guest ::
| Home > Publications database > NIMG-38. NON-INVASIVE PREDICTION OF MGMT PROMOTER METHYLATION USING COMBINED FET PET/MRI RADIOMICS > print |
| Home > Publications database > NIMG-38. NON-INVASIVE PREDICTION OF MGMT PROMOTER METHYLATION USING COMBINED FET PET/MRI RADIOMICS > print |
| 001 | 908215 | ||
| 005 | 20220622190124.0 | ||
| 037 | _ | _ | |a FZJ-2022-02466 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Lohmann, Philipp |0 P:(DE-Juel1)145110 |b 0 |
| 245 | _ | _ | |a NIMG-38. NON-INVASIVE PREDICTION OF MGMT PROMOTER METHYLATION USING COMBINED FET PET/MRI RADIOMICS |
| 260 | _ | _ | |c 2020 |
| 336 | 7 | _ | |a Text |2 DataCite |
| 336 | 7 | _ | |a Communication |b comm |m comm |0 PUB:(DE-HGF)4 |s 1655871122_6306 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a MISC |2 BibTeX |
| 336 | 7 | _ | |a OTHER |2 ORCID |
| 336 | 7 | _ | |a Other |2 DINI |
| 336 | 7 | _ | |a Personal Communication |0 4 |2 EndNote |
| 520 | _ | _ | |a BACKGROUNDRecently, the Response Assessment in Neuro-Oncology (RANO) Working Group emphasized the additional diagnostic value of amino acid PET in addition to MRI. However, the number of studies using amino acid PET/MRI radiomics is still low. We investigated the potential of combined O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET/MRI radiomics for the non-invasive prediction of the O6-methylguanine-DNA methyl-transferase (MGMT) promoter methylation status in glioma patients.METHODSSeventy-one patients with newly diagnosed glioma (predominantly WHO grade III and IV glioma, 82%) underwent a hybrid FET PET/MRI scan. Forty-six patients (65%) had a methylated MGMT promoter. The tumor and tumor subregions were manually segmented on conventional MRI. In total, 199 standardized features were obtained from FET PET, contrast-enhanced T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR) MRI. After feature extraction and data normalization, patients were randomly assigned to a training and a test dataset for final model evaluation in a ratio of 70/30, with a balanced distribution of the MGMT promoter methylation status. Feature selection was performed by recursive feature elimination using random forest regressors. For the final model generation, the number of features was limited to seven to avoid data overfitting. Different algorithms for model generation were compared, and the model performance in the training data was assessed by 5-fold cross-validation. Finally, the best performing models were applied to the test dataset to evaluate the robustness of the models.RESULTSIn the test dataset, the best radiomics signatures obtained from MRI or FET PET alone achieved diagnostic accuracies for the prediction of the MGMT promoter methylation of 64% and 70%, respectively. In contrast, the highest diagnostic accuracy of 83% was obtained by combining FET PET and MRI features.CONCLUSIONCombined FET PET/MRI radiomics allows the non-invasive prediction of the MGMT promoter methylation status in patients with gliomas, providing more diagnostic information than either modality alone. |
| 536 | _ | _ | |a 5253 - Neuroimaging (POF4-525) |0 G:(DE-HGF)POF4-5253 |c POF4-525 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Meissner, Anna-Katharina |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Werner, Jan-Michael |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Stoffels, Gabriele |0 P:(DE-Juel1)131627 |b 3 |
| 700 | 1 | _ | |a Kocher, Martin |0 P:(DE-Juel1)173675 |b 4 |
| 700 | 1 | _ | |a Bauer, Elena |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Fink, Gereon |0 P:(DE-Juel1)131720 |b 6 |
| 700 | 1 | _ | |a Shah, Nadim |0 P:(DE-Juel1)131794 |b 7 |
| 700 | 1 | _ | |a Langen, Karl-Josef |0 P:(DE-Juel1)131777 |b 8 |u fzj |
| 700 | 1 | _ | |a Galldiks, Norbert |0 P:(DE-Juel1)143792 |b 9 |
| 909 | C | O | |o oai:juser.fz-juelich.de:908215 |p VDB |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)145110 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)131627 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)173675 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 6 |6 P:(DE-Juel1)131720 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 7 |6 P:(DE-Juel1)131794 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 8 |6 P:(DE-Juel1)131777 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 9 |6 P:(DE-Juel1)143792 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-525 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Decoding Brain Organization and Dysfunction |9 G:(DE-HGF)POF4-5253 |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-11-20170113 |k INM-11 |l Jara-Institut Quantum Information |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-4-20090406 |k INM-4 |l Physik der Medizinischen Bildgebung |x 1 |
| 920 | 1 | _ | |0 I:(DE-Juel1)VDB1046 |k JARA-BRAIN |l Jülich-Aachen Research Alliance - Translational Brain Medicine |x 2 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-3-20090406 |k INM-3 |l Kognitive Neurowissenschaften |x 3 |
| 980 | _ | _ | |a comm |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)INM-11-20170113 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-4-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)VDB1046 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-3-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|