Multiparametric quantitative MRI for the evaluation of dysthyroid optic neuropathy.

OBJECTIVE: To evaluate the ability of quantitative MRI parameters for predicting dysthyroid optic neuropathy (DON). METHODS: We retrospectively collected and analyzed the clinical features and 3.0 T MRI data of 59 patients with Graves orbitopathy (GO), with (n = 26) and without DON (n = 33). We compared MRI quantitative parameters, including the modified muscle index (mMI), proptosis, volume of intra-orbital fat, mean apparent diffusion coefficient value, and T2 value of the optic nerve among patients with and without DON. A logistic regression analysis was performed to identify the risk factors associated with DON. Moreover, we performed a receiver operating characteristic curve analysis and decision curve analysis to evaluate the diagnostic performance of the identified parameters for DON. RESULTS: We studied 118 orbits (43 and 75 with and without DON, respectively). The mMI and mean T2 value of the optic nerve were significantly greater in orbits with DON (p < 0.001). A greater mMI at 21 mm (odds ratio (OR), 1.039; 95% confidence interval (CI): 1.019, 1.058) and higher mean T2 value of the optic nerve (OR, 1.035; 95% CI: 1.017, 1.054) were associated with a higher risk of DON. A model combining the mMI at 21 mm and mean T2 values for the optic nerve effectively predicted DON in patients with GO, with a sensitivity and specificity of 95.3% and 76%, respectively. CONCLUSION: A quantitative MRI parameter combining the mMI at 21 mm and mean T2 value of the optic nerve can be an effective imaging marker for identifying DON. KEY POINTS: • Patients with GO and DON had greater mMI than those without DON. • Optic nerves in patients with DON demonstrated an increased T2 value. • The quantitative MRI parameter combining the mMI at 21 mm and mean T2 value of the optic nerve is the most effective method for diagnosing DON.

Diffusion kurtosis imaging in evaluating gliomas: different region of interest selection methods on time efficiency, measurement repeatability, and diagnostic ability.

OBJECTIVES: Comparing the diagnostic efficacy of diffusion kurtosis imaging (DKI) derived from different region of interest (ROI) methods in tumor parenchyma for grading and predicting IDH-1 mutation and 1p19q co-deletion status of glioma patients and correlating with their survival data. METHODS: Sixty-six patients (29 females; median age, 45 years) with pathologically proved gliomas (low-grade gliomas, 36; high-grade gliomas, 30) were prospectively included, and their clinical data were collected. All patients underwent DKI examination. DKI maps of each metric were derived. Three groups of ROIs (ten spots, ROI-10s; three biggest tumor slices, ROI-3s; and whole-tumor parenchyma, ROI-whole) were manually drawn by two independent radiologists. The interobserver consistency, time spent, diagnostic efficacy, and survival analysis of DKI metrics based on these three ROI methods were analyzed. RESULTS: The intraexaminer reliability for all parameters among these three ROI methods was good, and the time spent on ROI-10s was significantly less than that of the other two methods (p < 0.001). DKI based on ROI-10s demonstrated a slightly better diagnostic value than the other two ROI methods for grading and predicting the IDH-1 mutation status of glioma, whereas DKI metrics derived from ROI-10s performed much better than those of the ROI-3s and ROI-whole in identifying 1p19q co-deletion. In survival analysis, the model based on ROI-10s that included patient age and mean diffusivity showed the highest prediction value (C-index, 0.81). CONCLUSIONS: Among the three ROI methods, the ROI-10s method had the least time spent and the best diagnostic value for a comprehensive evaluation of glioma. It is an effective way to process DKI data and has important application value in the clinical evaluation of glioma. KEY POINTS: • The intraexaminer reliability for all DKI parameters among different ROI methods was good, and the time spent on ROI-10 spots was significantly less than the other two ROI methods. • DKI metrics derived from ROI-10 spots performed the best in ROI selection methods (ROI-10s, ten-spot ROIs; ROI-3s, three biggest tumor slices ROI; and ROI-whole, whole-tumor parenchyma ROI) for a comprehensive evaluation of glioma. • The ROI-10 spots method is an effective way to process DKI data and has important application value in the clinical evaluation of glioma.