Conditional generative learning for medical image imputation.

Image imputation refers to the task of generating a type of medical image given images of another type. This task becomes challenging when the difference between the available images, and the image to be imputed is large. In this manuscript, one such application is considered. It is derived from the dynamic contrast enhanced computed tomography (CECT) imaging of the kidneys: given an incomplete sequence of three CECT images, we are required to impute the missing image. This task is posed as one of probabilistic inference and a generative algorithm to generate samples of the imputed image, conditioned on the available images, is developed, trained, and tested. The output of this algorithm is the "best guess" of the imputed image, and a pixel-wise image of variance in the imputation. It is demonstrated that this best guess is more accurate than those generated by other, deterministic deep-learning based algorithms, including ones which utilize additional information and more complex loss terms. It is also shown that the pixel-wise variance image, which quantifies the confidence in the reconstruction, can be used to determine whether the result of the imputation meets a specified accuracy threshold and is therefore appropriate for a downstream task.

Radiomics Correlation to CD68+ Tumor-Associated Macrophages in Clear Cell Renal Cell Carcinoma.

INTRODUCTION: Renal cell carcinoma (RCC) is the ninth most common cancer worldwide, with clear cell RCC (ccRCC) being the most frequent histological subtype. The tumor immune microenvironment (TIME) of ccRCC is an important factor to guide treatment, but current assessments are tissue-based, which can be time-consuming and resource-intensive. In this study, we used radiomics extracted from clinically performed computed tomography (CT) as a noninvasive surrogate for CD68 tumor-associated macrophages (TAMs), a significant component of ccRCC TIME. METHODS: TAM population was measured by CD68+/PanCK+ ratio and tumor-TAM clustering was measured by normalized K function calculated from multiplex immunofluorescence (mIF). A total of 1,076 regions on mIF slides from 78 patients were included. Radiomic features were extracted from multiphase CT of the ccRCC tumor. Statistical machine learning models, including random forest, Adaptive Boosting, and ElasticNet, were used to predict TAM population and tumor-TAM clustering. RESULTS: The best models achieved an area under the ROC curve of 0.81 (95% CI: [0.69, 0.92]) for TAM population and 0.77 (95% CI: [0.66, 0.88]) for tumor-TAM clustering, respectively. CONCLUSION: Our study demonstrates the potential of using CT radiomics-derived imaging markers as a surrogate for assessment of TAM in ccRCC for real-time treatment response monitoring and patient selection for targeted therapies and immunotherapies.

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.

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.

Sarcopenia and body fat change as risk factors for radiologic incisional hernia following robotic nephrectomy.

OBJECTIVE: To assess the effect of body muscle and fat metrics on the development of radiologic incisional hernia (IH) following robotic nephrectomy. MATERIALS AND METHODS: We retrospectively reviewed the records of patients who underwent robotic nephrectomy for kidney tumors between 2011 and 2017. All pre- and postoperative CTs were re-reviewed by experienced radiologists for detection of radiologic IH and calculation of the following metrics using Synapse 3D software: cross-sectional psoas muscle mass at the level of L3 and L4 as well as subcutaneous and visceral fat areas. Sarcopenia was defined as psoas muscle index below the lowest quartile. Cox proportional hazard model was constructed to examine the association between muscle and fat metrics and the risk of developing radiologic IH. RESULTS: A total of 236 patients with a median (IQR) age of 64 (54-70) years were included in this study. In a median (IQR) follow-up of 23 (14-38) months, 62 (26%) patients developed radiologic IH. On Cox proportional hazard model, we were unable to detect an association between sarcopenia and risk of IH development. In terms of subcutaneous fat change from pre-op, both lower and higher values were associated with IH development (HR (95% CI) 2.1 (1.2-3.4), p = 0.01 and 2.4 (1.4-4.1), p < 0.01 for < Q1 and ≥ Q3, respectively). Similar trend was found for visceral fat area changes from pre-op with a HR of 2.8 for < Q1 and 1.8 for ≥ Q3. CONCLUSION: Both excessive body fat gain and loss are associated with development of radiologic IH in patients undergoing robotic nephrectomy.

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.

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.

Weight Loss During Topiramate Treatment in a Severely Obese Adolescent with Congenital Adrenal Hyperplasia and Migraine

Youth with classical congenital adrenal hyperplasia (CAH) due to 21-hydroxylase deficiency exhibit an increased prevalence of obesity, early adiposity rebound, and increased abdominal adiposity compared to unaffected youth. Current obesity management in CAH largely focuses on lifestyle modifications. There is evidence that topiramate therapy is effective in reducing body mass index (BMI), as well as visceral adipose tissue (VAT), in unaffected adolescents with exogenous obesity. However, little is known about the efficacy of topiramate in patients with classical CAH. We report on a 17-year-old female with severe obesity and salt-wasting CAH due to 21-hydroxylase deficiency, who demonstrated reductions in BMI, as well as abdominal visceral and subcutaneous adipose tissue (SAT) while on topiramate therapy. The patient was diagnosed with classical CAH as a newborn with a 17-hydroxyprogesterone 11,000 ng/dL. She had a BMI over the 95th percentile at 3 years of age, followed by unremitting obesity. At 17 years old, she was started on topiramate to treat chronic migraines. Following three years of topiramate therapy, her BMI z-score decreased from +2.6 to +2.1. After four years of therapy, her waist circumference decreased from 110 to 101 cm, abdominal VAT decreased substantially by 34.2%, and abdominal SAT decreased by 25.6%. Topiramate therapy was associated with effective weight loss and reduced central adiposity in an adolescent with classical CAH and severe obesity, without any side effects. Further study is warranted regarding topiramate therapy in obese youth with classical CAH and increased central adiposity, who are at higher risk for significant morbidity.

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.].

Feasibility of using CT radiomic signatures for predicting CD8-T cell infiltration and PD-L1 expression in renal cell carcinoma.

Objectives: To identify computed tomography (CT)-based radiomic signatures of cluster of differentiation 8 (CD8)-T cell infiltration and programmed cell death ligand 1 (PD-L1) expression levels in patients with clear-cell renal cell carcinoma (ccRCC). Methods: Seventy-eight patients with pathologically confirmed localized ccRCC, preoperative multiphase CT and tumor resection specimens were enrolled in this retrospective study. Regions of interest (ROI) of the ccRCC volume were manually segmented from the CT images and processed using a radiomics panel comprising of 1708 metrics. The extracted metrics were used as inputs to three machine learning classifiers: Random Forest, AdaBoost, and ElasticNet to create radiomic signatures for CD8-T cell infiltration and PD-L1 expression, respectively. Results: Using a cut-off of 80 lymphocytes per high power field, 59 % were classified to CD8 highly infiltrated tumors and 41 % were CD8 non highly infiltrated tumors, respectively. An ElasticNet classifier discriminated between these two groups of CD8-T cells with an AUC of 0.68 (95 % CI, 0.55-0.80). In addition, based on tumor proportion score with a cut-off of > 1 % tumor cells expressing PD-L1, 76 % were PD-L1 positive and 24 % were PD-L1 negative. An Adaboost classifier discriminated between PD-L1 positive and PD-L1 negative tumors with an AUC of 0.8 95 % CI: (0.66, 0.95). 3D radiomics metrics of graylevel co-occurrence matrix (GLCM) and graylevel run-length matrix (GLRLM) metrics drove the performance for CD8-Tcell and PD-L1 classification, respectively. Conclusions: CT-radiomic signatures can differentiate tumors with high CD8-T cell infiltration with moderate accuracy and positive PD-L1 expression with good accuracy in ccRCC.

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.

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.

A Radiomic-based Machine Learning Algorithm to Reliably Differentiate Benign Renal Masses from Renal Cell Carcinoma.

BACKGROUND: A substantial proportion of patients undergo treatment for renal masses where active surveillance or observation may be more appropriate. OBJECTIVE: To determine whether radiomic-based machine learning platforms can distinguish benign from malignant renal masses. DESIGN, SETTING, AND PARTICIPANTS: A prospectively maintained single-institutional renal mass registry was queried to identify patients with a computed tomography-proven clinically localized renal mass who underwent partial or radical nephrectomy. INTERVENTION: Radiomic analysis of preoperative scans was performed. Clinical and radiomic variables of importance were identified through decision tree analysis, which were incorporated into Random Forest and REAL Adaboost predictive models. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary outcome was the degree of congruity between the virtual diagnosis and final pathology. Subanalyses were performed for small renal masses and patients who had percutaneous renal mass biopsies as part of their workup. Receiver operating characteristic curves were used to evaluate each model's discriminatory function. RESULTS AND LIMITATIONS: A total of 684 patients met the selection criteria. Of them, 76% had renal cell carcinoma; 57% had small renal masses, of which 73% were malignant. Predictive modeling differentiated benign pathology from malignant with an area under the curve (AUC) of 0.84 (95% confidence interval [CI] 0.79-0.9). In small renal masses, radiomic analysis yielded a discriminatory AUC of 0.77 (95% CI 0.69-0.85). When negative and nondiagnostic biopsies were supplemented with radiomic analysis, accuracy increased from 83.3% to 93.4%. CONCLUSIONS: Radiomic-based predictive modeling may distinguish benign from malignant renal masses. Clinical factors did not substantially improve the diagnostic accuracy of predictive models. Enhanced diagnostic predictability may improve patient selection before surgery and increase the utilization of active surveillance protocols. PATIENT SUMMARY: Not all kidney tumors are cancerous, and some can be watched. We evaluated a new method that uses radiographic features invisible to the naked eye to distinguish benign masses from true cancers and found that it can do so with acceptable accuracy.

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.

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.

Benchmarking Various Radiomic Toolkit Features While Applying the Image Biomarker Standardization Initiative toward Clinical Translation of Radiomic Analysis.

The image biomarkers standardization initiative (IBSI) was formed to address the standardization of extraction of quantifiable imaging metrics. Despite its effort, there remains a lack of consensus or established guidelines regarding radiomic feature terminology, the underlying mathematics and their implementation across various software programs. This creates a scenario where features extracted using different toolboxes cannot be used to build or validate the same model leading to a non-generalization of radiomic results. In this study, IBSI-established phantom and benchmark values were used to compare the variation of the radiomic features while using 6 publicly available software programs and 1 in-house radiomics pipeline. All IBSI-standardized features (11 classes, 173 in total) were extracted. The relative differences between the extracted feature values from the different software programs and the IBSI benchmark values were calculated to measure the inter-software agreement. To better understand the variations, features are further grouped into 3 categories according to their properties: 1) morphology, 2) statistic/histogram and 3)texture features. While a good agreement was observed for a majority of radiomics features across the various tested programs, relatively poor agreement was observed for morphology features. Significant differences were also found in programs that use different gray-level discretization approaches. Since these software programs do not include all IBSI features, the level of quantitative assessment for each category was analyzed using Venn and UpSet diagrams and quantified using two ad hoc metrics. Morphology features earned lowest scores for both metrics, indicating that morphological features are not consistently evaluated among software programs. We conclude that radiomic features calculated using different software programs may not be interchangeable. Further studies are needed to standardize the workflow of radiomic feature extraction.

PNPLA3 Genotype, Arachidonic Acid Intake, and Unsaturated Fat Intake Influences Liver Fibrosis in Hispanic Youth with Obesity.

Non-alcoholic fatty liver disease impacts 15.2% of Hispanic adolescents and can progress to a build-up of scared tissue called liver fibrosis. If diagnosed early, liver fibrosis may be reversible, so it is necessary to understand risk factors. The aims of this study in 59 Hispanic adolescents with obesity were to: (1) identify potential biological predictors of liver fibrosis and dietary components that influence liver fibrosis, and (2) determine if the association between dietary components and liver fibrosis differs by PNPLA3 genotype, which is highly prevalent in Hispanic adolescents and associated with elevated liver fat. We examined liver fat and fibrosis, genotyped for PNPLA3 gene, and assessed diet via 24-h diet recalls. The prevalence of increased fibrosis was 20.9% greater in males, whereas participants with the GG genotype showed 23.7% greater prevalence. Arachidonic acid was associated with liver fibrosis after accounting for sex, genotype, and liver fat (ß = 0.072, p = 0.033). Intakes of several dietary types of unsaturated fat have different associations with liver fibrosis by PNPLA3 genotype after accounting for sex, caloric intake, and liver fat. These included monounsaturated fat (ßCC/CG = -0.0007, ßGG = 0.03, p-value = 0.004), polyunsaturated fat (ßCC/CG = -0.01, ßGG = 0.02, p-value = 0.01), and omega-6 (ßCC/CG = -0.0102, ßGG = 0.028, p-value = 0.01). Results from this study suggest that reduction of arachidonic acid and polyunsaturated fatty acid intake might be important for the prevention of non-alcoholic fatty liver disease progression, especially among those with PNPLA3 risk alleles.

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.

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.