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Objective: A case of low-grade glioma in which 5-aminolevulinic acid (5-ALA) fluorescence was visualized by a digital exoscope is presented. Case presentation: A 14-year-old girl with recurrent paroxysmal episodes of a strange smell and nausea underwent magnetic resonance imaging (MRI) for further investigation. The MRI showed a tumor with an enhanced nodule in the right temporal lobe. The patient received 5-ALA preoperatively, and intraoperative observation using a 4 K-3-dimension digital exoscope (Olympus ORBEYE) showed that the tumor was fluorescent, which was useful in determining the extent of tumor removal. Postoperative MRI showed that the tumor was completely removed. The histopathological diagnosis was pleomorphic xanthoastrocytoma. She was discharged without any complications. Conclusions: 5-ALA-fluorescence-guided resection of low-grade glioma using the ORBEYE was useful for determining the extent of removal. [ABSTRACT FROM AUTHOR]

Background: Mutations in driver genes such as IDH and BRAF have been identified in gliomas. Meanwhile, dysregulations in the p53, RB1, and MAPK and/or PI3K pathways are involved in the molecular pathogenesis of glioblastoma. RAS family genes activate MAPK through activation of RAF and PI3K to promote cell proliferation. RAS mutations are a well-known driver of mutation in many types of cancers, but knowledge of their significance for glioma is insufficient. The purpose of this study was to reveal the frequency and the clinical phenotype of RAS mutant in gliomas.Methods: This study analysed RAS mutations and their clinical significance in 242 gliomas that were stored as unfixed or cryopreserved specimens removed at Kyoto University and Osaka National Hospital between May 2006 and October 2017. The hot spots mutation of IDH1/2, H3F3A, HIST1H3B, and TERT promoter and exon 2 and exon 3 of KRAS, HRAS, and NRAS were analysed with Sanger sequencing method, and 1p/19q codeletion was analysed with multiplex ligation-dependent probe amplification. DNA methylation array was performed in some RAS mutant tumours to improve accuracy of diagnosis.Results: RAS mutations were identified in four gliomas with three KRAS mutations and one NRAS mutation in one anaplastic oligodendroglioma, two anaplastic astrocytomas (IDH wild-type in each), and one ganglioglioma. RAS-mutant gliomas were identified with various types of glioma histology.Conclusion: RAS mutation appears infrequent, and it is not associated with any specific histological phenotype of glioma. [ABSTRACT FROM AUTHOR]

Gliomas are genetically and histopathologically heterogeneous. Intratumoral heterogeneity in the MGMT promoter methylation status is an important clinical biomarker of glioblastoma. A higher uptake of 11C-methionine in positron-emission tomography (PET) reportedly reflects increased MGMT promoter methylation; however, non-stereotactic comparison of MGMT methylation and 11C-methionine PET images may not be accurate. The present study examined the correlation between 11C-methionine uptake and MGMT promoter methylation in non-enhancing gliomas using stereotactic image-based histological analysis. Data were collected from 9 patients with newly diagnosed non-enhancing glioma who underwent magnetic resonance imaging and 11C-methionine PET during pre-surgical examination. Clinical data were also collected from 3 patients during repeat surgery. The correlation between 11C-methionine uptake and MGMT methylation or cell density was analyzed using histological specimens obtained by multiple stereotactic sampling and an exact local comparison of 11C-methionine PET images and histological specimens was made. A total of 31 stereotactic sample sites were identified. In newly diagnosed cases, the tumor to normal uptake (T/N) ratio revealed a significant positive correlation with MGMT methylation (R=0.54, P=0.009) and a marginal correlation with cell density (R=0.42, P=0.05). In recurrent cases, the T/N ratio demonstrated no correlation with MGMT methylation (R=0.01, P=0.97) or cell density (R=0.15, P=0.70). An increased uptake of 11C-methionine in PET may reflect increased MGMT promoter methylation according to stereotactic image-based histological analysis. 11C-methionine PET could therefore be a useful tool for detecting regional MGMT promoter methylation in non-enhancing primary glioma. [ABSTRACT FROM AUTHOR]

Simple Summary: This study evaluates the performance degradation of machine learning models for segmenting gliomas in brain magnetic resonance images caused by domain shift and proposed possible solutions. Although machine learning models exhibit significant potential for clinical applications, performance degradation in different cohorts is a problem that must be solved. In this study, we identify the impact of the performance degradation of machine learning models to be significant enough to render clinical applications difficult. This demonstrates that it can be improved by fine-tuning methods with a small number of cases from each facility, although the data obtained appeared to be biased. Our method creates a facility-specific machine learning model from a small real-world dataset and public dataset; therefore, our fine-tuning method could be a practical solution in situations where only a small dataset is available. Machine learning models for automated magnetic resonance image segmentation may be useful in aiding glioma detection. However, the image differences among facilities cause performance degradation and impede detection. This study proposes a method to solve this issue. We used the data from the Multimodal Brain Tumor Image Segmentation Benchmark (BraTS) and the Japanese cohort (JC) datasets. Three models for tumor segmentation are developed. In our methodology, the BraTS and JC models are trained on the BraTS and JC datasets, respectively, whereas the fine-tuning models are developed from the BraTS model and fine-tuned using the JC dataset. Our results show that the Dice coefficient score of the JC model for the test portion of the JC dataset was 0.779 ± 0.137, whereas that of the BraTS model was lower (0.717 ± 0.207). The mean Dice coefficient score of the fine-tuning model was 0.769 ± 0.138. There was a significant difference between the BraTS and JC models (p < 0.0001) and the BraTS and fine-tuning models (p = 0.002); however, no significant difference between the JC and fine-tuning models (p = 0.673). As our fine-tuning method requires fewer than 20 cases, this method is useful even in a facility where the number of glioma cases is small. [ABSTRACT FROM AUTHOR]