Monitoring tumour response with proton resonance spectroscopy in low-grade gliomas

2001 ◽  
Vol 51 (3) ◽  
pp. 164
Author(s):  
L. Viviers ◽  
P. Murphy ◽  
J. Britton ◽  
C. Abson ◽  
M. Leach ◽  
...  
Keyword(s):  
Tumour Response ◽  
Resonance Spectroscopy ◽  
Low Grade ◽  
Proton Resonance ◽  
Low Grade Gliomas

scholarly journals Molecular features assisting in diagnosis, surgery, and treatment decision making in low-grade gliomas

2015 ◽  
Vol 38 (3) ◽  
pp. E2 ◽  
Author(s):  
Ricky Chen ◽  
Vijay M. Ravindra ◽  
Adam L. Cohen ◽  
Randy L. Jensen ◽  
Karen L. Salzman ◽  
...  
Keyword(s):  
Imaging Techniques ◽  
Molecular Genetic ◽  
Treatment Decision ◽  
Resonance Spectroscopy ◽  
Low Grade ◽  
Isocitrate Dehydrogenase 1 ◽  
Genetic Event ◽  
Surgical Interventions ◽  
Low Grade Gliomas ◽  
Molecular Features

The preferred management of suspected low-grade gliomas (LGGs) has been disputed, and the implications of molecular changes for medical and surgical management of LGGs are important to consider. Current strategies that make use of molecular markers and imaging techniques and therapeutic considerations offer additional options for management of LGGs. Mutations in the isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2) genes suggest a role for this abnormal metabolic pathway in the pathogenesis and progression of these primary brain tumors. Use of magnetic resonance spectroscopy can provide preoperative detection of IDH-mutated gliomas and affect surgical planning. In addition, IDH1 and IDH2 mutation status may have an effect on surgical resectability of gliomas. The IDH-mutated tumors exhibit better prognosis throughout every grade of glioma, and mutation may be an early genetic event, preceding lineage-specific secondary and tertiary alterations that transform LGGs into secondary glioblastomas. The O6-methylguanine-DNAmethyltransferase (MGMT) promoter methylation and 1p19q codeletion status can predict sensitivity to chemotherapy and radiation in low- and intermediate-grade gliomas. Thus, these recent advances, which have led to a better understanding of how molecular, genetic, and epigenetic alterations influence the pathogenicity of the different histological grades of gliomas, can lead to better prognostication and may lead to specific targeted surgical interventions and medical therapies.

Metabolic imaging of low-grade gliomas with three-dimensional magnetic resonance spectroscopy

2002 ◽  
Vol 53 (5) ◽  
pp. 1254-1264 ◽  
Author(s):  
Andrea Pirzkall ◽  
Sarah J Nelson ◽  
Tracy R McKnight ◽  
Michelle M Takahashi ◽  
Xiaojuan Li ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Three Dimensional ◽  
Metabolic Imaging ◽  
Resonance Spectroscopy ◽  
Low Grade ◽  
Low Grade Gliomas

scholarly journals CBMT-10. MUTANT ISOCITRATE DEHYDROGENASE 1 INHIBITION INDUCES A UNIQUE MRS-DETECTABLE METABOLIC SIGNATURE IN LOW-GRADE GLIOMAS

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi34-vi35
Author(s):  
Abigail Molloy ◽  
Aliya Lakhani ◽  
Chloé Najac ◽  
Elavarasan Subramani ◽  
Anne Marie Gillespie ◽  
...  
Keyword(s):  
Cell Lines ◽  
Isocitrate Dehydrogenase ◽  
Phase 1 ◽  
Genetically Engineered ◽  
Resonance Spectroscopy ◽  
Low Grade ◽  
Isocitrate Dehydrogenase 1 ◽  
1H Mrs ◽  
Low Grade Gliomas ◽  
Non Invasive

Abstract Mutations in isocitrate dehydrogenase 1/2 (IDHmut) are reported in 70–90% of low-grade gliomas and secondary glioblastomas. IDHmut catalyzes the reduction of a-ketoglutarate (a-KG) to 2-hydroxyglutarate (2-HG), an oncometabolite that drives tumorigenesis. Inhibition of IDHmut is therefore a rapidly emerging therapeutic approach and IDHmut inhibitors such as AG-120 and AG-881 have shown promising results in phase 1 and 2 clinical studies. The goal of this study was to identify early non-invasive metabolic biomarkers of IDHmut inhibition that can serve to moniter response to these therapies. We used 1H and 13C magnetic resonance spectroscopy (MRS) to investigate the response of two genetically-engineered IDHmut cell lines (U87-based and normal human astrocyte-based) to AG-120 and AG-881 treatment. As expected, in both cell lines, our 1H-MRS data indicated that AG-120 and AG-881 induced a significant decrease in 2-HG. Interestingly however, we also observed a significant increase in phosphocholine and glutamate, pointing to broader changes in the metabolism of treated cells and a unique MRS signature. To further investigate the increase in glutamate induced by AG-120 and AG-881 in our models, we used 13C-MRS and quantified the flux of [1-13C] glucose and [3-13C] glutamine to 13C-labeled glutamate. Our results indicate that both AG-120 and AG-881 significantly increase the flux of 13C-labeled glutamine to 13C glutamate, while the flux of 13C-labeled glucose to 13C glutamate remained unchanged. Further studies are currently underway to explore the utility of using hyperpolarized [1-13C]-glutamine and hyperpolarized [1-13C]-a-KG for monitoring flux to glutamate and 2-HG, and to validate these probes as additional biomarkers of response to IDHmut inhibition. Taken together, our studies indicate that IDHmut inhibition induces a unique MRS-detectable metabolic profile that can potentially be exploited for early non-invasive, clinically translatable detection of response to emerging IDHmut inhibitors.

scholarly journals A PERMANENT NEUROSURGICAL CHALLENGE: LOW GRADE GLIOMAS

2016 ◽  
Vol 15 (4) ◽  
pp. 160-167
Author(s):  
Daniel Mihai Teleanu ◽  
◽  
Nicolae-Stefan Bogaciu ◽  
Andreea Marinescu ◽  
Raluca Ioana Teleanu ◽  
...  
Keyword(s):  
Histopathological Examination ◽  
Histological Grade ◽  
Pathological Examination ◽  
Resonance Spectroscopy ◽  
Grading System ◽  
Low Grade ◽  
Low Grade Gliomas ◽  
Tumoral Lesion ◽  
One Year

Introduction. The utilization of Magnetic Resonance Spectroscopy (MRS) brings an important piece of information to an overall MR study, thus aiding the physician in making an accurate assumption regarding the histological grade of a tumor. The purpose of this study is to verify the reliability of MRS in correctly diagnosing both the nature of tumors and their grade. Material and methods. This is an observational study that was conducted from January 2011 to June 2016 on 49 patients confirmed to be low-grade gliomas (LGG) by pathological examination, who were admitted in our Neurosurgery Department in this period. Both retrospective and prospective data were collected. Inclusion criteria comprise unique tumoral lesion at the moment of diagnosis, follow-up for at least one year. Exclusion criteria included: other types of tumors with any location, patient refusal to undergo histopathological examination of the resected tissue, uncompliant and non-collaborating patients. Of all patients, 22 were subjected to an MRS study which suggested the presence of a low-grade glioma subsequently confirmed by the pathological examination. Results. MRS has been shown to provide accurate non-invasive diagnosis of LGG’s, by analyzing the concentration of metabolites inside the lesions which tend to be specific for these tumours: relatively elevated levels of N-acetylaspartate and creatine with reduced levels in the concentration of choline and absent lipids and lactate. It has also been observed that pre-operative MRS assists the physician in selecting the optimal place for biopsy, so that the pathological examination provides conclusive results. Conclusions. With the increased availability of MRI technology, MRS comes as a milestone in the advancement of more accurate and patient-friendly methods of diagnostic for pathologies as LGG’s which constitute a permanent challenge for neurosurgeons. The results of this study underlines the importance of MRS as a method for follow up patients with LGG, but cannot replace the need for obtaining bioptic tissue for pathological examination, especially after the new grading system of WHO, which was published in June 2016. This new grading system takes into account the molecular biology in predicting the natural history of cerebral tumours.

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.

Sign in / Sign up

Export Citation Format

Share Document