Dataset Information

Validation of magnetic resonance imaging-based automatic high-grade glioma segmentation accuracy via ¹¹C-methionine positron emission tomography.

ABSTRACT: Brain Tumor Image Analysis (BraTumIA) is a fully automated segmentation tool dedicated to detecting brain tumors imaged by magnetic resonance imaging (MRI). BraTumIA has recently been applied to several clinical investigations; however, the validity of this novel method has not yet been fully examined. The present study was conducted to validate the quality of tumor segmentation with BraTumIA in comparison with results from ¹¹C-methionine positron emission tomography (MET-PET). A total of 45 consecutive newly diagnosed high-grade gliomas imaged by MRI and MET-PET were analyzed. Automatic tumor segmentation was conducted by BraTumIA and the resulting segmentation images were registered to MET-PET. Three-dimensional conformal association between these two modalities was calculated, considering MET-PET as the gold standard. High underestimation and overestimation errors were observed in tumor segmentation calculated by BraTumIA compared with MET-PET. Furthermore, when the tumor/normal ratio threshold was set at 1.3 from MET-PET, the BraTumIA false-positive fraction was ~0.4 and the false-negative fraction was 0.9. By tightening this threshold to 2.0, the BraTumIA false-positive fraction was 0.6 and the false-negative fraction was 0.6. Following comparison of segmentation performance with BraTumIA with regard to glioblastoma (GBM) and World Health Organization (WHO) grade III glioma, GBM exhibited better segmentation compared with WHO grade III glioma. Although BraTumIA may be able to detect enhanced tumors, non-enhancing tumors and necrosis, the spatial concordance rate with MET-PET was relatively low. Careful interpretation is therefore required when using this technique.

SUBMITTER: Ozaki T

PROVIDER: S-EPMC6732988 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Differentiating tumor recurrence or progression from pseudoprogression during surveillance of pediatric high-grade gliomas (PHGGs) using MRI, the primary imaging modality for evaluation of brain tumors, can be challenging. The aim of this study was to evaluate whether 11C-methionine PET, a molecular imaging technique that detects functionally active tumors, is useful for further evaluating MRI changes concerning for tumor recurrence during routine surveillance. Methods : Using 11C-methionine PET during follow-up visits, we evaluated 27 lesions in 26 patients with new or worsening MRI abnormalities for whom tumor recurrence was of concern. We performed quantitative and qualitative assessments of both 11C-methionine PET and MRI data to predict the presence of tumor recurrence. Further, to assess for an association with overall survival (OS), we plotted the time from development of the imaging changes against survival. Results: Qualitative evaluation of 11C-methionine PET achieved 100% sensitivity, 60% specificity, and 93% accuracy to correctly predict the presence of tumors in 27 new or worsening MRI abnormalities. Qualitative MRI evaluation achieved sensitivity ranging from 86% to 95%, specificity ranging from 40% to 60%, and accuracy ranging from 85% to 89%. The interobserver agreement for 11C-methionine PET assessment was 100%, whereas the interobserver agreement was only 50% for MRI (P < 0.01). Quantitative MRI and 11C-methionine PET evaluation using receiver-operating characteristics demonstrated higher specificity (80%) than did qualitative evaluations (40%-60%). Postcontrast enhancement volume, metabolic tumor volume, tumor-to-brain ratio, and presence of tumor as determined by consensus MRI assessment were inversely associated with OS. Conclusion: 11C-methionine PET has slightly higher sensitivity and accuracy for correctly predicting tumor recurrence, with excellent interobserver agreement, than does MRI. Quantitative 11C-methionine PET can also predict OS. These findings suggest that 11C-methionine PET can be useful for further evaluation of MRI changes during surveillance of previously treated PHGGs.

Project description:ObjectiveSynaptic loss is an early feature of neurodegenerative disease models, and is severe in post mortem clinical studies, including frontotemporal dementia. Positron emission tomography (PET) with radiotracers that bind to synaptic vesicle glycoprotein 2A enables quantification of synaptic density in vivo. This study used [11 C]UCB-J PET in participants with behavioral variant frontotemporal dementia (bvFTD), testing the hypothesis that synaptic loss is severe and related to clinical severity.MethodsEleven participants with clinically probable bvFTD and 25 age- and sex-matched healthy controls were included. Participants underwent dynamic [11 C]UCB-J PET, structural magnetic resonance imaging, and a neuropsychological battery, including the revised Addenbrooke Cognitive Examination, and INECO frontal screening. General linear models compared [11 C]UCB-J binding potential maps and gray matter volume between groups, and assessed associations between synaptic density and clinical severity in patients. Analyses were also performed using partial volume corrected [11 C]UCB-J binding potential from regions of interest (ROIs).ResultsPatients with bvFTD showed severe synaptic loss compared to controls. [11 C]UCB-J binding was reduced bilaterally in medial and dorsolateral frontal regions, inferior frontal gyri, anterior and posterior cingulate gyrus, insular cortex, and medial temporal lobe. Synaptic loss in the frontal and cingulate regions correlated significantly with cognitive impairments. Synaptic loss was more severe than atrophy. Results from ROI-based analyses mirrored the voxelwise results.InterpretationIn accordance with preclinical models, and human postmortem evidence, there is widespread frontotemporal loss of synapses in symptomatic bvFTD, in proportion to severity. [11 C]UCB-J PET could support translational studies and experimental medicine strategies for new disease-modifying treatments for neurodegeneration. ANN NEUROL 2023;93:142-154.

Dataset Information

Validation of magnetic resonance imaging-based automatic high-grade glioma segmentation accuracy via ¹¹C-methionine positron emission tomography.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Validation of magnetic resonance imaging-based automatic high-grade glioma segmentation accuracy via 11C-methionine positron emission tomography.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Validation of magnetic resonance imaging-based automatic high-grade glioma segmentation accuracy via ¹¹C-methionine positron emission tomography.