Search Results (115)

Objectives: To evaluate the diagnostic performance of automated vertebral trabecular Hounsfield unit (HU) measurements derived from routine fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) for identifying low bone density. Methods: This retrospective study included 131 consecutive women (mean age, 53.5 ± 9.6 years) undergoing health screening with FDG PET/CT and dual-energy X-ray absorptiometry (DXA) between January 2020 and December 2024. A deep learning-based model (TotalSegmentator) automatically segmented the lumbar vertebrae (L1–L4). HU-based metrics in trabecular regions were calculated, and their correlations with DXA-derived bone mineral density (BMD) were assessed. Diagnostic performance was evaluated using receiver operating characteristic analysis. A multivariable logistic regression model incorporating mean HU, age, and body mass index was developed and internally validated using bootstrap resampling. Results: According to WHO criteria, 47 of 131 participants (35.9%) had low bone density. Mean HU demonstrated strong diagnostic performance (area under the curve [95% confidence interval]: L1, 0.861 [0.800–0.923]; L2, 0.852 [0.788–0.915]; L3, 0.861 [0.800–0.921]; L4, 0.845 [0.781–0.909]). L1 mean HU provided the most balanced performance (sensitivity, 0.851; specificity, 0.750); L3 mean HU was slightly inferior. L1 mean HU was strongly correlated with BMD (r = 0.821, p < 0.001). In multivariable analysis, mean HU independently predicted low bone density (odds ratio: 0.949, p < 0.001). The model achieved an accuracy of 0.786 and demonstrated favorable calibration performance. Conclusions: The automated assessment of vertebral trabecular HU from routine FDG PET/CT provides a reliable and highly efficient method for screening low bone density without additional radiation exposure or cost. Full article

Objective: To quantify and map bone mineral density (BMD) at the bases of human finger phalanges using computed tomography (CT) with a calibration phantom and to compare BMD both between and within digits. Methods: Ten cadaveric hands (H1 to H10) were CT scanned with a Model 3 CT Calibration Phantom (Mindways). All data were processed in the Horos software (Version 4.0.0) and the regions of interest (ROIs) at each phalangeal base were delineated. Hounsfield Units (HU) were converted to BMD (mg/cm³) per the phantom framework. Descriptive statistics and repeated-measures ANOVA analyses were performed for each digit and corresponding phalangeal level (proximal, middle, distal). Inter-digital comparisons were performed at corresponding phalanx levels and intra-digital variations were analyzed within digits across phalangeal levels. Results: Mean BMD varied across digits and phalangeal levels. At the proximal phalanx base, the thumb and index fingers exhibited the highest values, whereas at the middle phalanx base the middle and ring fingers demonstrated the highest mean BMD values. At the distal phalanx base, the little finger demonstrated the highest BMD value, while the lowest value was observed at the distal phalanx of the index finger. Intra-digital analysis revealed distinct distribution patterns: BMD decreased distally in the thumb and index fingers, peaked at the middle phalanx in the middle and ring fingers, and was highest distally in the little finger. Repeated-measures ANOVA demonstrated statistically significant intra-digital differences in the thumb and index fingers, whereas no statistically significant inter-digital differences were observed across corresponding phalangeal levels. Conclusions: CT-based, phantom-calibrated BMD mapping at the bases of the phalanges demonstrates substantial intra-digital variability and descriptive inter-digital differences. These site-specific findings may provide additional information relevant to implant selection and preoperative planning for fixation in phalangeal fractures and tendon- or ligament-to-bone insertion injuries in hand surgery. Full article

Pathology of the cervical spine is an important cause of poor performance in sport horses. Computed tomography (CT) and magnetic resonance imaging (MRI) have improved musculoskeletal diagnostic capabilities. CT is well-suited to assess osseous pathology, although it is limited for assessing soft tissues. MRI, the gold standard for imaging soft tissues, cannot be used for antemortem imaging of the equine cervical spine. The diagnostic potential of CT for evaluating soft tissues of the equine cervical spine compared to MRI is not well-defined and warrants investigation. Postmortem MRI was performed on eighteen horses euthanized for clinical and CT myelographic findings of cervical disease. Synovial and vertebral nerve tissue abnormalities were graded on MRI and CT. Grading of radiologists’ confidence in identification of synovial tissues and vertebral nerves on MRI and CT was performed. Hounsfield unit (HU) measurements of synovial and vertebral nerve tissues were recorded. MRI was superior at evaluating, and radiologists had greater confidence in MRI for identifying synovial and vertebral nerve tissues. Agreement between measurements of HU was variable and poorest for vertebral nerves. Findings support MRI’s superiority for the evaluation of equine cervical vertebral nerves and synovial tissues. Caution should be exercised when interpreting CT findings of these tissues of horses. Full article

Objectives: Cone-beam computed tomography (CBCT) is central to three-dimensional assessment in oral surgery and implant dentistry; however, CBCT-derived gray values expressed as HU-like units are not equivalent to true CT-derived Hounsfield Units (HU). This brief methodological secondary analysis evaluated the reliability and practical limitations of such values in assessing radiographic changes after peri-implantitis surgery. Methods: The analysis used the imaging protocol and group-level radiological data from a previously published prospective clinical cohort, conducted under the same protocol and ethical approval of the Institutional Ethics Committee of the Medical University of Lublin (KE-0254/248/11/2023; 23 November 2023). The source cohort included 57 patients treated after implant removal for severe peri-implantitis with small-particle dentin (n = 22), Bio-Oss (n = 15), or spontaneous healing without grafting (n = 20). CBCT scans were analyzed in OnDemand3D (version 1.0.11.1007) using manually selected square regions of interest (ROI; 30 × 30 pixels). No external phantom calibration, cross-device normalization, or formal intra-/inter-observer reproducibility assessment was available in the secondary dataset. Results: The previously reported mean study-site values were 779.62 ± 325.92 gray-value units for small-particle dentin, 910.51 ± 155.03 gray-value units for Bio-Oss, and 206.04 ± 174.21 gray-value units for controls. These findings are presented as protocol-dependent attenuation patterns, not as direct material rankings, bone-density thresholds, or proof of regeneration. Variability remained substantial, with study-site coefficients of variation of 41.8%, 17.0%, and 84.6%, respectively, and high adjacent-site variability. Interpretation was constrained by manual ROI placement, lack of calibration, absence of observer-agreement metrics, unequal follow-up timing, and CBCT sensitivity to scatter, beam hardening, field of view, reconstruction settings, and metal-related artifacts. Conclusions: CBCT-derived gray values may be useful as relative indicators of local radiographic attenuation change within a standardized protocol, but they should not be interpreted as absolute measures of bone density. Future regenerative oral surgery studies should combine standardized acquisition, explicit ROI methodology, repeated measurements, observer-agreement analysis, and complementary clinical, radiographic, or histological outcomes. Full article

Background: Preoperative assessment of Head and Neck Squamous Cell Carcinoma (HNSCC) aggressiveness is often hindered by the sampling errors of incisional biopsies. While Contrast-Enhanced Computed Tomography (CECT) is the standard for staging, its potential to serve as a non-invasive complementary radiological tool of the entire tumor volume remains underutilized. Objective: To evaluate the predictive performance of preoperative CECT-derived tumor volume, densitometric values, and morphological features as predictors of histopathological grade and lymph node metastasis (pN) in HNSCC. The primary outcome was predicting lymph node metastasis (pN+), and the secondary outcome was predicting histopathological grade. Methods: This retrospective observational study analyzed 42 patients with SCC of the oral cavity, larynx, or maxilla. Quantitative (3D volume, Hounsfield Units [HU], HU Delta) and qualitative (margins, lobulations, necrosis) CT parameters were correlated with definitive histopathology. Diagnostic performance was assessed using Receiver Operating Characteristic (ROC) curve analysis and Spearman’s rank correlation. Results: High-grade tumors (G2/G3) demonstrated significantly larger median volumes (18.1 vs. 2.9 cm³, p = 0.006), lower contrast density (55 vs. 68 HU, p = 0.010), and reduced vascular wash-in (23 vs. 30 HU Delta, p = 0.008) compared to G1 lesions. ROC analysis identified a volume threshold of ≥9.43 cm³ for high-grade disease (AUC = 0.865; sensitivity 67.6%, specificity 100%). For regional metastasis (pN+), tumor volume was the only significant predictor (25.4 vs. 6.2 cm³, p = 0.036), with an optimal cut-off of ≥6.76 cm³ (AUC = 0.769; sensitivity 100%). Strong negative correlations were observed between contrast enhancement and internal necrosis (r = −0.812, p < 0.001). Conclusions: Preoperative CECT parameters show promise as non-invasive imaging surrogates of HNSCC aggressiveness. A paradoxical reduction in contrast enhancement characterizes high-grade biology, reflecting disorganized neo-angiogenesis and internal hypoxia. Integrating 3D volumetric analysis and morphological markers shows potential as a complementary exploratory tool that, pending future prospective validation, may support risk stratification and surgical planning alongside traditional histopathological assessment. Full article

Cone-beam computed tomography (CBCT) is widely used in image-guided radiotherapy because of its low radiation dose and on-board acquisition capability. However, CBCT images often suffer from scatter artifacts, increased noise, reduced soft-tissue contrast, and inaccurate Hounsfield Unit (HU) values, which limit their direct use for accurate dose calculation and quantitative analysis. To address this limitation, we propose a CBCT-to-CT synthesis framework based on 2.5D context encoding (concatenating five adjacent slices along the channel dimension) and latent-space variational diffusion. The proposed method combines a Vector Quantized Variational Autoencoder (VQ-VAE) and a U-shaped Vision Transformer (U-ViT)-based latent-space Variational Diffusion Model (VDM) to translate CBCT images into synthetic CT (sCT) images in a compressed latent space. To incorporate inter-slice anatomical context while preserving the computational efficiency of 2D processing, five adjacent CBCT slices are concatenated along the channel dimension and used as input. We evaluated the proposed method on the SynthRAD2025 paired CBCT-CT dataset covering head-and-neck, thoracic, and abdominal regions. Under the provided benchmark setting, quantitative evaluation on the validation set showed that the proposed 2.5D model improved peak signal-to-noise ratio (PSNR) from 25.39 dB to 27.44 dB (averaged across regions), structural similarity index measure (SSIM) from 0.813 to 0.846, reduced mean squared error (MSE) from 0.00313 to 0.00200, and lowered Fréchet inception distance (FID) from 1009.33 to 869.53 compared with the 2D baseline. Qualitative results also showed improved anatomical consistency and reduced artifact-related distortions. These findings suggest that neighboring-slice context can enhance HU fidelity and overall image quality in a computationally practical synthesis framework, supporting the usefulness of efficient AI-based cross-modality reconstruction for radiotherapy-related imaging workflows. Full article

A quantification technique for costal cartilage calcification using ¹⁸F-sodium fluoride–positron emission tomography/computed tomography (¹⁸F-NaF-PET/CT) has yet to be established, and the effects of aging and other demographic variables on costal cartilage calcification remain understudied. This study aims to introduce a quantification methodology for assessing costal cartilage calcification using ¹⁸F-NaF-PET/CT, assess age-related changes in its ¹⁸F-NaF uptake in females and males, and examine the relationship between its ¹⁸F-NaF uptake and CT attenuation as well as ¹⁸F-NaF uptake and coronary artery calcification. In this retrospective study, we analyzed subjects from the Cardiovascular Molecular Calcification Assessed by ¹⁸F-NaF PET/CT (CAMONA) clinical trial. This study evaluated 130 subjects (mean age 48.7 ± 14.5 years; n = 67 females). We manually generated regions of interest overlying the costal cartilages from ribs 8 to 10 on the left side, carefully avoiding osseous uptake from adjacent ribs and sternum, to measure cartilaginous ¹⁸F-NaF uptake. Non-parametric statistical analyses (Spearman correlations, Mann–Whitney U tests, Kruskal–Wallis tests) and receiver operating characteristic analysis were performed to evaluate sex-specific age-related changes in uptake, correlations between imaging parameters, and associations with coronary artery calcium (CAC) score. In females, the mean ¹⁸F-NaF uptake (as assessed by average SUV_mean) was 0.69 ± 0.38 while the corresponding mean Hounsfield Unit (HU) was 108.0 ± 40.0. In males, the mean ¹⁸F-NaF uptake (as assessed by average SUV_mean) was 0.63 ± 0.22, and the mean HU was 104.0 ± 24.0. There was a significant correlation between ¹⁸F-NaF uptake and age in both females (p = 0.003, r = 0.36) and males (p < 0.0001, r = 0.63). The correlation was significantly stronger in males than females (Fisher’s z-test, p = 0.040). There was a significant correlation between CAC score and costal cartilage SUV_mean in both females (r = 0.26, p = 0.036) and males (r = 0.51, p < 0.0001). This study introduces a quantification technique to assess costal cartilage calcification using ¹⁸F-NaF-PET/CT and demonstrates that the calcification increases with age, more strongly in males than in females, and ¹⁸F-NaF uptake is correlated with CAC score. This technique can be applied to other cartilages of interest, in both physiological and pathological conditions, to assess the effects of aging and various demographic variables on cartilage calcification. Full article

Background: Cone Beam Computed Tomography (CBCT) is widely used in image-guided radiotherapy because it provides on-board volumetric imaging at relatively low doses, but its clinical utility for synthetic CT (sCT) generation remains limited by noise, scatter, artifacts, and reduced Hounsfield Unit (HU) fidelity. Conditional diffusion models (CDMs) have recently emerged as a promising alternative to earlier deep learning approaches because their iterative denoising process may better preserve anatomical structure and model uncertainty. Objective: This systematic review evaluates the use of conditional diffusion models for CBCT-to-CT synthesis, with particular attention to architectural strategies, reported quantitative outcomes, and potential clinical relevance. A systematic search was conducted in PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar for studies published between 2013 and 2024. Eleven studies met the eligibility criteria and were analyzed to address three questions: (1) Which conditional diffusion strategies have been used? (2) What outcomes have been reported? and (3) What clinical implications have been discussed? Results: Across the included studies, CDMs frequently showed promising image quality performance, especially when incorporating anatomical priors, spatial-frequency guidance, hierarchical refinement, or latent representations. However, the evidence base remains small and highly heterogeneous with respect to anatomy, dimensionality, supervision strategy, and evaluation metrics, limiting the strength of direct comparative claims. The reviewed literature suggests that conditional diffusion models are a promising direction for CBCT-to-CT synthesis, but stronger dose-aware validation, standardized reporting, and broader multicenter evaluation are still needed before routine clinical deployment. This review has been registered with the International Prospective Register of Systematic Reviews (PROSPERO), under registration number CRD42024619240. Full article

Summary This study assessed the use of opportunistic abdominopelvic computed tomography (CT) for the evaluation of the sacrum as a predictive tool for osteoporosis. Sacral spine Hounsfield unit (HU) values measured by CT showed good correlation with mean bone mineral density (BMD) for L1–L2 measured by quantitative computed tomography (QCT), and good diagnostic performance for the identification of osteoporosis. The results of this study suggest that it is possible to obtain comprehensive information on bone health in individuals who undergo CT of pelvic. Objectives To examine the distribution pattern of bone density in the L1–S3 vertebrae using opportunistic abdominopelvic imaging. QCT was employed as a reference to establish HU thresholds for the sacral vertebrae facilitating the prediction of osteoporosis and the exclusion of bone abnormalities. Methods A total of 277 subjects aged 19 to 81 years who underwent abdominopelvic CT were evaluated. Bone mineral density (BMD) measurements for the L1–S3 vertebrae and HU values for the S1–S3 vertebrae were collected. The study analyzed the correlation between sacral spine HU values and sacral spine BMD, along with the clinically utilized mean BMD for L1–L2, was analyzed. Receiver operating characteristic (ROC) curves were generated to identify the optimal diagnostic thresholds. Results The BMD of the lumbosacral vertebrae displayed a gradual decrease from L1 to L3, followed by an increase from L4 to S1, and a subsequent decline from S1 to S3. HU values of the sacral vertebrae across all planes were strongly correlated with both sacral spine BMD and the mean BMD values for L1–L2 (r = 0.830 to 0.905, p < 0.05). For individual vertebrae, the area under the curve (AUC) of HU values for predicting osteoporosis ranged from 0.909 to 0.977, while the AUC for excluding bone abnormalities ranged from 0.933 to 0.950, with S1 demonstrating the highest predictive efficacy. The optimal threshold for S1 was >165.17 HU, yielding a specificity of 91.5% and a sensitivity of 83.0% for excluding bone abnormalities. Conversely, an S1 threshold of <130.50 HU resulted in a diagnostic specificity of 90.0% and a sensitivity of 96.6% for osteoporosis. Additionally, a predictive model that incorporated sex, age, and vertebral cancellous bone HU values achieved an AUC of 0.981. Conclusions Our data demonstrate a strong correlation between the HU values of the sacral spine and the clinically used BMD values for L1–L2, supporting the prediction of osteoporosis based on sacral spine HU values. Moreover, a predictive model that includes sex, age, and vertebral measurements offers improved diagnostic accuracy. Full article

Background/Objectives: Sarcopenia is a progressive skeletal muscle disorder linked to adverse outcomes. Computed Tomography (CT) can quantify skeletal muscle, while the Charlson Comorbidity Index (CCI) predicts mortality by categorising comorbidities. This study examined whether Computed Tomography of the Kidneys, Ureters, and Bladder (CT-KUB)-derived skeletal muscle measurements correlate with CCI scores in hospitalised patients. Methods: This retrospective study included all patients admitted with renal colic to the Urology Department, Blacktown Hospital and underwent cystoscopy between June 2022 and June 2025. Data were obtained from electronic medical records. CCI scores, incorporating age and comorbidities, generated 10-year survival estimates. CT-KUB scans were reviewed for psoas muscle perimeter, area, height, width and Hounsfield unit at the aortic bifurcation. Skeletal Muscle Index (SMI) was calculated as skeletal muscle area (SMA)/height². Associations between CCI, psoas muscle metrics and outcomes (length of stay, Intensive Care Unit (ICU) admission, Emergency Department (ED) re-presentation) were assessed using Pearson’s correlations and between-group comparisons. Results: A total of 397 patients were analysed. Median Length of Stay (LOS) was 1 day (mean = 1.92, SD = 1.88). ICU admission occurred in 2.3% of patients, and 18.6% re-presented to ED within 30 days. Both CCI survival percentage and psoas muscle metrics (including SMI) were significantly associated with LOS. Lower SMA, Hounsfield unit (HU), length and perimeter were linked to higher ICU admission risk. Neither CCI nor muscle measures predicted ED re-presentation. Conclusions: CCI and CT-derived muscle metrics were independently associated with outcomes such as LOS and ICU admission. Combining these measures may improve risk stratification, warranting further prospective evaluation. Full article

Background/Objectives: We aim to evaluate whether digital imaging and communications in medicine (DICOM)-level deep learning-based iodine-boosting applied to dual-source dual-energy computed tomography (DECT) source DICOM improves image quality in low-iodine-dose abdominal DECT in adults undergoing post-procedure follow-up computed tomography (CT). Methods: This retrospective study included 43 adults (April–September 2025) who underwent dynamic dual-source DECT using a low-iodine protocol. Three CT reconstructions were compared: mixed images, conventional 50-keV virtual monoenergetic images (VMIs), and 50-keV VMIs generated after applying DICOM-based deep learning iodine-boosting/denoising to the tube-specific dual-energy source DICOM series prior to VMI/iodine-map reconstruction (deep learning-based reconstruction [DLR]-VMI). Iodine material density (IMD) images were compared between the conventional and DLR-processed datasets. Quantitative attenuation and signal-to-noise ratio (SNR) were assessed using paired and repeated-measures tests. Image quality was scored by two readers using a five-point Likert scale. Results: Attenuation varied across CT reconstructions for all regions of interest in both phases (all overall p < 0.001). Liver attenuation increased from 94.9 ± 22.0 Hounsfield units (HU) (VMI) to 114.5 ± 34.6 HU (DLR-VMI) during the arterial phase and from 127.6 ± 25.6 HU to 166.6 ± 39.9 HU during the portal venous phase (both p < 0.001). Liver SNR improved with DLR-VMI compared to VMI (arterial: 9.11 ± 3.62 vs. 6.06 ± 1.90; portal: 12.74 ± 3.56 vs. 7.90 ± 1.82; both p < 0.001). On IMD images, DLR increased HU-equivalent values and liver SNR (arterial: 5.20 ± 2.89 vs. 2.61 ± 1.39; portal: 9.22 ± 2.81 vs. 4.48 ± 1.28; both p < 0.001). Qualitatively, DLR-VMI yielded the highest overall image-quality scores for both reviewers in both phases (Reviewer 1, arterial/portal: 4 (4–5)/5 (4–5); Reviewer 2, arterial/portal: 4 (3–4)/4 (4–4)). DLR also improved the overall image quality of IMD images for both reviewers (all p < 0.001). Conclusions: Raw DICOM-level iodine-boosting DLR applied to dual-source DECT-source DICOM enabled enhanced image quality and improved quantitative and qualitative metrics in low-iodine-dose abdominal DECT. Full article

Background and Objectives: Carotid artery stenosis has traditionally guided therapeutic decision-making; however, plaque morphology and composition are increasingly recognized as more reliable indicators of cerebrovascular risk than luminal narrowing alone. As imaging strategies shift toward vulnerability-based assessment, reproducibility of plaque characterization becomes essential for consistent clinical decision-making. This study aimed to evaluate interobserver agreement in carotid plaque assessment using multidetector computed tomography angiography (MDCTA) and to assess intermodality agreement with duplex ultrasonography (DUS). Materials and Methods: In this single-center study (January–September 2022), 50 patients with ≥60% internal carotid artery stenosis diagnosed by DUS (NASCET criteria), the majority of whom were asymptomatic (90%), were included. MDCTA examinations were independently analyzed by two radiologists, while DUS examinations were evaluated by a third observer. Plaque composition (lipid, fibrous, calcified), surface characteristics (regular, irregular, ulcerated), degree of stenosis, and plaque length were assessed. CT plaque characterization was based on Hounsfield unit (HU) thresholds (<50 HU lipid; 50–120 HU fibrous; >120 HU calcified). Interobserver agreement and intermodality agreement were calculated using Cohen’s kappa coefficient. Results: Good interobserver agreement was observed between the two MDCTA readers (κ = 0.751). Intermodality agreement between MDCTA and DUS was moderate (κ = 0.624 and κ = 0.595). Although significant differences were identified in 3 of 16 HU measurement points, no significant differences were found in overall plaque composition classification between MDCTA observers. DUS yielded significantly higher stenosis values (p = 0.007 and p = 0.005) and greater plaque length measurements (p < 0.0005) compared with MDCTA. Significant differences were also observed in plaque surface assessment between modalities (p = 0.044 and p = 0.033). Conclusions: MDCTA demonstrates good interobserver reproducibility for carotid plaque characterization, while intermodality agreement between MDCTA and DUS is moderate. Minor attenuation measurement differences do not significantly affect plaque classification; however, systematic intermodality differences in stenosis grading, plaque surface evaluation, and plaque length measurement should be considered in clinical decision-making. Full article

Computed tomography (CT) at the third lumbar vertebra (L3) is widely used for muscle quantification, but manual segmentation is labor intensive. This study externally validates an AI model, trained on a public dataset, for automated L3 muscle segmentation using an independent cohort, including a subgroup analysis of subject characteristics (e.g., age and a history of cancer). The AI model was trained on 900 CT scans with expert annotations from a publicly available repository. Validation was performed on 232 PET CT scans from the University Hospital Brussels, each manually segmented by an expert. Segmentation post-processing employed a density-based clustering algorithm to discard arm muscles and Hounsfield unit (HU) thresholding to refine the muscle segmentation. Performance was assessed using the Dice Similarity Coefficient (DSC) and Segmentation Surface Error (SSE). The model achieved a median DSC of 0.978 and a median SSE of 3.863 cm² across the validation set. At lower BMI values, the model was more prone to overestimation of muscle surface area. Most segmentation errors occurred in the abdominal wall muscles. Analysis showed no significant difference between arm positioning above the head and alongside the body, indicating robustness to minor artifacts from arm positioning. The AI model delivers accurate, automated L3 muscle segmentation, supporting larger-scale body composition studies. However, diminished accuracy at low BMI values and limited demographic diversity of the data highlight the need for broader validation. Full article

Background: In this study, we aimed to explore the relationship between quantitative indices derived from computed tomography (CT) attenuation histograms and disease prognosis in patients with pneumonia and acute respiratory failure. We also sought to assess the effectiveness of these parameters as clinical prognostic markers. Methods: CT images of patients with pneumonia and acute respiratory failure were analyzed using Vitrea^® Advanced Visualization software. The analyzed quantitative CT (qCT) indices included mean lung Hounsfield unit (HU) and density-based volume measurements, specifically low-, medium-, and high-density volume (LDV, MDV, and HDV). Comparative analyses were performed to examine the differences in the volume density between the lungs bilaterally; these were accompanied by regional analyses and density indices. All indices were calculated using previously defined and validated Hounsfield unit (HU) thresholds, which helped to ensure accurate and consistent quantitative measurements and facilitated a more robust evaluation of the prognostic potential of qCT parameters. Results: Quantitative CT indices proved to have significant prognostic value in predicting mortality. In multivariable analysis, Difference for Lung HDV > 193 mL emerged as an independent risk factor (aOR: 4.29, p = 0.041). The prognostic significance was especially evident in patients with unilateral dominant pneumonia, where Difference for Lung MDV >219 mL (aOR: 9.30, p = 0.03) and Difference for Lung HDV > 193 mL (aOR: 10.85, p = 0.02) emerged as strong independent predictors of mortality. In this subgroup, lung volume differences demonstrated the strongest diagnostic performance (AUC: 0.808, 95% CI: 0.667–0.908, p < 0.001). Conclusions: Clinical outcomes are associated with quantitative CT-derived lung volume and density difference indices. Inter-lung differences in Lung MDV and Lung HDV are linked to mortality and may provide additional prognostic information beyond conventional imaging methods. Prospective studies should be conducted to validate these findings, and caution should be exercised during their interpretation. Full article

The lemur tree frog (Agalychnis lemur), a critically endangered species, can benefit from ex situ conservation programs; however, managing amphibians under human care presents challenges, including the provision of appropriate nutrition. House crickets (Acheta domesticus), a common feeder insect, have an inverse calcium to phosphorus ratio (Ca:P; 0.15:1) and low calcium content (<0.3%). While gut-loading crickets with an 8% calcium diet can improve their calcium concentrations, no study has assessed the effects of dietary calcium on bone development in Agalychnis spp. Moreover, no study has examined how diet impacts the gut–skin axis and skin microbiome of these frogs. This study examined how crickets gut-loaded with either a 1.3% or 8% calcium diet affected lemur tree frog bone density and skin microbiome. We hypothesized that frogs consuming the 8% calcium diet would exhibit significantly higher Hounsfield units (HU; bone density) over time, as measured by micro-computed tomography (mCT), and that dietary calcium concentration would have no effect on skin bacterial and fungi microbiomes. Eleven juvenile lemur tree frogs underwent mCT scans at baseline and 90 and 180 days. Total body volume of interest analysis showed a significant increase in HU in the 8% calcium group compared to the 1.3% group (F = 9.9, p = 0.01). There was no significant difference noted in the alpha or beta diversities for the bacterial and fungal microbiomes between dietary groups. This study provides the first evidence of dietary calcium’s impact on bone density in lemur tree frogs, offering valuable insights for improving ex situ management of this species. Full article