scholarly journals Analysis of11C-methionine Uptake in Low-Grade Gliomas and Correlation with Proliferative Activity

2008 ◽  
Vol 29 (10) ◽  
pp. 1867-1871 ◽  
Author(s):  
T. Kato ◽  
J. Shinoda ◽  
N. Oka ◽  
K. Miwa ◽  
N. Nakayama ◽  
...  
Keyword(s):  
Proliferative Activity ◽  
Low Grade ◽  
Low Grade Gliomas ◽  
Methionine Uptake

Glucose consumption and methionine uptake in low-grade gliomas after iodine-125 brachytherapy

1996 ◽  
Vol 23 (5) ◽  
pp. 583-586 ◽  
Author(s):  
M. Würker ◽  
K. Herholz ◽  
J. Voges ◽  
U. Pietrzyk ◽  
H. Treuer ◽  
...  
Keyword(s):  
Glucose Consumption ◽  
Low Grade ◽  
Low Grade Gliomas ◽  
Methionine Uptake ◽  
Iodine 125

scholarly journals Domain Mapping and Deep Learning from Multiple MRI Clinical Datasets for Prediction of Molecular Subtypes in Low Grade Gliomas

2020 ◽  
Vol 10 (7) ◽  
pp. 463 ◽  
Author(s):  
Muhaddisa Barat Ali ◽  
Irene Yu-Hua Gu ◽  
Mitchel S. Berger ◽  
Johan Pallud ◽  
Derek Southwell ◽  
...  
Keyword(s):  
Molecular Subtypes ◽  
Negative Impact ◽  
Molecular Classification ◽  
Tumor Segmentation ◽  
Test Accuracy ◽  
Low Grade ◽  
Idh Mutation ◽  
Molecular Type ◽  
Low Grade Gliomas ◽  
Domain Mapping

Brain tumors, such as low grade gliomas (LGG), are molecularly classified which require the surgical collection of tissue samples. The pre-surgical or non-operative identification of LGG molecular type could improve patient counseling and treatment decisions. However, radiographic approaches to LGG molecular classification are currently lacking, as clinicians are unable to reliably predict LGG molecular type using magnetic resonance imaging (MRI) studies. Machine learning approaches may improve the prediction of LGG molecular classification through MRI, however, the development of these techniques requires large annotated data sets. Merging clinical data from different hospitals to increase case numbers is needed, but the use of different scanners and settings can affect the results and simply combining them into a large dataset often have a significant negative impact on performance. This calls for efficient domain adaption methods. Despite some previous studies on domain adaptations, mapping MR images from different datasets to a common domain without affecting subtitle molecular-biomarker information has not been reported yet. In this paper, we propose an effective domain adaptation method based on Cycle Generative Adversarial Network (CycleGAN). The dataset is further enlarged by augmenting more MRIs using another GAN approach. Further, to tackle the issue of brain tumor segmentation that requires time and anatomical expertise to put exact boundary around the tumor, we have used a tight bounding box as a strategy. Finally, an efficient deep feature learning method, multi-stream convolutional autoencoder (CAE) and feature fusion, is proposed for the prediction of molecular subtypes (1p/19q-codeletion and IDH mutation). The experiments were conducted on a total of 161 patients consisting of FLAIR and T1 weighted with contrast enhanced (T1ce) MRIs from two different institutions in the USA and France. The proposed scheme is shown to achieve the test accuracy of 74 . 81 % on 1p/19q codeletion and 81 . 19 % on IDH mutation, with marked improvement over the results obtained without domain mapping. This approach is also shown to have comparable performance to several state-of-the-art methods.

scholarly journals LGG-18. EVEROLIMUS TREATMENT IN PEDIATRIC PATIENTS AFFECTED BY LOW-GRADE GLIOMAS (pLGG) NON-TSC, BRAF v600-WT

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii369-iii369
Author(s):  
Antonella Cacchione ◽  
Evelina Miele ◽  
Maria Chiara Lodi ◽  
Andrea Carai ◽  
Giovanna Stefania Colafati ◽  
...  
Keyword(s):  
Adverse Event ◽  
Disease Progression ◽  
Mapk Pathway ◽  
Brain Mri ◽  
Mammalian Target Of Rapamycin ◽  
Low Grade ◽  
Duration Of Treatment ◽  
Tumor Biopsy ◽  
Low Grade Gliomas ◽  
Personalized Approach

Abstract BACKGROUND MAPK pathway is the hallmark of pediatric low grade gliomas (pLGGs); hyperactivation of mTOR (mammalian target of rapamycin) might be a suitable biomarker for therapeutic response. We investigated the feasibility of Everolimus, mTOR inhibitor, in patients affected by pLGGs. METHODS Patients 1 to 18 years old, diagnosed with pLGG, with a positive tumor biopsy for mTOR/phospho-mTOR and radiological and / or clinical disease progression, treated at Bambino Gesù Children’s Hospital in Rome were evaluated. Tumor DNA methylation analysis was performed in 10 cases. Exclusion criteria included: Tuberous Sclerosis patients, Sub Ependymal Giant Astrocytoma. Everolimus was administered orally at a dose of 2.5 mg or 5 mg daily based on body weight. Patients were evaluated with brain MRI every 4, 8 and 12 months after treatment start and every six months thereafter. RESULTS 16 patients were enrolled from September 2014 and 2019. The median age was 7.5 years old. All patients had at least one adverse event. Events rated as severe (grade 3/4) were reported in 6 patients. Stomatitis was the most frequent adverse event. One patient discontinued treatment due to grade 4 toxicity (ulcerative stomatitis and fatigue). The median duration of treatment was 21 months (4–57 months). Brain MRI evaluations have showed disease stability in 11 patients, partial response in 2 patients and disease progression in 3 patients. CONCLUSIONS Everolimus has proven to be well tolerated and effective treatment in terms of disease stability in patients with pLGGs. It’s also an excellent example of chemo-free personalized approach.

scholarly journals Hemispheric low-grade gliomas

2016 ◽  
Vol 32 (10) ◽  
pp. 1787-1787
Author(s):  
Gianpiero Tamburrini ◽  
Jose Hinojosa
Keyword(s):  
Low Grade ◽  
Low Grade Gliomas
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