The evaluation of bone marrow edema in sacroiliac joint in patients with ankylosing spondylitis using magnetic resonance imaging Dixon sequence.

BACKGROUND: Bone marrow edema of the sacroiliac joint is the early imaging manifestation, an indicator of inflammatory activity of ankylosing spondylitis (AS) (Yang R, et. al. Medicine (Baltimore) 98:e14620, 2019). OBJECTIVE: The aim of the study was to investigate the value of magnetic resonance imaging (MRI) Dixon sequence in the diagnosis of marrow edema of the sacroiliac joint in patients with AS. METHODS: Forty-five patients with AS admitted in our hospital between November 2016 and February 2019 were selected retrospectively as the case group. Forty-five healthy subjects recruited between November 2016 and February 2019 served as the control group. Bath ankylosing spondylitis disease activity index (BASDAI), Bath ankylosing spondylitis functional index (BASFI), C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) were recorded after admission to the hospital. The Dixon sequence water-fat ratio of the iliac and sacral surfaces of the bilateral sacroiliac joints in the study group were compared with indicators above in order to find the correlation. RESULTS: The water-fat ratio under the bilateral sacroiliac joints on Dixon sequence images in the case group was significantly higher than that in the healthy control group (P<0.05). The Dixon sequence water-fat ratio of the iliac and sacral surfaces of the bilateral sacroiliac joints in the study group were positively correlated with spinal arthritis research (SPARCC), BASFI and BASDAI score (all P < 0.05), but did not correlate with ESR and CRP. CONCLUSION: The water-fat ratio of magnetic resonance Dixon sequence can be used as a reference index to evaluate the degree of bone marrow edema in active stage of sacroiliac arthritis.

Quantitative measurement of cartilage volume with automatic cartilage segmentation in knee osteoarthritis.

PURPOSE: To determine the reproducibility of the automatic cartilage segmentation method using a prototype KneeCaP software (version 1.3; Siemens Healthcare, Erlangen, Germany) and to compare the difference in cartilage volume (CV) between the normal knee joint and knee osteoarthritis (KOA) of different degrees by using the above software. MATERIALS AND METHODS: The study included 62 subjects with knee OA and 29 healthy control subjects. The cartilage lesion patients were divided into a mild-to-moderate OA group (n = 29) and severe OA group (n = 33). Automatic cartilage segmentation was performed on all the subjects, and among them, 19 knee cases were randomly selected to also do the manual cartilage segmentation. Statistical significance was determined with one-way analysis of variance (ANOVA), intraclass correlation coefficient (ICC), and Pearson correlation coefficient. Automatic segmentation was compared with the manual one. The relative cartilage volume percentages of the femur, tibia, and patella in the normal control/mild-to-moderate/severe OA groups were assessed. RESULTS: Comparing the cartilage volumes derived by manual and automatic segmentation, the ICC value for the knee joint, patella, femur, or tibia was 0.784, 0.815, 0.740, and 0.797. The relative cartilage volume percentages of the femur, tibia, and patella in the normal control/mild-to-moderate/severe OA groups were 57.28%/59.30%/62.45% (femur), 25.35%/23.46%/21.84% (tibia), and 17.37%/17.24%/15.71% (patella), respectively. Compared with the normal control group, the relative tibia cartilage volume percentage was lower in the mild-to-moderate OA group and the severe OA group. Corresponding index showed a similar difference between the mild-to-moderate OA group and the severe OA group (p < 0.001). CONCLUSION: This study demonstrated that the relative cartilage volume percentage is correlated with the semi-quantitative systems and may be a preferred outcome measure in clinical studies of OA. Automatic cartilage segmentation using KneeCaP delivered reliable results on high-spatial-resolution 3 T MR images for the healthy, mild-moderate OA patients. Key Points • The cartilage automatic segmentation has excellent reproducibility and was not affected by inter-observer variation. • The relative cartilage volume percentage is correlated with the semi-quantitative systems and may be a preferred outcome measure in clinical studies of OA.

Association of radiomic imaging features and gene expression profile as prognostic factors in pancreatic ductal adenocarcinoma.

In this study, we investigated whether radiomic features of CT image data can accurately predict HMGA2 and C-MYC gene expression status and identify the patient survival time using a machine learning approach in pancreatic ductal adenocarcinoma (PDAC). A cohort of 111 patients with PDAC was enrolled in our study. Radiomic features were extracted using conventional (shape and texture analysis) and deep learning approaches following to segmentation of preoperative CT data. To predict patient survival time, significant radiomic features were identified using a log-rank test. After surgical resection, level of HMGA2 and C-MYC gene expressions of PDAC tumor regions were classified using a support vector machines method. The model was evaluated in terms of accuracy, sensitivity, specificity, and area under the curve (AUC). Besides, inter-reader reliability analysis was used to demonstrate the robustness of the proposed features. The identified features consistently achieved good performance in survival prediction and classification of gene expression status, on images segmented by different radiologists. Using CT data from 111 patients, six features in the segmented region of images were highly correlated with survival time. Using extracted deep features of excised lesions from 47 patients, we observed an average AUC score of 0.90 with an accuracy of 95% in C-MYC prediction (sensitivity: 92% and specificity: 98%). In HGMA2 group, using shape features, the average AUC score was measured as 0.91 with an accuracy of 88% (sensitivity: 89% and specificity: 88%). In conclusion, the radiomic features of CT image can accurately predict the expression status of HMGA2 and C-MYC genes and identify the survival time of PDAC patients.

Pulmonary invasive fungal disease and bacterial pneumonia: a comparative study with high-resolution CT.

BACKGROUND: Early diagnosis of invasive fungal disease (IFD) is challenging. High-resolution computed tomography (CT) may improve IFD diagnosis; however, there are no definitive imaging signs for differentiating between bacterial pneumonia and IFD. METHODS: We retrospectively evaluated CT images of 208 patients with IFD (n = 102) or bacterial pneumonia (n = 106). We classified pulmonary opacities as consolidations, ground-glass opacities (GGOs), or nodules and recorded the presence of perinodular ground-glass halos, reversed halo sign (RSH), and cavitation (crescent-shaped or not). RESULTS: Consolidation appeared in 83.3% and 92.5% of patients with IFD and bacterial pneumonia, respectively. Multifocal non-segmental consolidation was more common in IFD (48%) than bacterial pneumonia (22.6%; P < 0.05). Segmental or subsegmental consolidation was more common in bacterial pneumonia (43.4%) than IFD (7.8%; P < 0.01). GGOs and nodules were more common in IFD than bacterial pneumonia (60.8% vs. 24.5% and 54.9% vs. 15.1%, respectively; each P < 0.05). Consolidation combined with GGO, nodules, or both GGO and nodules was more frequent in IFD than in bacterial pneumonia (each P < 0.05). Nodules with halo sign (n = 23) appeared in 22.5% and 3.8% of patients with IFD and bacterial pneumonia, respectively. Nodules with RSH appeared only in IFD, and those with cavitation appeared in 11.8% and 1.9% of patients with IFD and bacterial pneumonia, respectively. CONCLUSIONS: Consolidation plus GGO and nodules or consolidation plus nodules is suggestive for IFD. Segmental or subsegmental consolidations are more frequent in bacterial pneumonia than in IFD. Large nodules, as well as nodules with halo sign or both small and large nodules, are related to IFD.

Multi-parameter evaluation of lumbar intervertebral disc degeneration using quantitative magnetic resonance imaging techniques.

Objective: To quantitatively evaluate lumbar disc degeneration with recently-developed quantitative magnetic resonance imaging (MRI) techniques. A series of MRI parameters, including T2*, T1rho relaxation time, apparent diffusion coefficient and gagCEST, were compared and correlated with the Pfirrmann semi-quantitative classification of lumbar intervertebral disc degeneration; the most accurate and relevant MRI parameters of lumbar disc degeneration were identified. Materials and Methods: Thirty-seven subjects (age range, 18-74 years) with non-specific low back pain (LBP) for more than 6 months were enrolled. The L1/2-L5/S1 discs of each subject were measured and then analyzed. Results: The gagCEST value of the discs showed the best negative correlation with degeneration level (nucleus pulposus: r = -0.951, P < 0.001; annulus fibrosus: r = -0.938, P < 0.001). The discriminant analysis results showed that this parameter also had the highest correct rate using a single index (gagCEST discriminant accuracy = 82%). Conclusion: Early stage lumbar disc degeneration can be quantitatively evaluated with MRI using the chemical exchange saturation transfer technique.