Detecting Bone Marrow Edema of the Extremities on Spectral Computed Tomography Using a Three-Material Decomposition

Quantitative evaluation of disc degeneration using dual-energy CT: advantages of R-VH, D-VH values and the IVNCa + CT model

OBJECTIVE

To evaluate the correlation between dual-energy CT (DECT) virtual calcium free (VNCA), CT attenuation, the ratio and difference of VNCA to CT attenuation, and Pfirrmann grading of lumbar disc degeneration.

METHODS

A retrospective analysis on 135 intervertebral discs from 30 patients who underwent DECT and MR. Discs was graded using the Pfirrmann system. ROIs on the sagittal plane assessed HU value, VNCA value, Rho value, Z value, R-VH value, and D-VH value. Correlation, grade differences, and multivariate regression models were assessed. Diagnostic performance and cut-off values were determined using AUC.

RESULTS

VNCA (r = 0.589, P < 0.001), R-VH (r = 0.622, P < 0.001), and D-VH (r = 0.613, P < 0.001) moderately correlated with Pfirrmann grading. HU (r = 0.388, P < 0.001), Rho (r = 0.142, P = 0.102), and Z (r = -0.125, P = 0.153) showed a weak correlation. R-VH, D-VH, and VNCA had significantly higher correlation than HU. Statistically significant differences were observed in P values of VNCA, HU, R-VH, and D-VH in relative groups (P < 0.05), but not in Rho and Z values (P > 0.05). R-VH and D-VH had significant differences between Pfirrmann grades 1 and 2, and grades 2 and 3 (early stage) (P < 0.05). AUC readings of R-VH and D-VH (≥2,  ≥3, ≥4) were higher. The multivariate model IVNCa + CT had the highest AUC.

CONCLUSION

The new quantitative indices R-VH value and D-VH value of DECT have advantages over VNCA value and HU value in evaluating early-stage disc degeneration (≥2 grades, ≥3 grades). The multivariate model IVNCa + CT has the best AUC values for evaluating disc degeneration at all stages.

AI-based automated detection and stability analysis of traumatic vertebral body fractures on computed tomography

PURPOSE

We developed and tested a neural network for automated detection and stability analysis of vertebral body fractures on computed tomography (CT).

MATERIALS AND METHODS

257 patients who underwent CT were included in this Institutional Review Board (IRB) approved study. 463 fractured and 1883 non-fractured vertebral bodies were included, with 190 fractures unstable. Two readers identified vertebral body fractures and assessed their stability. A combination of a Hierarchical Convolutional Neural Network (hNet) and a fracture Classification Network (fNet) was used to build a neural network for the automated detection and stability analysis of vertebral body fractures on CT. Two final test settings were chosen: one with vertebral body levels C1/2 included and one where they were excluded.

RESULTS

The mean age of the patients was 68 ± 14 years. 140 patients were female. The network showed a slightly higher diagnostic performance when excluding C1/2. Accordingly, the network was able to distinguish fractured and non-fractured vertebral bodies with a sensitivity of 75.8 % and a specificity of 80.3 %. Additionally, the network determined the stability of the vertebral bodies with a sensitivity of 88.4 % and a specificity of 80.3 %. The AUC was 87 % and 91 % for fracture detection and stability analysis, respectively. The sensitivity of our network in indicating the presence of at least one fracture / one unstable fracture within the whole spine achieved values of 78.7 % and 97.2 %, respectively, when excluding C1/2.

CONCLUSION

The developed neural network can automatically detect vertebral body fractures and evaluate their stability concurrently with a high diagnostic performance.

Fracture Severity and Triangular Fibrocartilage Complex Injury in Distal Radius Fractures with or without Osteoporosis

The purpose of this study was to investigate the fracture morphology of distal radius fractures (DRFs) with the status of triangular fibrocartilage complex (TFCC) foveal insertion in patients with or without osteoporosis and to identify the relationship between osteoporosis and foveal tear. Seventy-five patients who underwent surgery for DRF from January 2021 to September 2023 were included. All patients were evaluated by standard radiography and dual-energy X-ray absorptiometry and underwent a 3.0 T magnetic-resonance imaging examination of the involved wrist to identify TFCC foveal tear. Patients were allocated into two groups according to the presence of osteoporosis: patients with osteoporosis (group I) and those without osteoporosis (group II). Group I showed a significantly larger displacement of fractures compared to group II (radial inclination; 13.7 ± 5.4 vs. 17.9 ± 4.2; p < 0.001, dorsal angulation; 22.2 ± 12.1 vs. 16.5 ± 9.4; p = 0.024, ulnar variance; 4.15 ± 2.1 vs. 2.2 ± 1.9; p < 0.001). Dorsal angulation and ulnar variance were found to be independent prognostic factors for TFCC foveal tear in logistic regression analysis. Displacement of fractures was related to osteoporosis, and dorsal angulation and ulnar variance were independent prognostic factors for TFCC foveal tear. However, osteoporosis was not identified as a factor associated with TFCC foveal tears.

Stable Housekeeping Genes in Bone Marrow, Adipose Tissue, and Amniotic Membrane-Derived Mesenchymal Stromal Cells for Orthopedic Regenerative Medicine Approaches

The therapeutic effect of mesenchymal stromal cells (MSCs) has been described for a variety of disorders, including those affecting musculoskeletal tissues. In this context, the literature reports several data about the regenerative effectiveness of MSCs derived from bone marrow, adipose tissue, and an amniotic membrane (BMSCs, ASCs, and hAMSCs, respectively), either when expanded or when acting as clinical-grade biologic pillars of products used at the point of care. To date, there is no evidence about the superiority of one source over the others from a clinical perspective. Therefore, a reliable characterization of the tissue-specific MSC types is mandatory to identify the most effective treatment, especially when tailored to the target disease. Because molecular characterization is a crucial parameter for cell definition, the need for reliable normalizers as housekeeping genes (HKGs) is essential. In this report, the stability levels of five commonly used HKGs (ACTB, EF1A, GAPDH, RPLP0, and TBP) were sifted into BMSCs, ASCs, and hAMSCs. Adult and fetal/neonatal MSCs showed opposite HKG stability rankings. Moreover, by analyzing MSC types side-by-side, comparison-specific HKGs emerged. The effect of less performant HKG normalization was also demonstrated in genes coding for factors potentially involved in and predicting MSC therapeutic activity for osteoarthritis as a model musculoskeletal disorder, where the choice of the most appropriate normalizer had a higher impact on the donors rather than cell populations when compared side-by-side. In conclusion, this work confirms HKG source-specificity for MSCs and suggests the need for cell-type specific normalizers for cell source or condition-tailored gene expression studies.

Advances in Musculoskeletal Imaging and Their Applications

Modern medical imaging systems provide ever-more information about the patient's health condition [...].