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.

The Application of Advanced Bone Imaging Technologies in Sports Medicine.

Until recently, the evaluation of bone health and fracture risk through imaging has been limited to dual-energy X-ray absorptiometry (DXA) and plain radiographs, with a limited application in the athletic population. Several novel imaging technologies are now available for the clinical assessment of bone health, including bone injury risk and healing progression, with a potential for use in sports medicine. Among these imaging modalities is high-resolution peripheral quantitative computed tomography (HR-pQCT) which is a promising technology that has been developed to examine the bone microarchitecture in both cortical and trabecular bone at peripheral anatomical sites. Technologies that do not expose patients to ionizing radiation are optimal, particularly for athletes who may require frequent imaging. One such alternative is diagnostic ultrasound, which is preferable due to its low cost and lack of radiation exposure. Furthermore, ultrasound, which has not been a common imaging modality for monitoring fracture healing, has been shown to potentially demonstrate earlier signs of union compared to conventional radiographs, including callus mineralization and density at the healing site. Through the use of conventional magnetic resonance imaging (MRI), finite element analysis (FEA) can be used to simulate the structural and mechanical properties of bone. On the other hand, the ultrashort echo time (UTE) MRI can evaluate cortical bone quality by detecting water bound to the organic bone matrix and free water, providing important information about bone porosity. Several novel bone imaging techniques originally developed for osteoporosis assessment have great potential to be utilized to improve the standard of care in bone fracture risk assessment and healing in sports medicine with much greater precision and less adverse radiation exposure.

Conquering the Cobb Angle: A Deep Learning Algorithm for Automated, Hardware-Invariant Measurement of Cobb Angle on Radiographs in Patients with Scoliosis.

Scoliosis is a disease estimated to affect more than 8% of adults in the United States. It is diagnosed with use of radiography by means of manual measurement of the angle between maximally tilted vertebrae on a radiograph (ie, the Cobb angle). However, these measurements are time-consuming, limiting their use in scoliosis surgical planning and postoperative monitoring. In this retrospective study, a pipeline (using the SpineTK architecture) was developed that was trained, validated, and tested on 1310 anterior-posterior images obtained with a low-dose stereoradiographic scanning system and radiographs obtained in patients with suspected scoliosis to automatically measure Cobb angles. The images were obtained at six centers (2005-2020). The algorithm measured Cobb angles on hold-out internal (n = 460) and external (n = 161) test sets with less than 2° error (intraclass correlation coefficient, 0.96) compared with ground truth measurements by two experienced radiologists. Measurements, produced in less than 0.5 second, did not differ significantly (P = .05 cutoff) from ground truth measurements, regardless of the presence or absence of surgical hardware (P = .80), age (P = .58), sex (P = .83), body mass index (P = .63), scoliosis severity (P = .44), or image type (low-dose stereoradiographic image vs radiograph; P = .51) in the patient. These findings suggest that the algorithm is highly robust across different clinical characteristics. Given its automated, rapid, and accurate measurements, this network may be used for monitoring scoliosis progression in patients. Keywords: Cobb Angle, Convolutional Neural Network, Deep Learning Algorithms, Pediatrics, Machine Learning Algorithms, Scoliosis, Spine Supplemental material is available for this article. © RSNA, 2023.

Head and neck tumor segmentation convolutional neural network robust to missing PET/CT modalities using channel dropout.

Objective. Radiation therapy for head and neck (H&N) cancer relies on accurate segmentation of the primary tumor. A robust, accurate, and automated gross tumor volume segmentation method is warranted for H&N cancer therapeutic management. The purpose of this study is to develop a novel deep learning segmentation model for H&N cancer based on independent and combined CT and FDG-PET modalities.Approach. In this study, we developed a robust deep learning-based model leveraging information from both CT and PET. We implemented a 3D U-Net architecture with 5 levels of encoding and decoding, computing model loss through deep supervision. We used a channel dropout technique to emulate different combinations of input modalities. This technique prevents potential performance issues when only one modality is available, increasing model robustness. We implemented ensemble modeling by combining two types of convolutions with differing receptive fields, conventional and dilated, to improve capture of both fine details and global information.Main Results. Our proposed methods yielded promising results, with a Dice similarity coefficient (DSC) of 0.802 when deployed on combined CT and PET, DSC of 0.610 when deployed on CT, and DSC of 0.750 when deployed on PET.Significance. Application of a channel dropout method allowed for a single model to achieve high performance when deployed on either single modality images (CT or PET) or combined modality images (CT and PET). The presented segmentation techniques are clinically relevant to applications where images from a certain modality might not always be available.

Radiomic-Based MRI for Classification of Solitary Brain Metastases Subtypes From Primary Lymphoma of the Central Nervous System.

BACKGROUND: Differential diagnosis of brain metastases subtype and primary central nervous system lymphoma (PCNSL) is necessary for treatment decisions. The application of machine learning facilitates the classification of brain tumors, but prior investigations into primary lymphoma and brain metastases subtype classification have been limited. PURPOSE: To develop a machine-learning model to classify PCNSL, brain metastases with primary lung and non-lung origin. STUDY TYPE: Retrospective. POPULATION: A total of 211 subjects with pathologically confirmed PCNSL or brain metastases (training cohort 168 and testing cohort 43). FIELD STRENGTH/SEQUENCE: A 3.0 T axial contrast-enhanced T1-weighted spin-echo inversion recovery sequence (T1WI-CE), axial T2-weighted fluid-attenuation inversion recovery sequence (T2FLAIR) ASSESSMENT: Several machine-learning models (support vector machine, random forest, and K-nearest neighbors) were built with least absolute shrinkage and selection operator (LASSO) using features from T1WI-CE, T2FLAIR, and clinical. The model with the highest performance in the training cohort was selected to differentiate lesions in the testing cohort. Then, three radiologists conducted a two-round classification (with and without model reference) using images and clinical information from testing cohorts. STATISTICAL TESTS: Five-fold cross-validation was used for model evaluation and calibration. Model performance was assessed based on sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC). RESULTS: Twenty-five image features were selected by LASSO analysis. Random forest classifier was selected for its highest performance on the training set with an AUC of 0.73. After calibration, this model achieved an accuracy of 0.70 on the testing set. Accuracies of all three radiologists improved under model reference (0.49 vs. 0.70, 0.60 vs. 0.77, 0.58 vs. 0.72, respectively). DATA CONCLUSION: The random forest model based on conventional MRI and clinical data can diagnose PCNSL and brain metastases subtypes (lung and non-lung origin). Model classification can help foster the diagnostic accuracy of specialists and streamline prognostication workflow. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Distinct characteristics of limbic-predominant age-related TDP-43 encephalopathy in Lewy body disease.

Limbic-predominant age-related TDP-43 encephalopathy (LATE) is characterized by the accumulation of TAR-DNA-binding protein 43 (TDP-43) aggregates in older adults. LATE coexists with Lewy body disease (LBD) as well as other neuropathological changes including Alzheimer's disease (AD). We aimed to identify the pathological, clinical, and genetic characteristics of LATE in LBD (LATE-LBD) by comparing it with LATE in AD (LATE-AD), LATE with mixed pathology of LBD and AD (LATE-LBD + AD), and LATE alone (Pure LATE). We analyzed four cohorts of autopsy-confirmed LBD (n = 313), AD (n = 282), LBD + AD (n = 355), and aging (n = 111). We assessed the association of LATE with patient profiles including LBD subtype and AD neuropathologic change (ADNC). We studied the morphological and distributional differences between LATE-LBD and LATE-AD. By frequency analysis, we staged LATE-LBD and examined the association with cognitive impairment and genetic risk factors. Demographic analysis showed LATE associated with age in all four cohorts and the frequency of LATE was the highest in LBD + AD followed by AD, LBD, and Aging. LBD subtype and ADNC associated with LATE in LBD or AD but not in LBD + AD. Pathological analysis revealed that the hippocampal distribution of LATE was different between LATE-LBD and LATE-AD: neuronal cytoplasmic inclusions were more frequent in cornu ammonis 3 (CA3) in LATE-LBD compared to LATE-AD and abundant fine neurites composed of C-terminal truncated TDP-43 were found mainly in CA2 to subiculum in LATE-LBD, which were not as numerous in LATE-AD. Some of these fine neurites colocalized with phosphorylated α-synuclein. LATE-LBD staging showed LATE neuropathological changes spread in the dentate gyrus and brainstem earlier than in LATE-AD. The presence and prevalence of LATE in LBD associated with cognitive impairment independent of either LBD subtype or ADNC; LATE-LBD stage also associated with the genetic risk variants of TMEM106B rs1990622 and GRN rs5848. These data highlight clinicopathological and genetic features of LATE-LBD.

MRI-based assessment of proximal femur strength compared to mechanical testing.

Half of the women who sustain a hip fracture would not qualify for osteoporosis treatment based on current DXA-estimated bone mineral density criteria. Therefore, a better approach is needed to determine if an individual is at risk of hip fracture from a fall. The objective of this study was to determine the association between radiation-free MRI-derived bone strength and strain simulations compared to results from direct mechanical testing of cadaveric femora. Imaging was conducted on a 3-Tesla MRI scanner using two sequences: one balanced steady-state free precession sequence with 300 µm isotropic voxel size and one spoiled gradient echo with anisotropic voxel size of 234 × 234 × 1500 µm. Femora were dissected free of soft-tissue and 4350-ohm strain-gauges were securely applied to surfaces at the femoral shaft, inferior neck, greater trochanter, and superior neck. Cadavers were mechanically tested with a hydraulic universal test frame to simulate loading in a sideways fall orientation. Sideways fall forces were simulated on MRI-based finite element meshes and bone stiffness, failure force, and force for plastic deformation were computed. Simulated bone strength metrics from the 300 µm isotropic sequence showed strong agreement with experimentally obtained values of bone strength, with stiffness (r = 0.88, p = 0.0002), plastic deformation point (r = 0.89, p < 0.0001), and failure force (r = 0.92, p < 0.0001). The anisotropic sequence showed similar trends for stiffness, plastic deformation point, and failure force (r = 0.68, 0.70, 0.84; p = 0.02, 0.01, 0.0006, respectively). Surface strain-gauge measurements showed moderate to strong agreement with simulated magnitude strain values at the greater trochanter, superior neck, and inferior neck (r = -0.97, -0.86, 0.80; p ≤0.0001, 0.003, 0.03, respectively). The findings from this study support the use of MRI-based FE analysis of the hip to reliably predict the mechanical competence of the human femur in clinical settings.

The Effect of Inflammation on Bone.

Bone remodeling is the continual process to renew the adult skeleton through the sequential action of osteoblasts and osteoclasts. Nuclear factor RANK, an osteoclast receptor, and its ligand RANKL, expressed on the surface of osteoblasts, result in coordinated control of bone remodeling. Inflammation, a feature of illness and injury, plays a distinct role in skewing this process toward resorption. It does so via the interaction of inflammatory mediators and their related peptides with osteoblasts and osteoclasts, as well as other immune cells, to alter the expression of RANK and RANKL. Such chemical mediators include TNFα, glucocorticoids, histamine, bradykinin, PGE2, systemic RANKL from immune cells, and interleukins 1 and 6. Conditions, such as periodontal disease and alveolar bone erosion, aseptic prosthetic loosening, rheumatoid arthritis, and some sports related injuries are characterized by the result of this process. A thorough understanding of bone response to injury and disease, and ability to detect such biomarkers, as well as imaging to identify early structural and mechanical property changes in bone architecture, is important in improving management and outcomes of bone related pathology. While gut health and vitamin and mineral availability appear vitally important, nutraceuticals also have an impact on bone health. To date most pharmaceutical intervention targets inflammatory cytokines, although strategies to favorably alter inflammation induced bone pathology are currently limited. Further research is required in this field to advance early detection and treatments.

Diagnosis and Monitoring of Osteoporosis With 18F-Sodium Fluoride PET: An Unavoidable Path for the Foreseeable Future.

The prevalence of metabolic bone diseases particularly osteoporosis and its precursor, osteopenia, continue to grow as serious global health issues today. On a worldwide perspective, 200million people suffer from osteoporosis and in 2005, over 2million fracture incidents were estimated due to osteoporosis in the United States. Currently, osteoporosis and other metabolic bone diseases are evaluated primarily through dual energy X-ray absorptiometry, and rarely by bone biopsy with tetracycline labeling or Technetium-99m (99mTc) based bone scintigraphy. Deficiencies in these methods have prompted the use of more precise methods of assessment. This review highlights the use of 18F-sodium fluoride (NaF) with PET (NaF-PET), NaF-PET/CT, or NaF-PET/MRI in the evaluation of osteoporosis and osteopenia in the lumbar spine and hip. This imaging modality provides a molecular perspective with respect to the underlying metabolic alterations that lead to osseous disorders by measuring bone turnover through standardized uptake values. Its sensitivity and ability to examine the entire skeletal system make it a more superior imaging modality compared to standard structural imaging techniques. Further research is needed to determine its accuracy in reflecting the efficacy of therapeutic interventions in metabolic bone diseases.