective case-control study of 174 patients with predominantly solid nonmacroscopic fat-containing enhancing renal masses, 129 cases of malignant renal cell carcinoma were found, including clear cell, papillary, and chromophobe subtypes. Benign renal masses-oncocytoma and lipid-poor angiomyolipoma-were found in 45 patients. Whole-lesion ROIs were manually segmented and coregistered from the standard-of-care multiphase contrast-enhanced CT (CECT) scans of these patients. Pathologic diagnosis of all tumors was obtained after surgical resection. CECT images of the renal masses were used as inputs to a CECT texture analysis panel comprising 31 texture metrics derived with six texture methods. Stepwise logistic regression analysis was used to select the best predictor among all candidate predictors from each of the texture methods, and their performance was quantified by AUC. RESULTS: Among the texture predictors aiding renal mass subtyping were entropy, entropy of fast-Fourier transform magnitude, mean, uniformity, information measure of correlation 2, and sum of averages. These metrics had AUC values ranging from good (0.80) to excellent (0.98) across the various subtype comparisons. The overall CECT-based tumor texture model had an AUC of 0.87 (p < 0.05) for differentiating benign from malignant renal masses. CONCLUSION: The CT texture statistical model studied was accurate for differentiating benign from malignant solid enhancing lipid-poor renal masses.

Cortical activation associated with muscle synergies of the human male pelvic floor.

Human pelvic floor muscles have been shown to operate synergistically with a wide variety of muscles, which has been suggested to be an important contributor to continence and pelvic stability during functional tasks. However, the neural mechanism of pelvic floor muscle synergies remains unknown. Here, we test the hypothesis that activation in motor cortical regions associated with pelvic floor activation are part of the neural substrate for such synergies. We first use electromyographic recordings to extend previous findings and demonstrate that pelvic floor muscles activate synergistically during voluntary activation of gluteal muscles, but not during voluntary activation of finger muscles. We then show, using functional magnetic resonance imaging (fMRI), that a region of the medial wall of the precentral gyrus consistently activates during both voluntary pelvic floor muscle activation and voluntary gluteal activation, but not during voluntary finger activation. We finally confirm, using transcranial magnetic stimulation, that the fMRI-identified medial wall region is likely to generate pelvic floor muscle activation. Thus, muscle synergies of the human male pelvic floor appear to involve activation of motor cortical areas associated with pelvic floor control.

Contrast-Enhanced Sonography for Monitoring Neoadjuvant Chemotherapy in Soft Tissue Sarcomas.

Neoadjuvant chemotherapy is a mainstay in treating soft tissue sarcomas. Soft tissue sarcomas can show an increase in size and central necrosis, with a decrease in the viable tumor, as an initial response to neoadjuvant chemotherapy. Thus, the maximum tumor diameter may not reliably assess the response to this therapy. Contrast-enhanced sonography may address this limitation. We evaluated 4 patients with soft tissue sarcomas by contrast-enhanced sonography, performed concomitantly with conventional imaging (computed tomography, magnetic resonance imaging, or positron emission tomography). Quantitative analysis was also performed on 1 sarcoma. A viable, enhancing tumor versus tumor necrosis was nearly identical on contrast-enhanced sonography and conventional imaging. Preliminary results demonstrate potential for contrast-enhanced sonographic monitoring of soft tissue sarcomas during neoadjuvant chemotherapy.

Image coregistration: quantitative processing framework for the assessment of brain lesions.

The quantitative, multiparametric assessment of brain lesions requires coregistering different parameters derived from MRI sequences. This will be followed by analysis of the voxel values of the ROI within the sequences and calculated parametric maps, and deriving multiparametric models to classify imaging data. There is a need for an intuitive, automated quantitative processing framework that is generalized and adaptable to different clinical and research questions. As such flexible frameworks have not been previously described, we proceeded to construct a quantitative post-processing framework with commonly available software components. Matlab was chosen as the programming/integration environment, and SPM was chosen as the coregistration component. Matlab routines were created to extract and concatenate the coregistration transforms, take the coregistered MRI sequences as inputs to the process, allow specification of the ROI, and store the voxel values to the database for statistical analysis. The functionality of the framework was validated using brain tumor MRI cases. The implementation of this quantitative post-processing framework enables intuitive creation of multiple parameters for each voxel, facilitating near real-time in-depth voxel-wise analysis. Our initial empirical evaluation of the framework is an increased usage of analysis requiring post-processing and increased number of simultaneous research activities by clinicians and researchers with non-technical backgrounds. We show that common software components can be utilized to implement an intuitive real-time quantitative post-processing framework, resulting in improved scalability and increased adoption of post-processing needed to answer important diagnostic questions.

Brain Glutathione Increase and Seizure Burden Decrease in Patients with Intractable Epilepsy on Ketogenic Diet.

Background and Purpose: Ketogenic diet (KD) improves seizure control in patients with drug-resistant epilepsy. As increased mitochondrial levels of glutathione (GSH) might contribute to a change in seizure susceptibility, we quantified changes of absolute GSH levels in the brain by in vivo 1H magnetic resonance spectroscopy (1H MRS) and correlate that with degree of seizure control in patients on KD. Methods: Five cognitively normal adult patients with drug-resistant epilepsy were initially included and 2 completed the study. Each patient was evaluated by a neurologist and registered dietitian at baseline, 1, 3, and 6 months for seizure status and diet adherence after initiation of a modified atkins diet. Multiple metabolites including GSH were quantified using LCModel (version 6.3-1P; Stephen Provencher, Oakville, ON, CA) on a short echo time single-voxel 1H MRS in parieto/occipital grey matter and parietal white matter on a 3 Tesla General Electric magnet prior to starting the ketogenic diet and at 6 months. Results: Both patients (42-years-old male and 35-years-old female) demonstrated marked increases in absolute GSH level in both gray matter (0.12 to 1.40 and 0.10 to 0.70 international unit [IU]) and white matter (0.65 to 1.50 and 0.80 to 2.00 IU), as well as 50% improvements in seizure duration and frequency. Other metabolites including ketone bodies did not demonstrate consistent changes. Conclusions: Markedly increased levels of GSH (7-fold and 14-fold) were observed in longitudinal prospective study of two adult patients with intractable epilepsy with 50% seizure improvement after initiation of ketogenic diets. This pilot study supports the possible anticonvulsant role of GSH in the brain.

Corrigendum to: Glutathione Increase and Seizure Burden Decrease in Patients with Intractable Epilepsy on Ketogenic Diet.

[This corrects the article DOI: 10.14581/jer.23001.].

Spatial assessments in texture analysis: what the radiologist needs to know.

To date, studies investigating radiomics-based predictive models have tended to err on the side of data-driven or exploratory analysis of many thousands of extracted features. In particular, spatial assessments of texture have proven to be especially adept at assessing for features of intratumoral heterogeneity in oncologic imaging, which likewise may correspond with tumor biology and behavior. These spatial assessments can be generally classified as spatial filters, which detect areas of rapid change within the grayscale in order to enhance edges and/or textures within an image, or neighborhood-based methods, which quantify gray-level differences of neighboring pixels/voxels within a set distance. Given the high dimensionality of radiomics datasets, data dimensionality reduction methods have been proposed in an attempt to optimize model performance in machine learning studies; however, it should be noted that these approaches should only be applied to training data in order to avoid information leakage and model overfitting. While area under the curve of the receiver operating characteristic is perhaps the most commonly reported assessment of model performance, it is prone to overestimation when output classifications are unbalanced. In such cases, confusion matrices may be additionally reported, whereby diagnostic cut points for model predicted probability may hold more clinical significance to clinical colleagues with respect to related forms of diagnostic testing.

Predicting Soft Tissue Sarcoma Response to Neoadjuvant Chemotherapy Using an MRI-Based Delta-Radiomics Approach.

OBJECTIVES: To evaluate the performance of machine learning-augmented MRI-based radiomics models for predicting response to neoadjuvant chemotherapy (NAC) in soft tissue sarcomas. METHODS: Forty-four subjects were identified retrospectively from patients who received NAC at our institution for pathologically proven soft tissue sarcomas. Only subjects who had both a baseline MRI prior to initiating chemotherapy and a post-treatment scan at least 2 months after initiating chemotherapy and prior to surgical resection were included. 3D ROIs were used to delineate whole-tumor volumes on pre- and post-treatment scans, from which 1708 radiomics features were extracted. Delta-radiomics features were calculated by subtraction of baseline from post-treatment values and used to distinguish treatment response through univariate analyses as well as machine learning-augmented radiomics analyses. RESULTS: Though only 4.74% of variables overall reached significance at p ≤ 0.05 in univariate analyses, Laws Texture Energy (LTE)-derived metrics represented 46.04% of all such features reaching statistical significance. ROC analyses similarly failed to predict NAC response, with AUCs of 0.40 (95% CI 0.22-0.58) and 0.44 (95% CI 0.26-0.62) for RF and AdaBoost, respectively. CONCLUSION: Overall, while our result was not able to separate NAC responders from non-responders, our analyses did identify a subset of LTE-derived metrics that show promise for further investigations. Future studies will likely benefit from larger sample size constructions so as to avoid the need for data filtering and feature selection techniques, which have the potential to significantly bias the machine learning procedures.

5-Aminolevulinic acid-enhanced fluorescence-guided treatment of high-grade glioma using angled endoscopic blue light visualization: technical case series with preliminary follow-up.

OBJECTIVE: 5-Aminolevulinic acid (5-ALA)-enhanced fluorescence-guided resection of high-grade glioma (HGG) using microscopic blue light visualization offers the ability to improve extent of resection (EOR); however, few descriptions of HGG resection performed using endoscopic blue light visualization are currently available. In this report, the authors sought to describe their surgical experience and patient outcomes of 5-ALA-enhanced fluorescence-guided resection of HGG using primary or adjunctive endoscopic blue light visualization. METHODS: The authors performed a retrospective review of prospectively collected data from 30 consecutive patients who underwent 5-ALA-enhanced fluorescence-guided biopsy or resection of newly diagnosed HGG was performed. Patient demographic data, tumor characteristics, surgical technique, EOR, tumor fluorescence patterns, and progression-free survival were recorded. RESULTS: In total, 30 newly diagnosed HGG patients were included for analysis. The endoscope was utilized for direct 5-ALA-guided port-based biopsy (n = 9), microscopic to endoscopic (M2E; n = 18) resection, or exoscopic to endoscopic (E2E; n = 3) resection. All endoscopic biopsies of fluorescent tissue were diagnostic. 5-ALA-enhanced tumor fluorescence was visible in all glioblastoma cases, but only in 50% of anaplastic astrocytoma cases and no anaplastic oligodendroglioma cases. Gross-total resection (GTR) was achieved in 10 patients in whom complete resection was considered safe, with 11 patients undergoing subtotal resection. In all cases, endoscopic fluorescence was more avid than microscopic fluorescence. The endoscope offered the ability to diagnose and resect additional tumor not visualized by the microscope in 83.3% (n = 10/12) of glioblastoma cases, driven by angled lenses and increased fluorescence facilitated by light source delivery within the cavity. Mean volumetric EOR was 90.7% in all resection patients and 98.8% in patients undergoing planned GTR. No complications were attributable to 5-ALA or blue light endoscopy. CONCLUSIONS: The blue light endoscope is a viable primary or adjunctive visualization platform for optimization of 5-ALA-enhanced HGG fluorescence. Implementation of the blue light endoscope to guide resection of HGG glioma is feasible and ergonomically favorable, with a potential advantage of enabling increased detection of tumor fluorescence in deep surgical cavities compared to the microscope.

Liver Fat Reduction After Gastric Banding and Associations with Changes in Insulin Sensitivity and ß-Cell Function.

OBJECTIVE: The aim of this study was to examine the relationship between changes in liver fat and changes in insulin sensitivity and ß-cell function 2 years after gastric banding surgery. METHODS: Data included 23 adults with the surgery who had prediabetes or type 2 diabetes for less than 1 year and BMI 30 to 40 kg/m2 at baseline. Body adiposity measures including liver fat content (LFC), insulin sensitivity (M/I), and ß-cell responses (acute, steady-state, and arginine-stimulated maximum C-peptide) were assessed at baseline and 2 years after surgery. Regression models were used to assess associations adjusted for age and sex. RESULTS: Two years after surgery, all measures of body adiposity, LFC, fasting and 2-hour glucose, and hemoglobin A1c significantly decreased; M/I significantly increased; and ß-cell responses adjusted for M/I did not change significantly. Among adiposity measures, reduction in LFC had the strongest association with M/I increase (r = -0.61, P = 0.003). Among ß-cell measures, change in LFC was associated with change in acute C-peptide response to arginine at maximal glycemic potentiation adjusted for M/I (r = 0.66, P = 0.007). Significant reductions in glycemic measures and increase in M/I were observed in individuals with LFC loss >2.5%. CONCLUSIONS: Reduction in LFC after gastric banding surgery appears to be an important factor associated with long-term improvements in insulin sensitivity and glycemic profiles in adults with obesity and prediabetes or early type 2 diabetes.

Smaller spared subcortical nuclei are associated with worse post-stroke sensorimotor outcomes in 28 cohorts worldwide.

Up to two-thirds of stroke survivors experience persistent sensorimotor impairments. Recovery relies on the integrity of spared brain areas to compensate for damaged tissue. Deep grey matter structures play a critical role in the control and regulation of sensorimotor circuits. The goal of this work is to identify associations between volumes of spared subcortical nuclei and sensorimotor behaviour at different timepoints after stroke. We pooled high-resolution T1-weighted MRI brain scans and behavioural data in 828 individuals with unilateral stroke from 28 cohorts worldwide. Cross-sectional analyses using linear mixed-effects models related post-stroke sensorimotor behaviour to non-lesioned subcortical volumes (Bonferroni-corrected, P < 0.004). We tested subacute (≤90 days) and chronic (≥180 days) stroke subgroups separately, with exploratory analyses in early stroke (≤21 days) and across all time. Sub-analyses in chronic stroke were also performed based on class of sensorimotor deficits (impairment, activity limitations) and side of lesioned hemisphere. Worse sensorimotor behaviour was associated with a smaller ipsilesional thalamic volume in both early (n = 179; d = 0.68) and subacute (n = 274, d = 0.46) stroke. In chronic stroke (n = 404), worse sensorimotor behaviour was associated with smaller ipsilesional putamen (d = 0.52) and nucleus accumbens (d = 0.39) volumes, and a larger ipsilesional lateral ventricle (d = -0.42). Worse chronic sensorimotor impairment specifically (measured by the Fugl-Meyer Assessment; n = 256) was associated with smaller ipsilesional putamen (d = 0.72) and larger lateral ventricle (d = -0.41) volumes, while several measures of activity limitations (n = 116) showed no significant relationships. In the full cohort across all time (n = 828), sensorimotor behaviour was associated with the volumes of the ipsilesional nucleus accumbens (d = 0.23), putamen (d = 0.33), thalamus (d = 0.33) and lateral ventricle (d = -0.23). We demonstrate significant relationships between post-stroke sensorimotor behaviour and reduced volumes of deep grey matter structures that were spared by stroke, which differ by time and class of sensorimotor measure. These findings provide additional insight into how different cortico-thalamo-striatal circuits support post-stroke sensorimotor outcomes.

Early Adiposity Rebound Predicts Obesity and Adiposity in Youth with Congenital Adrenal Hyperplasia.

INTRODUCTION: Youth with classical congenital adrenal hyperplasia (CAH) have higher prevalence of cardiometabolic risk factors such as obesity, abdominal adiposity, and hypertension. Patients with CAH also exhibit an earlier adiposity rebound (AR) compared to normative populations. However, the predictive relationship between AR and cardiometabolic risk factors needs to be better understood. METHODS: We performed a retrospective cohort study at a US tertiary pediatric center in youth with classical CAH due to 21-hydroxylase deficiency. AR was determined by cubic polynomial modeling. A subset of participants had fasting analytes, whole-body dual-energy X-ray absorptiometry, and magnetic resonance imaging as adolescents. RESULTS: In 42 youth with CAH (45.2% female, 54.8% Hispanic, and 90.5% salt-wasting form), the average age at AR was 3.4 ± 1.3 years. AR differed by BMI-z, with youth with obesity having an earlier AR (2.8 ± 1.0 years) compared to lean youth (4.1 ± 1.3 years, p = 0.001). However, AR did not differ by either CAH form or sex. Earlier AR predicted higher BMI-z at 7 and 12 years of age. In addition, earlier AR predicted increased central obesity (as measured by waist circumference, subcutaneous adipose tissue, and trunk fat) and total body fat in adolescence. AR was negatively correlated with bone age, and its relationships with HDL and hypertension were trending towards significance. CONCLUSIONS: AR in youth with classical CAH could serve as a useful clinical marker to identify those patients who are at higher risk for developing cardiometabolic risk factors during childhood and adolescence.

Perioperative Outcome of Suprarenal Resection of Vena Cava Without Reconstruction in Urologic Malignancies: A Case Series and Review of the Literature.

OBJECTIVE: To describe the feasibility and perioperative outcome of suprarenal resection of inferior vena cava (IVC) in urologic neoplasms without reconstruction. METHODS: We retrospectively reviewed the patients who underwent suprarenal resection of IVC without reconstruction for urologic neoplasms in our institution between September 2010 and October 2019. Patients' demographic, clinical, radiologic, and 90-day perioperative complications were recorded. RESULTS: Twenty-eight (79% male) patients with a median age of 59 (25-75) years were included in the study. Twenty-five (89%) of patients had renal cell carcinoma, 1 had renal leiomyosarcoma, and 2 had metastatic testicular teratoma. Twenty-two patients had Mayo level 3 thrombus, 3 had level 2, and 3 had level 4. The mean radiologic thrombus length was 12.6 cm. Eleven patients had radiologic bland thrombosis in the infrarenal IVC. Twenty-seven patients underwent open, and 1 robotic surgery. The median operating time was 411 (range 240-808) minutes, median blood loss was 3750 cc, and all but 1 patient received perioperative transfusion (median 11 units of packed red blood cells). Median hospital stay was 5 (3-50) days. Ninety-day complication rate was 35% (Clavien-Dindo grade I/II and III/IV were 21% and 14%, respectively). Four patients (14%) developed transient nondisabling leg edema. The 90-day mortality rate was 7%. CONCLUSION: Suprarenal inferior vena cava resection without reconstruction is feasible, yet high-risk operation that should be performed in experienced centers in selected patients with urologic malignancies.

Brain Segmentation From Computed Tomography of Healthy Aging and Geriatric Concussion at Variable Spatial Resolutions.

When properly implemented and processed, anatomic T 1-weighted magnetic resonance imaging (MRI) can be ideal for the noninvasive quantification of white matter (WM) and gray matter (GM) in the living human brain. Although MRI is more suitable for distinguishing GM from WM than computed tomography (CT), the growing clinical use of the latter technique has renewed interest in head CT segmentation. Such interest is particularly strong in settings where MRI is unavailable, logistically unfeasible or prohibitively expensive. Nevertheless, whereas MRI segmentation is a sophisticated and technically-mature research field, the task of automatically classifying soft brain tissues from CT remains largely unexplored. Furthermore, brain segmentation methods for MRI hold considerable potential for adaptation and application to CT image processing. Here we demonstrate this by combining probabilistic, atlas-based classification with topologically-constrained tissue boundary refinement to delineate WM, GM and cerebrospinal fluid (CSF) from head CT images. The feasibility and utility of this approach are revealed by comparison of MRI-only vs. CT-only segmentations in geriatric concussion victims with both MRI and CT scans. Comparison of the two segmentations yields mean Sørensen-Dice coefficients of 85.5 ± 4.6% (WM), 86.7 ± 5.6% (GM) and 91.3 ± 2.8% (CSF), as well as average Hausdorff distances of 3.76 ± 1.85 mm (WM), 3.43 ± 1.53 mm (GM) and 2.46 ± 1.27 mm (CSF). Bootstrapping results suggest that the segmentation approach is sensitive enough to yield WM, GM and CSF volume estimates within ~5%, ~4%, and ~3% of their MRI-based estimates, respectively. To our knowledge, this is the first 3D segmentation approach for CT to undergo rigorous within-subject comparison with high-resolution MRI. Results suggest that (1) standard-quality CT allows WM/GM/CSF segmentation with reasonable accuracy, and that (2) the task of soft brain tissue classification from CT merits further attention from neuroimaging researchers.