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.

Deep learning segmentation of mandible with lower dentition from cone beam CT.

OBJECTIVES: This study aimed to train a 3D U-Net convolutional neural network (CNN) for mandible and lower dentition segmentation from cone-beam computed tomography (CBCT) scans. METHODS: In an ambispective cross-sectional design, CBCT scans from two hospitals (2009-2019 and 2021-2022) constituted an internal dataset and external validation set, respectively. Manual segmentation informed CNN training, and evaluations employed Dice similarity coefficient (DSC) for volumetric accuracy. A blinded oral maxillofacial surgeon performed qualitative grading of CBCT scans and object meshes. Statistical analyses included independent t-tests and ANOVA tests to compare DSC across patient subgroups of gender, race, body mass index (BMI), test dataset used, age, and degree of metal artifact. Tests were powered for a minimum detectable difference in DSC of 0.025, with alpha of 0.05 and power level of 0.8. RESULTS: 648 CBCT scans from 490 patients were included in the study. The CNN achieved high accuracy (average DSC: 0.945 internal, 0.940 external). No DSC differences were observed between test set used, gender, BMI, and race. Significant differences in DSC were identified based on age group and the degree of metal artifact. The majority (80%) of object meshes produced by both manual and automatic segmentation were rated as acceptable or higher quality. CONCLUSION: We developed a model for automatic mandible and lower dentition segmentation from CBCT scans in a demographically diverse cohort including a high degree of metal artifacts. The model demonstrated good accuracy on internal and external test sets, with majority acceptable quality from a clinical grader.

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.

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.

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.