Opportunistic CT for Prediction of Adverse Postoperative Events in Patients with Spinal Metastases.

The purpose of this study was to assess the value of body composition measures obtained from opportunistic abdominal computed tomography (CT) in order to predict hospital length of stay (LOS), 30-day postoperative complications, and reoperations in patients undergoing surgery for spinal metastases. 196 patients underwent CT of the abdomen within three months of surgery for spinal metastases. Automated body composition segmentation and quantifications of the cross-sectional areas (CSA) of abdominal visceral and subcutaneous adipose tissue and abdominal skeletal muscle was performed. From this, 31% (61) of patients had postoperative complications within 30 days, and 16% (31) of patients underwent reoperation. Lower muscle CSA was associated with increased postoperative complications within 30 days (OR [95% CI] = 0.99 [0.98-0.99], p = 0.03). Through multivariate analysis, it was found that lower muscle CSA was also associated with an increased postoperative complication rate after controlling for the albumin, ASIA score, previous systemic therapy, and thoracic metastases (OR [95% CI] = 0.99 [0.98-0.99], p = 0.047). LOS and reoperations were not associated with any body composition measures. Low muscle mass may serve as a biomarker for the prediction of complications in patients with spinal metastases. The routine assessment of muscle mass on opportunistic CTs may help to predict outcomes in these patients.

Cross-sectional areas of rotator cuff muscles in males without tears on shoulder MRI.

OBJECTIVE: To establish reference values of rotator cuff (RC) cross sectional area (CSA) in males. MATERIALS AND METHODS: We retrospectively analyzed shoulder MRIs from 500 patients aged 13-78 years, grouped as follows (N=100 in each): <20, 20-30, 30-40, 40-50, >50 years. All examinations were reviewed to exclude prior surgery, tears, or significant RC pathology. We segmented a standardized T1 sagittal MR image in each case to obtain CSA of supraspinatus (SUP), infraspinatus/teres minor (INF), and subscapularis (SUB) muscles. Across age groups, we recorded individual and total muscle CSA. We also performed ratios between individual muscle CSA and total CSA to examine total muscle mass contribution over age groups. We tested for differences between age groups controlled for BMI. RESULTS: CSAs for SUP, INF, SUB, and total RC CSA were lower in subjects >50 years compared to all other groups (P<0.003 for all comparisons), persisting after controlling for BMI (P<0.03). Relative contribution of SUP CSA to total RC CSA was stable across age groups (P>0.32). INF CSA relative to total RC CSA increased with age, whereas SUB decreased (P<0.005). Subjects >50 years showed lower SUP (-15%), INF (-6%), and SUB (-21%) CSA, when compared to mean CSAs of all subjects <50 years. Total RC CSA significantly correlated with age (r=-0.34, P<0.001), persisting after controlling for BMI (r=-0.42, P<0.001). CONCLUSION: RC muscles in male subjects with no tears on MRI show decreasing CSA with age, independent of BMI.

Original research: utilization of a convolutional neural network for automated detection of lytic spinal lesions on body CTs.

OBJECTIVE: To develop, train, and test a convolutional neural network (CNN) for detection of spinal lytic lesions in chest, abdomen, and pelvis CT scans. MATERIALS AND METHODS: Cases of malignant spinal lytic lesions in CT scans were identified. Images were manually segmented for the following classes: (i) lesion, (ii) normal bone, (iii) background. If more than one lesion was on a single slice, all lesions were segmented. Images were stored as 128×128 pixel grayscale, with 10% segregated for testing. The training pipeline of the dataset included histogram equalization and data augmentation. A model was trained on Keras/Tensorflow using an 80/20 training/validation split, based on U-Net architecture. Additional testing of the model was performed on 1106 images of healthy controls. Global sensitivity measured detection of any lesion on a single image. Local sensitivity and positive predictive value (PPV) measured detection of all lesions on an image. Global specificity measured false positive rate in non-pathologic bone. RESULTS: Six hundred images were obtained for model creation. The training set consisted of 540 images, which was augmented to 20,000. The test set consisted of 60 images. Model training was performed in triplicate. Mean Dice scores were 0.61 for lytic lesion, 0.95 for normal bone, and 0.99 for background. Mean global sensitivity was 90.6%, local sensitivity was 74.0%, local PPV was 78.3%, and global specificity was 63.3%. At least one false positive lesion was noted in 28.8-44.9% of control images. CONCLUSION: A task-trained CNN showed good sensitivity in detecting spinal lytic lesions in axial CT images.

Bone marrow adipocytes fuel emergency hematopoiesis after myocardial infarction.

After myocardial infarction (MI), emergency hematopoiesis produces inflammatory myeloid cells that accelerate atherosclerosis and promote heart failure. Since the balance between glycolysis and mitochondrial metabolism regulates hematopoietic stem cell homeostasis, metabolic cues may influence emergency myelopoiesis. Here, we show in humans and female mice that hematopoietic progenitor cells increase fatty acid metabolism after MI. Blockade of fatty acid oxidation by deleting carnitine palmitoyltransferase (Cpt1A) in hematopoietic cells of Vav1Cre/+Cpt1Afl/fl mice limited hematopoietic progenitor proliferation and myeloid cell expansion after MI. We also observed reduced bone marrow adiposity in humans, pigs and mice following MI. Inhibiting lipolysis in adipocytes using AdipoqCreERT2Atglfl/fl mice or local depletion of bone marrow adipocytes in AdipoqCreERT2iDTR mice also curbed emergency hematopoiesis. Furthermore, systemic and regional sympathectomy prevented bone marrow adipocyte shrinkage after MI. These data establish a critical role for fatty acid metabolism in post-MI emergency hematopoiesis.

Role of renin-angiotensin-aldosterone system activation and other metabolic variables in relation to arterial inflammation in HIV.

BACKGROUND: Arterial inflammation remains increased among persons with HIV (PWH) compared with persons without HIV (PWOH) even when controlling for traditional risk factors. We sought to understand whether increased renin-angiotensin-aldosterone system (RAAS) activation may be related to arterial inflammation in PWH and when compared with PWOH. DESIGN: Twenty PWH and 9 PWOH followed a controlled, standardized low and liberal sodium diet to simulate a RAAS-activated and RAAS-suppressed state, respectively. We measured serum lipoprotein-associated phospholipase A2 (LpPLA2) concentrations following both conditions to assess the physiologic dynamics of aldosterone in relation to arterial inflammation. RESULTS: LpPLA2 levels were significantly higher among PWH versus PWOH during both the RAAS-activated state[5.3(4.2, 6.1) versus 4.0(3.0, 4.8)nmol/L, median(interquartile range),p = .01]) and RAAS-suppressed state[4.4(3.9, 5.3) versus 3.8(3.4, 4.1)nmol/L,p = .01]. Among PWH, but not PWOH, LpPLA2 increased significantly with RAAS activation(p = .03). LpPLA2 levels measured during the RAAS-suppressed state among PWH remained relatively higher than LpPLA2 levels under both conditions among PWOH. Log LpPLA2 was related to log aldosterone during the RAAS-activated state(r = .39,p = .04) among all participants. Log LpPLA2 was correlated with visceral fat(r = .46,p = .04) and log systolic blood pressure(r = .57,p = .009) during a RAAS-activated state when an increase in aldosterone was stimulated in HIV. CONCLUSION: LpPLA2 is increased during a RAAS-activated state among PWH, but not among PWOH. Further, LpPLA2 was increased in both RAAS-activated and suppressed states in PWH compared with PWOH. These data suggest a biological link between increased aldosterone and arterial inflammation in this population. Future studies should test RAAS blockade on arterial inflammation as a targeted treatment approach in HIV.

Body Composition Predictors of Adverse Postoperative Events in Patients Undergoing Surgery for Long Bone Metastases.

INTRODUCTION: Body composition assessed using opportunistic CT has been recently identified as a predictor of outcome in patients with cancer. The purpose of this study was to determine whether the cross-sectional area (CSA) and the attenuation of abdominal subcutaneous adipose tissue, visceral adipose tissue (VAT), and paraspinous and abdominal muscles are the predictors of length of hospital stay, 30-day postoperative complications, and revision surgery in patients treated for long bone metastases. METHODS: A retrospective database of patients who underwent surgery for long bone metastases from 1999 to 2017 was used to identify 212 patients who underwent preoperative abdominal CT. CSA and attenuation measurements for subcutaneous adipose tissue, VAT, and muscles were taken at the level of L4 with the aid of an in-house segmentation algorithm. Bivariate and multivariate linear and logistic regression models were created to determine associations between body composition measurements and outcomes while controlling for confounders, including primary tumor, metastasis location, and preoperative albumin. RESULTS: On multivariate analysis, increased VAT CSA {regression coefficient (r) (95% confidence interval [CI]); 0.01 (0.01 to 0.02); P < 0.01} and decreased muscle attenuation (r [95% CI] -0.07 [-0.14 to -0.01]; P = 0.04) were associated with an increased length of hospital stay. In bivariate analysis, increased muscle CSA was associated with increased chance of revision surgery (odds ratio [95% CI]; 1.02 [1.01 to 1.03]; P = 0.04). No body composition measurements were associated with postoperative complications within 30 days. DISCUSSION: Body composition measurements assessed using opportunistic CT predict adverse postoperative outcomes in patients operated for long bone metastases.

Body composition predictors of mortality in patients undergoing surgery for long bone metastases.

BACKGROUND AND OBJECTIVES: Body composition measurements using computed tomography (CT) may serve as imaging biomarkers of survival in patients with and without cancer. This study assesses whether body composition measurements obtained on abdominal CTs are independently associated with 90-day and 1-year mortality in patients with long-bone metastases undergoing surgery. METHODS: This single institutional retrospective study included 212 patients who had undergone surgery for long-bone metastases and had a CT of the abdomen within 90 days before surgery. Quantification of cross-sectional areas (CSA) and CT attenuation of abdominal subcutaneous adipose tissue, visceral adipose tissue, and paraspinous and abdominal muscles were performed at L4. Multivariate Cox proportional-hazards analyses were performed. RESULTS: Sarcopenia was independently associated with 90-day mortality (hazard ratio [HR] = 1.87; 95% confidence interval [CI] = 1.11-3.16; p = 0.019) and 1-year mortality (HR = 1.50; 95% CI = 1.02-2.19; p = 0.038) in multivariate analysis while controlling for clinical variables such as primary tumors, comorbidities, and chemotherapy. Abdominal fat CSAs and muscle attenuation were not associated with mortality. CONCLUSIONS: The presence of sarcopenia assessed by CT is predictive of 90-day and 1-year mortality in patients undergoing surgery for long-bone metastases. This body composition measurement can be used as novel imaging biomarker supplementing existing prognostic tools to optimize patient selection for surgery and improve shared decision making.

Metatarsal Bone Marrow Edema on Magnetic Resonance Imaging and Its Correlation to Bone Stress Injuries in Male Collegiate Basketball Players.

BACKGROUND: The presence of bone marrow edema (BME) on magnetic resonance imaging (MRI) has been used to evaluate for bone stress injuries in athletes. PURPOSE: To examine the prevalence of MRI findings, including BME, in a single male collegiate basketball team before and after a single season and to assess its association with clinically symptomatic metatarsal bone stress injuries. STUDY DESIGN: Cohort Study; Level of evidence, 3. METHODS: A total of 16 men on a single collegiate basketball team (mean age, 20.0 ± 1.8 years) underwent 1.5-T MRI focused on both midfeet during the preseason, and 13 underwent repeat MRI during the postseason. MRI findings included the presence of BME and the radiographic classification of the bone stress injury (grades 1-4). Injury surveillance performed by athletic trainers was used to identify metatarsal bone stress injuries over the course of the season. RESULTS: Preseason MRI demonstrated metatarsal BME in 5 of the 16 participants, and postseason MRI demonstrated metatarsal BME in 4 of the 13 participants. All 4 of the participants with postseason BME had MRI findings of BME in the same metatarsals. Compared to those without BME, participants with metatarsal BME had a shorter history of basketball exposure (preseason: 10.4 ± 4.1 vs 14.2 ± 1.9 years, respectively [P = .023]; postseason: 9.6 ± 4.1 vs 14.0 ± 2.1 years, respectively [P = .024]), and those with postseason BME had started playing at an older age (9.8 ± 4.3 vs 6.2 ± 1.6 years, respectively; P = .050). The preseason MRI classification for metatarsals included grade 1 (n = 3), followed by grades 2 and 3 (n = 2 each). In the 4 participants with postseason MRI findings, the grade increased from 1 to 4 in 1 participant and was stable in the other 3. No participants were diagnosed clinically with a metatarsal bone stress injury during the season. BME of the sesamoids was identified in 6 participants, who trended toward being older (21.0 ± 2.2 vs 19.4 ± 1.3 years, respectively; P < .10), with the abnormalities persisting on postseason MRI in all players. CONCLUSION: Collegiate male basketball players may have a high prevalence of BME, often without associated symptoms. The absence of foot pain or a corresponding diagnosis of a metatarsal bone stress injury in this study suggests that MRI findings of BME in asymptomatic athletes should be interpreted with caution.

Body composition predictors of mortality on computed tomography in patients with spinal metastases undergoing surgical treatment.

BACKGROUND CONTEXT: Although survival of patients with spinal metastases has improved over the last decades due to advances in multi-modal therapy, there are currently no reliable predictors of mortality. Body composition measurements obtained using computed tomography (CT) have been recently proposed as biomarkers for survival in patients with and without cancer. Patients with cancer routinely undergo CT for staging or surveillance of therapy. Body composition assessed using opportunistic CTs might be used to determine survival in patients with spinal metastases. PURPOSE: The purpose of this study was to determine the value of body composition measures obtained on opportunistic abdomen CTs to predict 90-day and 1-year mortality in patients with spinal metastases undergoing surgery. We hypothesized that low muscle and abdominal fat mass were positive predictors of mortality. STUDY DESIGN: Retrospective study at a single tertiary care center in the United States. PATIENT SAMPLE: This retrospective study included 196 patients between 2001 and 2016 that were 18 years of age or older, underwent surgical treatment for spinal metastases, and had a preoperative CT of the abdomen within three months prior to surgery. OUTCOME MEASURES: Ninety-day and 1-year mortality by any cause. METHODS: Quantification of cross-sectional areas (CSA) and CT attenuation of abdominal subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and paraspinous and abdominal skeletal muscle were performed on CT images at the level of L4 using an in-house automated algorithm. Sarcopenia was determined by total muscle CSA (cm2) divided by height squared (m2) with cutoff values of <52.4 cm2/m2 for men and <38.5 cm2/m2 for women. Bivariate and multivariate Cox proportional-hazard analyses were used to determine the associations between body compositions and 90-day and 1-year mortality. RESULTS: The median age was 62 years (interquartile range=53-70). The mortality rate for 90-day was 24% and 1-year 54%. The presence of sarcopenia was associated with an increased 1-year mortality rate of 66% compared with a 1-year mortality rate of 41% in patients without sarcopenia (hazard ratio, 1.68; 95% confidence interval, 1.08-2.61; p=.02) after adjusting for various clinical factors including primary tumor type, ECOG performance status, additional metastases, neurology status, and systemic therapy. Additional analysis showed an association between sarcopenia and increased 1-year mortality when controlling for the prognostic modified Bauer score (HR, 1.58; 95%CI, 1.04-2.40; p=.03). Abdominal fat CSAs or muscle attenuation were not independently associated with mortality. CONCLUSIONS: The presence of sarcopenia is associated with an increased risk of 1-year mortality for patients surgically treated for spinal metastases. Sarcopenia retained an independent association with mortality when controlling for the prognostic modified Bauer score. This implies that body composition measurements such as sarcopenia could serve as novel biomarkers for prediction of mortality and may supplement other existing prognostic tools to improve shared decision making for patients with spinal metastases that are contemplating surgical treatment.

Automated detection and segmentation of sclerotic spinal lesions on body CTs using a deep convolutional neural network.

PURPOSE: To develop a deep convolutional neural network capable of detecting spinal sclerotic metastases on body CTs. MATERIALS AND METHODS: Our study was IRB-approved and HIPAA-compliant. Cases of confirmed sclerotic bone metastases in chest, abdomen, and pelvis CTs were identified. Images were manually segmented for 3 classes: background, normal bone, and sclerotic lesion(s). If multiple lesions were present on a slice, all lesions were segmented. A total of 600 images were obtained, with a 90/10 training/testing split. Images were stored as 128 × 128 pixel grayscale and the training dataset underwent a processing pipeline of histogram equalization and data augmentation. We trained our model from scratch on Keras/TensorFlow using an 80/20 training/validation split and a U-Net architecture (64 batch size, 100 epochs, dropout 0.25, initial learning rate 0.0001, sigmoid activation). We also tested our model's true negative and false positive rate with 1104 non-pathologic images. Global sensitivity measured model detection of any lesion on a single image, local sensitivity and positive predictive value (PPV) measured model detection of each lesion on a given image, and local specificity measured the false positive rate in non-pathologic bone. RESULTS: Dice scores were 0.83 for lesion, 0.96 for non-pathologic bone, and 0.99 for background. Global sensitivity was 95% (57/60), local sensitivity was 92% (89/97), local PPV was 97% (89/92), and local specificity was 87% (958/1104). CONCLUSION: A deep convolutional neural network has the potential to assist in detecting sclerotic spinal metastases.

Body composition predictors of outcome in patients with COVID-19.

BACKGROUND/OBJECTIVE: Obesity is a strong risk factor for adverse outcomes in patients hospitalized with COVID-19, however, the distribution of fat and the amount of muscle mass are more accurate risk factors than BMI. The objective of this study was to assess body composition measures obtained on opportunistic abdominal CTs as predictors of outcome in patients hospitalized with COVID-19. We hypothesized that elevated visceral and intermuscular adipose tissue would be associated with adverse outcome. SUBJECTS/METHODS: Our retrospective study was IRB-approved and HIPAA-compliant. The study group comprised 124 patients (median age: 68 years, IQR: 56, 77; 59 weeks, 65 months) who were admitted with COVID-19 to a single hospital and who had undergone abdominal CT for clinical purposes. Visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intermuscular adipose tissue (IMAT), and paraspinal and abdominal muscle cross-sectional areas (CSA) were assessed. Clinical information including prognostic factors, time of admission to the intensive care unit (ICU) and time of death within 28 days were obtained. Multivariate time-to-event competing risk models were fitted to estimate the hazard ratio (HR) for a composite outcome of ICU admission/mortality associated with a one standard deviation increase in each body compositional measure. Each model was adjusted for age, sex, race, BMI, and cardiometabolic comorbidities. RESULTS: There were 50 patients who were admitted to the ICU or deceased over a median time of 1 day [IQR 1, 6] from hospital admission. Higher VAT/SAT ratio (HR of 1.30; 95% CI 1.04-1.62, p = 0.022) and higher IMAT CSA (HR of 1.44; 95% CI 1.10-1.89, p = 0.008) were associated with a reduced time to ICU admission or death in adjusted models. CONCLUSION: VAT/SAT and IMAT are predictors of adverse outcome in patients hospitalized with COVID-19, independent of other established prognostic factors. This suggests that body composition measures may serve as novel biomarkers of outcome in patients with COVID-19.

Bone marrow adipose tissue composition following high-caloric feeding and fasting.

BACKGROUND: Bone marrow adipose tissue (BMAT) plays a role in systemic energy metabolism and responds to nutritional changes. Chronic starvation as well as visceral adiposity are associated with BMAT accumulation. Two types of BMAT have been described which differ in anatomic location (proximal-regulated-rBMAT vs distal-constitutive-cBMAT) and composition (higher unsaturated lipids of cBMAT compared to rBMAT). OBJECTIVE: To determine the response of BMAT composition to short-term high-caloric feeding and fasting. We hypothesized that high-feeding and caloric restriction would be associated with differences in BMAT composition according to the skeletal site. MATERIALS AND METHODS: We examined 23 healthy subjects (13 m, 10 f, mean age 33 ± 7 years, BMI 26 ± 1.5 kg/m2) who were admitted for a 10-day high-caloric stay (caloric intake with goal to achieve 7% weight gain) followed by discharge home for 13-18 days to resume normal diet (stabilization period), followed by a 10-day fasting stay (no caloric intake). Subjects underwent single voxel proton MR spectroscopy (1H-MRS) at 3T of the lumbar spine (L4) (rBMAT), the femoral diaphysis and distal tibial metaphysis (cBMAT) to determine BMAT composition (unsaturation index, UI and saturation index, SI). Within group comparisons were performed by the Wilcoxon signed rank test. RESULTS: After the high-calorie visit, SI of L4 increased compared to baseline (0.62 ± 0.27 to 0.70 ± 0.28, p = 0.02), and there was a trend of an increase in femoral SI and UI (p ≥ 0.07), while there was no significant change in tibial BMAT (p ≥ 0.13). During the stabilization period, SI of L4 decreased (0.70 ± 0.28 to 0.57 ± 0.21, p < 0.0001) and SI of the femoral diaphysis decreased (5.37 ± 2.27 to 5.09 ± 2.43, p = 0.03), while there was no significant change in UI or tibial BMAT (p ≥ 0.14). During the fasting period, SI of L4 increased (0.57 ± 0.21 to 0.63 ± 0.30, p = 0.03), while there was no change in UI (p = 0.7). SI and UI of femoral diaphysis decreased (5.09 ± 2.43 to 4.68 ± 2.15, p = 0.03, and 0.62 ± 0.42 to 0.47 ± 0.37, p = 0.02, respectively) and UI of the tibial metaphysis decreased (1.48 ± 0.49 to 1.24 ± 0.57, p = 0.04). CONCLUSION: 1H-MRS is able to quantify BMAT composition during short-term nutritional challenges, showing a significant increase in SI of rBMAT during high caloric feeding and a differential response to fasting with an increase in SI of rBMAT and a decrease in SI and UI of femoral cBMAT and decrease in UI of tibial cBMAT.

Changes in marrow adipose tissue in relation to changes in bone parameters following estradiol replacement in adolescent and young adult females with functional hypothalamic amenorrhea.

CONTEXT: Low energy availability causes disruption of hypothalamic gonadotropin-releasing hormone secretion leading to functional hypothalamic amenorrhea (FHA) and hypoestrogenism, which in turn contributes to decreased bone mineral density (BMD) and increased bone marrow adipose tissue (MAT). Transdermal estradiol administration in physiologic doses increases BMD in adolescents and adults with FHA. However, the impact of estrogen replacement on MAT in relation to changes in BMD has not been studied in adolescents and young adults. We hypothesized that physiologic estrogen replacement would lead to decreases in MAT, associated with increases in BMD. METHODS AND MATERIALS: We studied 15 adolescent and young adult females with FHA (14-25 years). All participants received a17ß- estradiol transdermal patch at a dose of 0.1 mg/day (applied twice weekly) for 12 months. Participants also received cyclic progestin for 10-12 days each month. We quantified MAT (lipid/water ratio) of the fourth lumbar (L4) vertebral body and femoral diaphysis by single proton (1H)-magnetic resonance spectroscopy, and compartmental volumetric BMD of the distal radius and tibia using high-resolution peripheral quantitative computed tomography. RESULTS: Transdermal estradiol therapy over 12 months resulted in a decrease in MAT at the lumbar (L4) vertebra from 0.92 ± 0.55 at baseline to 0.63 ± 0.29 at 12-months (p = 0.008), and an increase in radial and tibial cortical vBMD (p = 0.006, p = 0.0003). Changes in L4 MAT trended to be inversely associated with changes in radial cortical vBMD (rho = -0.47, p = 0.08). CONCLUSION: We show that in adolescent and young adult girls with FHA, MAT decreases following transdermal estrogen therapy and these changes are associated with increased cortical vBMD.

Deep learning method for segmentation of rotator cuff muscles on MR images.

OBJECTIVE: To develop and validate a deep convolutional neural network (CNN) method capable of (1) selecting a specific shoulder sagittal MR image (Y-view) and (2) automatically segmenting rotator cuff (RC) muscles on a Y-view. We hypothesized a CNN approach can accurately perform both tasks compared with manual reference standards. MATERIAL AND METHODS: We created 2 models: model A for Y-view selection and model B for muscle segmentation. For model A, we manually selected shoulder sagittal T1 Y-views from 258 cases as ground truth to train a classification CNN (Keras/Tensorflow, Inception v3, 16 batch, 100 epochs, dropout 0.2, learning rate 0.001, RMSprop). A top-3 success rate evaluated model A on 100 internal and 50 external test cases. For model B, we manually segmented subscapularis, supraspinatus, and infraspinatus/teres minor on 1048 sagittal T1 Y-views. After histogram equalization and data augmentation, the model was trained from scratch (U-Net, 8 batch, 50 epochs, dropout 0.25, learning rate 0.0001, softmax). Dice (F1) score determined segmentation accuracy on 105 internal and 50 external test images. RESULTS: Model A showed top-3 accuracy > 98% to select an appropriate Y-view. Model B produced accurate RC muscle segmentations with mean Dice scores > 0.93. Individual muscle Dice scores on internal/external datasets were as follows: subscapularis 0.96/0.93, supraspinatus 0.97/0.96, and infraspinatus/teres minor 0.97/0.95. CONCLUSIONS: Our results show overall accurate Y-view selection and automated RC muscle segmentation using a combination of deep CNN algorithms.

Clinical Predictors of Liver Fibrosis Presence and Progression in Human Immunodeficiency Virus-Associated Nonalcoholic Fatty Liver Disease.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) affects more than one-third of people living with human immunodeficiency virus (HIV). Nonetheless, its natural history is poorly understood, including which patients are most likely to have a progressive disease course. METHODS: We leveraged a randomized trial of the growth hormone-releasing hormone analogue tesamorelin to treat NAFLD in HIV. Sixty-one participants with HIV-associated NAFLD were randomized to tesamorelin or placebo for 12 months with serial biopsies. RESULTS: In all participants with baseline biopsies (n = 58), 43% had hepatic fibrosis. Individuals with fibrosis had higher NAFLD Activity Score (NAS) (mean ± standard deviation [SD], 3.6 ± 2.0 vs 2.0 ± 0.8; P < .0001) and visceral fat content (mean ± SD, 284 ± 91 cm2 vs 212 ± 95 cm2; P = .005), but no difference in hepatic fat or body mass index. Among placebo-treated participants with paired biopsies (n = 24), 38% had hepatic fibrosis progression over 12 months. For each 25 cm2 higher visceral fat at baseline, odds of fibrosis progression increased by 37% (odds ratio, 1.37 [95% confidence interval, 1.03-2.07]). There was no difference in baseline NAS between fibrosis progressors and nonprogressors, though NAS rose over time in the progressor group (mean ± SD, 1.1 ± 0.8 vs -0.5 ± 0.6; P < .0001). CONCLUSIONS: In this longitudinal study of HIV-associated NAFLD, high rates of hepatic fibrosis and progression were observed. Visceral adiposity was identified as a novel predictor of worsening fibrosis. In contrast, baseline histologic characteristics did not relate to fibrosis progression.

Effect of recombinant human growth hormone on liver fat content in young adults with nonalcoholic fatty liver disease.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is highly prevalent in young adults with obesity. Obesity is associated with relative growth hormone (GH) deficiency, and data from animal studies and from humans with pituitary GH deficiency suggest a role for GH deficiency in the pathogenesis of NAFLD. The effects of GH on NAFLD in those with obesity are unknown, however, prompting this pilot study to assess effects of GH administration on measures of NAFLD in young adults. METHODS: Twenty-four men and women aged 18-29 years with BMI ≥ 30 kg/m2 , hepatic fat fraction (HFF) ≥ 5% on proton magnetic resonance spectroscopy (1 H-MRS) and insulin-like growth factor 1 (IGF-1) z-score ≤ 0 were randomized to treatment with recombinant human GH (rhGH) versus no treatment for 24 weeks. The primary endpoint was change in HFF. RESULTS: Compared to no treatment, the effect size of rhGH on absolute HFF over 24 weeks was -3.3% (95% confidence interval: -7.8%, 1.2%; p = .14). At 24 weeks, HFF < 5% was achieved in 5 of 9 individuals receiving rhGH versus 1 of 9 individuals receiving no treatment (p = .04). rhGH did not significantly reduce ALT, AST or GGT. Serum IGF-1 increased as expected with rhGH treatment, and there were no changes in fasting lipids, C-reactive protein, fasting glucose or 2-h glucose following an oral glucose tolerance test. CONCLUSION: Data from this pilot study suggest that rhGH treatment in young adults with obesity and NAFLD may have benefits to reduce liver fat content, although larger studies are needed to confirm this effect.

Quantitative Imaging of Body Composition.

Body composition refers to the amount and distribution of lean tissue, adipose tissue, and bone in the human body. Lean tissue primarily consists of skeletal muscle; adipose tissue comprises mostly abdominal visceral adipose tissue and abdominal and nonabdominal subcutaneous adipose tissue. Hepatocellular and myocellular lipids are also fat pools with important metabolic implications. Importantly, body composition reflects generalized processes such as increased adiposity in obesity and age-related loss of muscle mass known as sarcopenia.In recent years, body composition has been extensively studied quantitatively to predict overall health. Multiple imaging methods have allowed precise estimates of tissue types and provided insights showing the relationship of body composition to varied pathologic conditions. In this review article, we discuss different imaging methods used to quantify body composition and describe important anatomical locations where target tissues can be measured.

MRI texture analysis in acromegaly and its role in predicting response to somatostatin receptor ligands.

PURPOSE: Given the paucity of reliable predictors of tumor recurrence, progression, or response to somatostatin receptor ligand (SRL) therapy in acromegaly, we attempted to determine whether preoperative MR image texture was predictive of these clinical outcomes. We also determined whether image texture could differentiate somatotroph adenomas from non-functioning pituitary adenomas (NFPAs). METHODS: We performed a retrospective study of patients with acromegaly due to a macroadenoma who underwent transsphenoidal surgery at our institution between 2007 and 2015. Clinical data were extracted from electronic medical records. MRI texture analysis was performed on preoperative non-enhanced T1-weighted images using ImageJ (NIH). Logistic and Cox models were used to determine if image texture parameters predicted outcomes. RESULTS: Eighty-nine patients had texture parameters measured, which were compared to that of NFPAs, while 64 of these patients had follow-up and were included in the remainder of analyses. Minimum pixel intensity, skewness, and kurtosis were significantly different in somatotroph adenomas versus NFPAs (area under the receiver operating characteristic curve, 0.7771, for kurtosis). Furthermore, those with a maximum pixel intensity above the median had an increased odds of IGF-I normalization on SRL therapy (OR 5.96, 95% CI 1.33-26.66), which persisted after adjusting for several potential predictors of response. Image texture did not predict tumor recurrence or progression. CONCLUSION: Our data suggest that MRI texture analysis can distinguish NFPAs from somatotroph macroadenomas with good diagnostic accuracy and can predict normalization of IGF-I with SRL therapy.

Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment.

OBJECTIVE: To develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized that a deep CNN approach would achieve high accuracy when compared to manual segmentations as the reference standard. MATERIALS AND METHODS: We manually segmented 200 axial CT images at the supra-acetabular level in 200 subjects, labeling background, subcutaneous adipose tissue (SAT), muscle, inter-muscular adipose tissue (IMAT), bone, and miscellaneous intra-pelvic content. The dataset was randomly divided into training (180/200) and test (20/200) datasets. Data augmentation was utilized to enlarge the training dataset and all images underwent preprocessing with histogram equalization. Our model was trained for 50 epochs using the U-Net architecture with batch size of 8, learning rate of 0.0001, Adadelta optimizer and a dropout of 0.20. The Dice (F1) score was used to assess similarity between the manual segmentations and the CNN predicted segmentations. RESULTS: The CNN model with data augmentation of N = 3000 achieved accurate segmentation of body composition for all classes. The Dice scores were as follows: background (1.00), miscellaneous intra-pelvic content (0.98), SAT (0.97), muscle (0.95), IMAT (0.91), and bone (0.92). Mean time to automatically segment one CT image was 0.07 s (GPU) and 2.51 s (CPU). CONCLUSIONS: Our CNN-based model enables accurate automated segmentation of multiple tissues on pelvic CT images, with promising implications for body composition studies.