- Vessel Wall Thickening and Enhancement in High-Resolution Intracranial Vessel Wall Imaging: A Predictor of Future Ischemic Events in Moyamoya Disease
Twenty-nine patients with Moyamoya disease were enrolled in this study. The median age at symptom onset was 12 years. A total of 166 steno-occlusive lesions were detected by high-resolution intracranial vessel wall imaging. Eleven lesions with concentric wall thickening (6.6%) were noted in 9 patients. Ten concentric contrast-enhancing lesions were observed in 8 patients, of which 4 lesions in 3 patients showed grade II enhancement. The presence of contrast enhancement and wall thickening showed a statistically significant association with ischemic events within 3 months before and after the vessel wall imaging. The presence of wall thickening and enhancement may predict future ischemic events in patients with MMD.
- Does Increasing Packing Density Using Larger Caliber Coils Improve Angiographic Results of Embolization of Intracranial Aneurysms at 1 Year: A Randomized Trial
Does Embolization with Larger Coils Lead to Better Treatment of Aneurysms (DELTA) was an investigator-initiated multicenter prospective, parallel, randomized, controlled clinical trial. Patients had 4- to 12-mm unruptured aneurysms. Treatment allocation to either 15- (experimental group) or 10-caliber coils (control group) was randomized 1:1 using a Web-based platform. The primary efficacy outcome was a major recurrence or a residual aneurysm at follow-up angiography at 12 ± 2 months adjudicated by an independent core lab blinded to the treatment allocation. The trial was stopped after 210 patients were recruited between November 2013 and June 2017 when funding was interrupted. On an intent-to-treat analysis, the primary outcome was reached in 37 patients allocated to 15-caliber coils and 36 patients allocated to 10-caliber coils. Safety and other clinical outcomes were similar. Coiling of aneurysms randomized to 15-caliber coils achieved higher packing densities compared with 10-caliber coils, but this had no impact on the angiographic outcomes at 1 year, which were primarily driven by aneurysm size and initial angiographic results.
- Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas
Fifty patients with high-grade gliomas from the authors’ hospital and 128 patients with high-grade gliomas from The Cancer Genome Atlas were included in this study. For each patient, the authors calculated 348 hand-crafted radiomics features and 8192 deep features generated by a pretrained convolutional neural network. They then applied feature selection and Elastic Net-Cox modeling to differentiate patients into long- and short-term survivors. In the 50 patients with high-grade gliomas from their institution, the combined feature analysis framework classified the patients into long- and short-term survivor groups with a log-rank test P value <.001. In the 128 patients from The Cancer Genome Atlas, the framework classified patients into long- and short-term survivors with a log-rank test P value of .014. In conclusion, the authors report successful production and initial validation of a deep transfer learning model combining radiomics and deep features to predict overall survival of patients with glioblastoma from postcontrast T1-weighed brain MR imaging.
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