A reference framework for standardization and harmonization of CT radiomics features on cadaveric sample.

Radiomics features (RFs) serve as quantitative metrics to characterize shape, density/intensity, and texture patterns in radiological images. Despite their promise, RFs exhibit reproducibility challenges across acquisition settings, thus limiting implementation into clinical practice. In this investigation, we evaluate the effects of different CT scanners and CT acquisition protocols (KV, mA, field-of-view, and reconstruction kernel settings) on RFs extracted from lumbar vertebrae of a cadaveric trunk. Employing univariate and multivariate Generalized Linear Models (GLM), we evaluated the impact of each acquisition parameter on RFs. Our findings indicate that variations in mA had negligible effects on RFs, while alterations in kV resulted in exponential changes in several RFs, notably First Order (94.4%), GLCM (87.5%), and NGTDM (100%). Moreover, we demonstrated that a tailored GLM model was superior to the ComBat algorithm in harmonizing CT images. GLM achieved R2 > 0.90 in 21 RFs (19.6%), contrasting ComBat's mean R2 above 0.90 in only 1 RF (0.9%). This pioneering study unveils the effects of CT acquisition parameters on bone RFs in cadaveric specimens, highlighting significant variations across parameters and scanner datasets. The proposed GLM model presents a robust solution for mitigating these differences, potentially advancing harmonization efforts in Radiomics-based studies across diverse CT protocols and vendors.

CT Cadaveric dataset for Radiomics features stability assessment in lumbar vertebrae.

Radiomics features (RFs) studies have showed limitations in the reproducibility of RFs in different acquisition settings. To date, reproducibility studies using CT images mainly rely on phantoms, due to the harness of patient exposure to X-rays. The provided CadAIver dataset has the aims of evaluating how CT scanner parameters effect radiomics features on cadaveric donor. The dataset comprises 112 unique CT acquisitions of a cadaveric truck acquired on 3 different CT scanners varying KV, mA, field-of-view, and reconstruction kernel settings. Technical validation of the CadAIver dataset comprises a comprehensive univariate and multivariate GLM approach to assess stability of each RFs extracted from lumbar vertebrae. The complete dataset is publicly available to be applied for future research in the RFs field, and could foster the creation of a collaborative open CT image database to increase the sample size, the range of available scanners, and the available body districts.

The gut-brain axis: Correlation of choroid plexus volume and permeability with inflammatory biomarkers in Crohn's disease.

BACKGROUND: The dysregulation of the gut-brain axis in chronic inflammatory bowel diseases can cause neuro-psychological disturbances, but the underlying mechanisms are still not fully understood. The choroid plexus (CP) maintains brain homeostasis and nourishment through the secretion and clearance of cerebrospinal fluid. Recent research has demonstrated the existence of a CP vascular barrier in mice which is modulated during intestinal inflammation. This study investigates possible correlations between CP modifications and inflammatory activity in patients with Crohn's disease (CD). METHODS: In this prospective study, 17 patients with CD underwent concomitant abdominal and brain 3 T MRI. The volume and permeability of CP were compared with levels of C-reactive protein (CRP), fecal calprotectin (FC), sMARIA and SES-CD scores. RESULTS: The CP volume was negatively correlated with CRP levels (R = -0.643, p-value = 0.024) and FC (R = -0.571, p-value = 0.050). DCE metrics normalized by CP volume were positively correlated with CRP (K-trans: R = 0.587, p-value = 0.045; Vp: R = 0.706, p-value = 0.010; T1: R = 0.699, p-value = 0.011), and FC (Vp: R = 0.606, p-value = 0.037). CONCLUSIONS: Inflammatory activity in patients with CD is associated with changes in CP volume and permeability, thus supporting the hypothesis that intestinal inflammation could affect the brain through the modulation of CP vascular barrier also in humans.

Quantification of cauda equina nerve root dispersion through radiomics features in weight-bearing MRI in normal subjects and spinal canal stenosis patients.

OBJECTIVE: To quantify the distribution of cauda equina nerve roots in supine and upright positions using manual measurements and radiomics features both in normal subjects and in lumbar spinal canal stenosis (LSCS) patients. METHODS: We retrospectively recruited patients who underwent weight-bearing MRI in supine and upright positions for back pain. 3D T2-weighted isotropic acquisition (3D-HYCE) sequences were used to develop a 3D convolutional neural network for identification and segmentation of lumbar vertebrae. Para-axial reformatted images perpendicular to the spinal canal and parallel to each vertebral endplate were automatically extracted. From each level, we computed the maximum antero-posterior (AP) and latero-lateral (LL) dispersion of nerve roots; further, radiomics features were extracted to quantify standardized metrics of nerve root distribution. RESULTS: We included 16 patients with LSCS and 20 normal subjects. In normal subjects, nerve root AP dispersion significantly increased from supine to upright position (p < 0.001, L2-L5 levels), and radiomics features showed an increase in non-uniformity. In LSCS subjects, in the upright position AP dispersion of nerve roots and entropy-related features increased caudally to the stenosis level (p < 0.001) and decreased cranially (p < 0.001). Moreover, entropy-related radiomics features negatively correlated with pre-operative Pain Numerical Rating Scale. Comparison between normal subjects and LSCS patients showed a difference in AP dispersion and increase of variance cranially to the stenosis level (p < 0.001) in the upright position. CONCLUSIONS: Nerve root distribution inside the dural sac changed between supine and upright positions, and radiomics features were able to quantify the differences between normal and LSCS subjects. CLINICAL RELEVANCE STATEMENT: The distribution of cauda equina nerve roots and the redundant nerve root sign significantly varies between supine and upright positions in normal subjects and spinal canal stenosis patients, respectively. Radiomics features quantify nerve root dispersion and correlates with pain severity. KEY POINTS: • Weight-bearing MRI depicts spatial distribution of the cauda equina in both supine and upright positions in normal subjects and spinal stenosis patients. • Radiomics features can quantify the effects of spinal stenosis on the dispersion of the cauda equina in the dural sac. • In the orthostatic position, dispersion of nerve roots is different in lumbar spinal stenosis patients compared to that in normal subjects; entropy-related features negatively correlated with pre-operative Pain Numerical Rating Scale.

CT-based radiomics can identify physiological modifications of bone structure related to subjects' age and sex.

PURPOSE: Radiomics of vertebral bone structure is a promising technique for identification of osteoporosis. We aimed at assessing the accuracy of machine learning in identifying physiological changes related to subjects' sex and age through analysis of radiomics features from CT images of lumbar vertebrae, and define its generalizability across different scanners. MATERIALS AND METHODS: We annotated spherical volumes-of-interest (VOIs) in the center of the vertebral body for each lumbar vertebra in 233 subjects who had undergone lumbar CT for back pain on 3 different scanners, and we evaluated radiomics features from each VOI. Subjects with history of bone metabolism disorders, cancer, and vertebral fractures were excluded. We performed machine learning classification and regression models to identify subjects' sex and age respectively, and we computed a voting model which combined predictions. RESULTS: The model was trained on 173 subjects and tested on an internal validation dataset of 60. Radiomics was able to identify subjects' sex within single CT scanner (ROC AUC: up to 0.9714), with lower performance on the combined dataset of the 3 scanners (ROC AUC: 0.5545). Higher consistency among different scanners was found in identification of subjects' age (R2 0.568 on all scanners, MAD 7.232 years), with highest results on a single CT scanner (R2 0.667, MAD 3.296 years). CONCLUSION: Radiomics features are able to extract biometric data from lumbar trabecular bone, and determine bone modifications related to subjects' sex and age with great accuracy. However, acquisition from different CT scanners reduces the accuracy of the analysis.