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.

Personalized Repetitive Transcranial Magnetic Stimulation for Depression.

Personalized treatments are gaining momentum across all fields of medicine. Precision medicine can be applied to neuromodulatory techniques, in which focused brain stimulation treatments such as repetitive transcranial magnetic stimulation (rTMS) modulate brain circuits and alleviate clinical symptoms. rTMS is well tolerated and clinically effective for treatment-resistant depression and other neuropsychiatric disorders. Despite its wide stimulation parameter space (location, angle, pattern, frequency, and intensity can be adjusted), rTMS is currently applied in a one-size-fits-all manner, potentially contributing to its suboptimal clinical response (∼50%). In this review, we examine components of rTMS that can be optimized to account for interindividual variability in neural function and anatomy. We discuss current treatment options for treatment-resistant depression, the neural mechanisms thought to underlie treatment, targeting strategies, stimulation parameter selection, and adaptive closed-loop treatment. We conclude that a better understanding of the wide and modifiable parameter space of rTMS will greatly improve the clinical outcome.

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.

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.