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.

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 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.

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.

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.

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.

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 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 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.