No abstract

BACKGROUNDDiffusion-weighted magnetic resonance imaging has shown promise in the detection and quantification of hepatic fibrosis. In addition, the liver has numerous endogenous micro-RNAs (miRs) that play important roles in the regulation of biological processes such as cell proliferation and hepatic fibrosis.AIMTo assess diffusion-weighted magnetic resonance imaging and miRs in diagnosing and staging hepatic fibrosis in patients with chronic hepatitis C.METHODSThis prospective study included 208 patients and 82 age- and sex-matched controls who underwent diffusion-weighted magnetic resonance imaging of the abdomen, miR profiling, and liver biopsy. Pathological scoring was classified according to the METAVIR scoring system. The apparent diffusion coefficient (ADC) and miR were calculated and correlated with pathological scoring.RESULTSThe ADC value decreased significantly with the progression of fibrosis, from controls (F0) to patients with early fibrosis (F1 and F2) to those with late fibrosis (F3 and F4) (median 1.92, 1.53, and 1.25 × 10-3 mm2/s, respectively) (P = 0.001). The cut-off ADC value used to differentiate patients from controls was 1.83 × 10-3 mm2/s with an area under the curve (AUC) of 0.992. Combining ADC and miR-200b revealed the highest AUC (0.995) for differentiating patients from controls with an accuracy of 96.9%. The cut-off ADC used to differentiate early fibrosis from late fibrosis was 1.54 × 10-3 mm2/s with an AUC of 0.866. The combination of ADC and miR-200b revealed the best AUC (0.925) for differentiating early fibrosis from late fibrosis with an accuracy of 80.2%. The ADC correlated with miR-200b (r = - 0.61, P = 0.001), miR-21 (r = - 0.62, P = 0.001), and miR-29 (r = 0.52, P = 0.001).CONCLUSIONCombining ADC and miRs offers an alternative surrogate non-invasive diagnostic tool for diagnosing and staging hepatic fibrosis in patients with chronic hepatitis C.

No abstract

Purpose This study aimed to evaluate the role of diffusion tensor imaging of microstructural changes in gray and white matter in Crigler-Najjar syndrome type I. Patient and Methods A prospective study was conducted on 10 patients with Crigler-Najjar syndrome type I and 10 age- and sex-matched children who underwent diffusion tensor imaging of the brain. Mean diffusivity (MD) and fractional anisotropy (FA) of gray and white matter were measured. Results There was a significantly higher MD of the gray matter regions including the globus pallidus, thalamus, caudate head, substantia nigra, and dentate nucleus in patients versus controls (P = 0.007, 0.001, 0.014, 0.003, and 0.002), respectively. The areas under the curve (AUC) of MD of the globus pallidus and thalamus used to differentiate patients from controls were 0.93 and 0.925, respectively. There was a significant difference in MD of the frontal white matter and posterior limb of the internal capsule in patients versus controls (P = 0.001 and 0.02), respectively. The AUCs of MD of these regions used to differentiate patients from controls were 0.82 and 0.8. There was a significant difference in FA of the frontal white matter and posterior limb of the internal capsule in patients versus controls (P = 0.006 and 0.006), respectively. The AUCs of FA of these regions were 0.83 and 0.85, respectively. The MD of the globus pallidus correlated with serum bilirubin (r = 0.87 and P = 0.001). Conclusion Diffusion tensor imaging can detect microstructural changes of deep gray matter and some regions of white matter in Crigler-Najjar syndrome type I.

To assess whether the integration between (a) functional imaging features that will be extracted from diffusion-weighted imaging (DWI); and (b) shape and texture imaging features as well as volumetric features that will be extracted from T2-weighted magnetic resonance imaging (MRI) can noninvasively improve the diagnostic accuracy of thyroid nodules classification. Patients and methods: In a retrospective study of 55 patients with pathologically proven thyroid nodules, T2-weighted and diffusion-weighted MRI scans of the thyroid gland were acquired. Spatial maps of the apparent diffusion coefficient (ADC) were reconstructed in all cases.To quantify the nodules'morphology, we used spherical harmonics as a new parametric shape descriptor to describe the complexity of the thyroid nodules in addition to traditional volumetric descriptors (e.g., tumor volume and cuboidal volume). To capture the inhomogeneity of the texture of the thyroid nodules, we used the histogram-based statistics (e.g., kurtosis, entropy, skewness, etc.) of the T2-weighted signal. To achieve the main goal of this paper, a fusion system using an artificial neural network (NN) is proposed to integrate both the functional imaging features (ADC) with the structural morphology and texture features. This framework has been tested on 55 patients (20 patients with malignant nodules and 35 patients with benign nodules), using leave-one-subject-out (LOSO) for training/testing validation tests. Results: The functionality, morphology, and texture imaging features were estimated for 55 patients. The accuracy of the computer-aided diagnosis (CAD) system steadily improved as we integrate the proposed imaging features. The fusion system combining all biomarkers achieved a