Serum Markers of Methylation Status in Relation to Brain MRI Measures and Risk of Dementia

2018 ◽  
Author(s):  
Babak Hooshmand ◽  
Helga Refsum ◽  
A. David Smith ◽  
Grégoria Kalpouzos ◽  
Francesca Mangialasche ◽  
...  
Keyword(s):  
Brain Mri ◽  
Methylation Status ◽  
Serum Markers

GLIA-Deep: Glioblastoma Image Analysis Using Deep Learning Convolutional Neural Networks to Accurately Classify Gene Methylation and Predict Drug Effectiveness

2020 ◽  
Author(s):  
Viraj Mehta
Keyword(s):  
Neural Networks ◽  
Dna Repair ◽  
Precision Medicine ◽  
Survival Time ◽  
Convolutional Neural Networks ◽  
Alkylating Agent ◽  
Brain Mri ◽  
Methylation Status ◽  
Chemotherapy Drugs ◽  
Mri Scans

Glioblastoma multiforme is a deadly brain cancer with a median patient survival time of 18-24 months, despite aggressive treatments. This limited success is due to a combination of aggressive tumor behavior, genetic heterogeneity of the disease within a single patient’s tumor, resistance to therapy, and lack of precision medicine treatments. A single specimen using a biopsy cannot be used for complete assessment of the tumor’s microenvironment, making personalized care limited and challenging. Temozolomide (TMZ) is a commercially approved alkylating agent used to treat glioblastoma, but around 50% of temozolomide-treated patients do not respond to it due to the over-expression of O6-methylguanine methyltransferase (MGMT). MGMT is a DNA repair enzyme that rescues tumor cells from alkylating agent-induced damage, leading to resistance to chemotherapy drugs. Epigenetic silencing of the MGMT gene by promoter methylation results in decreased MGMT protein expression, reduced DNA repair activity, increased sensitivity to TMZ, and longer survival time. Thus, it is paramount that clinicians determine the methylation status of patients to provide personalized chemotherapy drugs. However, current methods for determining this via invasive biopsies or manually curated features from brain MRI (Magnetic Resonance Imaging) scans are time- and cost- intensive, and have a very low accuracy. Authors present a novel approach of using convolutional neural networks to predict methylation status and recommend patient-specific treatments via an analysis of brain MRI scans. The authors have developed an AI platform, GLIA-Deep, using a U-Net architecture and a ResNet-50 architecture trained on genomic data from TCGA (The Cancer Genome Atlas through the National Cancer Institute) and brain MRI scans from TCIA (The Cancer Imaging Archive). GLIA-Deep performs tumor region identification and determines MGMT methylation status with 90% accuracy in less than 5 seconds, a real-time analysis that eliminates huge time and cost investments of invasive biopsies. Using computational modeling, the analysis further recommends microRNAs that modulate MGMT gene expression by translational repression to make glioma cells TMZ sensitive, thereby improving the survival of glioblastoma patients with unmethylated MGMT. GLIA-Deep is a completely integrated, end-to-end, cost-effective and time-efficient platform that advances precision medicine by recommending personalized therapies from an analysis of individual MRI scans to improving glioblastoma treatment options.

P14.86 Predictive value of MGMT promoter (pMGMT) methylation status on pseudoprogression (PsP) and survival analysis in Glioblastoma (GBM) patients: a retrospective single institutional analysis

2021 ◽  
Vol 23 (Supplement_2) ◽  
pp. ii54-ii54
Author(s):  
V Interno’ ◽  
P De Santis ◽  
L Stucci ◽  
C Porta
Keyword(s):  
Survival Analysis ◽  
Brain Mri ◽  
Methylation Status ◽  
Response Assessment ◽  
Potential Effect ◽  
Primary Brain Tumor ◽  
Survival Outcome ◽  
Maximal Extension ◽  
Radiological Response ◽  
True Progression

Abstract BACKGROUND Glioblastoma is the most common and aggressive primary brain tumor. Conventional therapies, such as maximal extension of surgery followed by radiotherapy (RT) and chemotherapy with Temozolomide (TMZ) have not resulted in major improvements in terms of patients’ outcome, overall survival (OS) still remaining poor. In this context, radiological response assessment after radiotherapy remains challenging due to the potential effect of radionecrosis, often mimicking tumor progression. Differentiation between PsP and true progression is required to avoid further unnecessary surgeries, or the premature discontinuation of TMZ. It is known that pMGMT methylated patients respond better to chemotherapy than unmethylated counterpart, so, tumor cells necrosis can be enhanced in this setting. The aim of the study is to observe the correlation between pMGMT methylation status with the incidence of PsP in GBM patients at the first radiological evaluation after RT. MATERIALS AND METHODS Patients with histologically diagnosis of GBM from 2017 to 2021 and availability of pMGMT methylation status were enrolled. PsP was radiologically defined at first brain MRI after RT in case of increasing size of the enhancing component and of peritumoral oedema that remain stable or decrease after antioedema therapy, such as a clinical improvement was observed. RESULTS We analysed 55 GBM patients, 35 (64%) displayed pMGMT methylation whereas 20 (36%) resulted pMGMT unmethylated. PsP was evident in 29 patients (53%), all of them showed methylation of pMGMT. In our analysis, none of pMGMT unmethylated patients experienced PsP. Regarding survival outcome for pMGMT methylated patients, our analysis shows a mPFS of 8.7 (95% CI: 5–10) months versus 9.3 (95%CI: 4.6–12.3) months in methylated and unmethylated respectively (p=0.87). CONCLUSIONS Methylation status of pMGMT showed to be predictor of PsP in GBM patients. If validated, this information could be very useful to guide clinicians in differentiating PsP from true progression. To date, our survival analysis regarding PFS showed no statistical difference among methylated patients with respect to the presence or absence of PsP. Thus, PsP seems not to be a marker of responsiveness to common treatment. Further data are needed to validate our results.

scholarly journals MRI-BASED DEEP LEARNING METHOD FOR DETERMINING METHYLATION STATUS OF THE O6-METHYLGUANINE–DNA METHYLTRANSFERASE PROMOTER OUTPERFORMS TISSUE BASED METHODS IN BRAIN GLIOMAS

2020 ◽  
Author(s):  
Chandan Ganesh Bangalore Yogananda ◽  
Bhavya R. Shah ◽  
Sahil S. Nalawade ◽  
Gowtham K. Murugesan ◽  
Frank F. Yu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dna Methyltransferase ◽  
Cross Validation ◽  
Brain Mri ◽  
Methylation Status ◽  
Magnetic Resonance Images ◽  
The Cancer Genome Atlas ◽  
Tumor Segmentation ◽  
Methylguanine Dna Methyltransferase ◽  
Mgmt Promoter

ABSTRACTPURPOSEMethylation of the O6-Methylguanine-DNA Methyltransferase (MGMT) promoter results in epigenetic silencing of the MGMT enzyme and confers an improved prognosis and treatment response in gliomas. The purpose of this study was to develop a deep-learning network for determining the methylation status of the MGMT Promoter in gliomas using T2-w magnetic resonance images only.METHODSBrain MRI and corresponding genomic information were obtained for 247 subjects from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). 163 subjects had a methylated MGMT promoter. A T2-w image only network (MGMT-net) was developed to determine MGMT promoter methylation status and simultaneous single label tumor segmentation. The network was trained using 3D-Dense-UNets. Three-fold cross-validation was performed to generalize the networks’ performance. Dice-scores were computed to determine tumor segmentation accuracy.RESULTSMGMT-net demonstrated a mean cross validation accuracy of 94.73% across the 3 folds (95.12%, 93.98%, and 95.12%, standard dev=0.66) in predicting MGMT methylation status with a sensitivity and specificity of 96.31% ±0.04 and 91.66% ±2.06, respectively and a mean AUC of 0.93 ±0.01. The whole tumor segmentation mean Dice-score was 0.82 ± 0.008.CONCLUSIONWe demonstrate high classification accuracy in predicting the methylation status of the MGMT promoter using only T2-w MR images that surpasses the sensitivity, specificity, and accuracy of invasive histological methods such as pyrosequencing, methylation-specific PCR, and immunofluorescence methods. This represents an important milestone toward using MRI to predict glioma histology, prognosis, and response to treatment.

Serum Markers Predict Severe Preeclampsia

Ob Gyn News ◽  
2011 ◽  
Vol 46 (5) ◽  
pp. 8
Author(s):  
SUSAN LONDON
Keyword(s):  
Serum Markers ◽  
Severe Preeclampsia

Effects of enteral nutrition on nutritional serum markers of 161 hospital patients: a preliminary analysis

2004 ◽  
Vol 29 (02) ◽  
Author(s):  
N Maisonneuve ◽  
CA Raguso ◽  
S Gottraux ◽  
C Pichard
Keyword(s):  
Enteral Nutrition ◽  
Preliminary Analysis ◽  
Serum Markers ◽  
Hospital Patients

USEFULNESS OF BRAIN MRI IN DIAGNOSIS OF NBIA IN PKAN AND NON-PKAN PATIENTS

2006 ◽  
Vol 37 (S 1) ◽  
Author(s):  
T Kmiec ◽  
E Jurkiewicz ◽  
S Jozwiak ◽  
I Pakula-Kosciesza ◽  
M Ebhart ◽  
...  
Keyword(s):  
Brain Mri
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