Predicting Fractures Using Vertebral 18F-NaF Uptake in Prostate Cancer Patients.

BACKGROUND: Patients with prostate cancer tend to be at heightened risk for fracture due to bone metastases and treatment with androgen-deprivation therapy. Bone mineral density (BMD) derived from dual energy X-ray absorptiometry (DXA) is the standard for determining fracture risk in this population. However, BMD often fails to predict many osteoporotic fractures. Patients with prostate cancer also undergo 18F-sodium fluoride (18F-NaF)-positron emission tomography/computed tomography (PET/CT) to monitor metastases. The purpose of this study was to assess whether bone deposition, assessed by 18F-NaF uptake in 18F-NaF PET/CT, could predict incident fractures better than DXA- or CT-derived BMD in patients with prostate cancer. METHODS: This study included 105 males with prostate cancer who had undergone full body 18F-NaF PET/CT. Standardized uptake value (SUVmean and SUVmax) and CT-derived Hounsfield units (HU), a correlate of BMD, were recorded for each vertebral body. The average SUVmean, SUVmax, and HU were calculated for cervical, thoracic, lumbar, and sacral areas. The t-test was used to assess significant differences between fracture and no-fracture groups. RESULTS: The SUVmean and SUVmax values for the thoracic area were lower in the fracture group than in the no-fracture group. There was no significant difference in cervical, thoracic, lumbar or sacral HU between the 2 groups. CONCLUSIONS: Our study reports that lower PET-derived non-metastatic bone deposition in the thoracic spine is correlated with incidence of fractures in patients with prostate cancer. CT-derived HU, a correlate of DXA-derived BMD, was not predictive of fracture risk. 18F-NaF PET/CT may provide important insight into bone quality and fracture risk.

Atherogenic Indices as a Predictor of Aortic Calcification in Prostate Cancer Patients Assessed Using 18F-Sodium Fluoride PET/CT.

A major pathophysiological cause of cardiovascular disease is vascular plaque calcification. Fluorine 18−Sodium Fluoride (18F-NaF) PET/CT can be used as a sensitive imaging modality for detection of vascular calcification. The aim of this study was to find a non-invasive, cost-efficient, and readily available metric for predicting vascular calcification severity. This retrospective study was performed on 36 participants who underwent 18F-NaF fused PET/CT scans. The mean standard uptake values (SUVs) were calculated from manually sectioned axial sections over the aortic arch and thoracic aorta. Correlation analyses were performed between SUVs and calculated atherogenic indices (AIs). Castelli's Risk Index I (r = 0.63, p < 0.0001), Castelli's Risk Index II (r = 0.64, p < 0.0001), Atherogenic Coefficient (r = 0.63, p < 0.0001), Atherogenic Index of Plasma (r = 0.51, p = 0.00152), and standalone high-density lipoprotein (HDL) cholesterol (r = −0.53, p = 0.000786) were associated with aortic calcification. AIs show strong association with aortic arch and thoracic aorta calcifications. AIs are better predictors of vascular calcification compared to standalone lipid metrics, with the exception of HDL cholesterol. Clinical application of AIs provides a holistic metric beneficial for enhancing screening and treatment protocols.

Vertebral Deformity Measurements at MRI, CT, and Radiography Using Deep Learning.

PURPOSE: To construct and evaluate the efficacy of a deep learning system to rapidly and automatically locate six vertebral landmarks, which are used to measure vertebral body heights, and to output spine angle measurements (lumbar lordosis angles [LLAs]) across multiple modalities. MATERIALS AND METHODS: In this retrospective study, MR (n = 1123), CT (n = 137), and radiographic (n = 484) images were used from a wide variety of patient populations, ages, disease stages, bone densities, and interventions (n = 1744 total patients, 64 years ± 8, 76.8% women; images acquired 2005-2020). Trained annotators assessed images and generated data necessary for deformity analysis and for model development. A neural network model was then trained to output vertebral body landmarks for vertebral height measurement. The network was trained and validated on 898 MR, 110 CT, and 387 radiographic images and was then evaluated or tested on the remaining images for measuring deformities and LLAs. The Pearson correlation coefficient was used in reporting LLA measurements. RESULTS: On the holdout testing dataset (225 MR, 27 CT, and 97 radiographic images), the network was able to measure vertebral heights (mean height percentage of error ± 1 standard deviation: MR images, 1.5% ± 0.3; CT scans, 1.9% ± 0.2; radiographs, 1.7% ± 0.4) and produce other measures such as the LLA (mean absolute error: MR images, 2.90°; CT scans, 2.26°; radiographs, 3.60°) in less than 1.7 seconds across MR, CT, and radiographic imaging studies. CONCLUSION: The developed network was able to rapidly measure morphometric quantities in vertebral bodies and output LLAs across multiple modalities.Keywords: Computer Aided Diagnosis (CAD), MRI, CT, Spine, Demineralization-Bone, Feature Detection Supplemental material is available for this article. © RSNA, 2021.

Exposure to e-cigarette aerosol over two months induces accumulation of neurotoxic metals and alteration of essential metals in mouse brain.

Despite a recent increase in e-cigarette use, the adverse human health effects of exposure to e-cigarette aerosol, especially on the central nervous system (CNS), remain unclear. Multiple neurotoxic metals have been identified in e-cigarette aerosol. However, it is unknown whether those metals accumulate in the CNS at biologically meaningful levels. To answer this question, two groups of mice were whole-body exposed twice a day, 5 days a week, for two months, to either a dose of e-cigarette aerosol equivalent to human secondhand exposure, or a 5-fold higher dose. After the last exposure, the olfactory bulb, anterior and posterior frontal cortex, striatum, ventral midbrain, cerebellum, brainstem, remaining brain tissue and spinal cord were collected for metal quantification by inductively coupled plasma mass spectrometry and compared to tissues from unexposed control mice. The two-month exposure caused significant accumulation of several neurotoxic metals in various brain areas - for some metals even at the low exposure dose. The most striking increases were measured in the striatum. For several metals, including Cr, Cu, Fe, Mn, and Pb, similar accumulations are known to be neurotoxic in mice. Decreases in some essential metals were observed across the CNS. Our findings suggest that chronic exposure to e-cigarette aerosol could lead to CNS neurotoxic metal deposition and endogenous metal dyshomeostasis, including potential neurotoxicity. We conclude that e-cigarette-mediated metal neurotoxicity may pose long-term neurotoxic and neurodegenerative risks for e-cigarette users and bystanders.

Association of baseline subject characteristics with changes in coronary calcification assessed by 18F-sodium fluoride PET/CT.

OBJECTIVE: The goal of this study was to test if changes in coronary microcalcification over a two year period assessed by fluorine-18-sodium fluoride (18F-NaF) positron emission tomography/computed tomography (PET/CT) are associated with baseline subject characteristics. SUBJECTS AND METHODS: This prospective study included healthy female (N=8, age 52±10 years, body mass index(BMI) 24±1.7kg/m2) and male (N=15, age 50±10 years, BMI 27±2.9kg/m2) participants who had 18F-NaF PET/CT scans taken two years apart. Imaging was performed 90 minutes after intravenous injection of 2.2MBq of 18F-NaF per kilogram of body weight. The analysis regions were selected on CT images by drawing volumes of interest around the entire heart using a semi-automatic segmentation method.Mean standardize uptake value (SUVmean) and maximum SUV (SUVmax) were calculated in the same regions of the registered PET images. Percent change in SUV between the two time points were correlated against baseline age, BMI, cardiovascular risk factors, and blood chemistry. RESULTS: In males, percent change in SUVmean over the two year period was positively correlated with baseline BMI (r=0.85, P<0.0001) and systolic blood pressure (r=0.65, P=0.0082). These baseline values were not significantly correlated with SUVmax in either gender. CONCLUSION: High BMI is a known risk factor for atherosclerosis. Our data showed that rate of increase in coronary microcalcification over time measured by 18F-NaF PET/CT is associated with baseline BMI and some clinical risk factors in males. Lack of such associations in females could be due to low sample size (N=8). Further prospective studies are needed to determine if baseline BMI and clinical factors could be used to predict rate of increase in coronary microcalcification which could provide the basis for managing the progression of atherosclerosis in patient-specific manner.

A deep learning system for automated, multi-modality 2D segmentation of vertebral bodies and intervertebral discs.

PURPOSE: Fractures in vertebral bodies are among the most common complications of osteoporosis and other bone diseases. However, studies that aim to predict future fractures and assess general spine health must manually delineate vertebral bodies and intervertebral discs in imaging studies for further radiomic analysis. This study aims to develop a deep learning system that can automatically and rapidly segment (delineate) vertebrae and discs in MR, CT, and X-ray imaging studies. RESULTS: We constructed a neural network to output 2D segmentations for MR, CT, and X-ray imaging studies. We trained the network on 4490 MR, 550 CT, and 1935 X-ray imaging studies (post-data augmentation) spanning a wide variety of patient populations, bone disease statuses, and ages from 2005 to 2020. Evaluated using 5-fold cross validation, the network was able to produce median Dice scores > 0.95 across all modalities for vertebral bodies and intervertebral discs (on the most central slice for MR/CT and on image for X-ray). Furthermore, radiomic features (skewness, kurtosis, mean of positive value pixels, and entropy) calculated from predicted segmentation masks were highly accurate (r ≥ 0.96 across all radiomic features when compared to ground truth). Mean time to produce outputs was <1.7 s across all modalities. CONCLUSIONS: Our network was able to rapidly produce segmentations for vertebral bodies and intervertebral discs for MR, CT, and X-ray imaging studies. Furthermore, radiomic quantities derived from these segmentations were highly accurate. Since this network produced outputs rapidly for these modalities which are commonly used, it can be put to immediate use for radiomic and clinical imaging studies assessing spine health.