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BACKGROUND: There is limited literature on the use of positron emission tomography (PET) for benign tumors originating in the brain ventricles, and the use of multiple tracers for subependymal giant cell astrocytoma (SEGA) has not been reported. The authors compared the PET findings in two SEGA cases with past reports and literature, exploring the distinctive characteristics of SEGA on PET. OBSERVATIONS: In a 21-year-old female with SEGA, the authors utilized 18F-fluorodeoxyglucose (18F-FDG), 11C-methionine (11C-MET), 18F-fluorothymidine (18F-FLT), 18F-fluoromisonidazole, and 18F-THK5351 tracers. Additionally, in a 6-year-old girl, the authors performed 11C-MET PET. LESSONS: The results indicated the accumulation of all tracers except 18F-FDG, with particularly intense accumulation noted with 18F-FLT. In particular, 18F-FLT demonstrated accumulation comparable to that observed in malignant tumors. This study suggests that multiple PET tracers can provide valuable insights into the characterization of SEGA, with 18F-FLT showing particular promise as a distinctive marker of blood-brain barrier disruption. Further research in larger cohorts may enhance our understanding of metabolic patterns in SEGA and aid in its diagnosis and treatment. https://thejns.org/doi/10.3171/CASE24111.
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BACKGROUND: Hypertrophic cardiomyopathy (HCM) is often concomitant with sleep-disordered breathing (SDB), which can cause adverse cardiovascular events. Although an appropriate approach to SDB prevents cardiac remodelling, detection of concomitant SDB in patients with HCM remains suboptimal. Thus, we aimed to develop a machine learning-based discriminant model for SDB in HCM. METHODS: In the present multicentre study, we consecutively registered patients with HCM and performed nocturnal oximetry. The outcome was a high Oxygen Desaturation Index (ODI), defined as 3% ODI >10, which significantly correlated with the presence of moderate or severe SDB. We randomly divided the whole participants into a training set (80%) and a test set (20%). With data from the training set, we developed a random forest discriminant model for high ODI based on clinical parameters. We tested the ability of the discriminant model on the test set and compared it with a previous logistic regression model for distinguishing SDB in patients with HCM. RESULTS: Among 369 patients with HCM, 228 (61.8%) had high ODI. In the test set, the area under the receiver operating characteristic curve of the discriminant model was 0.86 (95% CI 0.77 to 0.94). The sensitivity was 0.91 (95% CI 0.79 to 0.98) and specificity was 0.68 (95% CI 0.48 to 0.84). When the test set was divided into low-probability and high-probability groups, the high-probability group had a higher prevalence of high ODI than the low-probability group (82.4% vs 17.4%, OR 20.9 (95% CI 5.3 to 105.8), Fisher's exact test p<0.001). The discriminant model significantly outperformed the previous logistic regression model (DeLong test p=0.03). CONCLUSIONS: Our study serves as the first to develop a machine learning-based discriminant model for the concomitance of SDB in patients with HCM. The discriminant model may facilitate cost-effective screening tests and treatments for SDB in the population with HCM.