BACKGROUND AND PURPOSE: Contrast-enhanced 3D fast spin-echo T1 black-blood imaging selectively suppresses the signal of blood flow and could provide a higher contrast-to-noise ratio compared with contrast-enhanced 3D ultrafast gradient recalled echo (contrast-enhanced gradient recalled echo) and 2D spin-echo T1WI (contrast-enhanced spin-echo). The purpose of our study was to evaluate whether black-blood imaging can improve the diagnostic accuracy for leptomeningeal carcinomatosis compared with contrast-enhanced gradient recalled-echo and contrast-enhanced spin-echo and, furthermore, to determine whether the grade of leptomeningeal carcinomatosis evaluated on black-blood imaging is a significant predictor of progression-free survival. MATERIALS AND METHODS: Leptomeningeal carcinomatosis (n = 78) and healthy (n = 31) groups were enrolled. Contrast-enhanced gradient recalled-echo, contrast-enhanced spin-echo, and black-blood imaging were separately reviewed, and a diagnostic rating (positive, indeterminate, or negative) and grading of leptomeningeal carcinomatosis were assigned. The diagnostic accuracies of the 3 imaging sequences were compared in terms of leptomeningeal carcinomatosis detection. The Kaplan-Meier and the Cox proportional hazards model analyses were performed to determine the relationship between the leptomeningeal carcinomatosis grade evaluated on black-blood imaging and progression-free survival. RESULTS: Black-blood imaging showed a significantly higher sensitivity (97.43%) than contrast-enhanced gradient recalled-echo (64.1%) and contrast-enhanced spin-echo (66.67%) (P < .05). In terms of specificities, we did not find any significant differences among contrast-enhanced gradient recalled-echo (90.32%), contrast-enhanced spin-echo (90.32%), and black-blood imaging (96.77%) (P > .05). A Cox proportional hazards model identified the time to metastasis, Karnofsky Performance Scale status, and a combination of the leptomeningeal carcinomatosis grade with a linear pattern as independent predictors of progression-free survival (P < .05). CONCLUSIONS: Black-blood imaging can improve the diagnostic accuracy and predict progression-free survival in patients with leptomeningeal carcinomatosis.

Hong EK, et al. Korean J Radiol. 2020 Jul;21:e150. https://doi.org/10.3348/kjr.2020.0011

Song H, et al. Korean J Radiol. 2022 Oct;23(10):959-975. https://doi.org/10.3348/kjr.2022.0067

Objectives: This study evaluated the radiologic and radiomic features extracted from magnetic resonance imaging (MRI) in meningioma after radiation therapy and investigated the impact of radiation therapy in treating meningioma based on routine brain MRI. Methods: Observation (n = 100) and radiation therapy (n = 62) patients with meningioma who underwent MRI were randomly divided (7:3 ratio) into training (n = 118) and validation (n = 44) groups. Radiologic findings were analyzed. Radiomic features (filter types: original, square, logarithm, exponential, wavelet; feature types: first order, texture, shape) were extracted from the MRI. The most significant radiomic features were selected and applied to quantify the imaging phenotype using random forest machine learning algorithms. Area under the curve (AUC), sensitivity, and specificity for predicting both the training and validation sets were computed with multiple-hypothesis correction. Results: The radiologic difference in the maximum area and diameter of meningiomas between two groups was statistically significant. The tumor decreased in the treatment group. A total of 241 series and 1691 radiomic features were extracted from the training set. In univariate analysis, 24 radiomic features were significantly different (P < 0.05) between both groups. Best subsets were one original, three first-order, and six wavelet-based features, with an AUC of 0.87, showing significant differences (P < 0.05) in multivariate analysis. When applying the model, AUC was 0.76 and 0.79 for the training and validation set, respectively. Conclusion: In meningioma cases, better size reduction can be expected after radiation treatment. The radiomic model using MRI showed significant changes in radiomic features after radiation treatment.

Jo SW, et al. Korean J Radiol. 2021 Aug;22(8):1369-1378. https://doi.org/10.3348/kjr.2020.1272