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| 100 | 1 | _ | |a Kebir, Sied |0 0000-0002-0678-5852 |b 0 |
| 245 | _ | _ | |a A Preliminary Study on Machine Learning-Based Evaluation of Static and Dynamic FET-PET for the Detection of Pseudoprogression in Patients with IDH-Wildtype Glioblastoma |
| 260 | _ | _ | |a Basel |c 2020 |b MDPI |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a Pseudoprogression (PSP) detection in glioblastoma remains challenging and has important clinical implications. We investigated the potential of machine learning (ML) in improving the performance of PET using O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) for differentiation of tumor progression from PSP in IDH-wildtype glioblastoma. We retrospectively evaluated the PET data of patients with newly diagnosed IDH-wildtype glioblastoma following chemoradiation. Contrast-enhanced MRI suspected PSP/TP and all patients underwent subsequently an additional dynamic FET-PET scan. The modified Response Assessment in Neuro-Oncology (RANO) criteria served to diagnose PSP. We trained a Linear Discriminant Analysis (LDA)-based classifier using FET-PET derived features on a hold-out validation set. The results of the ML model were compared with a conventional FET-PET analysis using the receiver-operating-characteristic (ROC) curve. Of the 44 patients included in this preliminary study, 14 patients were diagnosed with PSP. The mean (TBRmean) and maximum tumor-to-brain ratios (TBRmax) were significantly higher in the TP group as compared to the PSP group (p = 0.014 and p = 0.033, respectively). The area under the ROC curve (AUC) for TBRmax and TBRmean was 0.68 and 0.74, respectively. Using the LDA-based algorithm, the AUC (0.93) was significantly higher than the AUC for TBRmax. This preliminary study shows that in IDH-wildtype glioblastoma, ML-based PSP detection leads to better diagnostic performance |
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| 700 | 1 | _ | |a Schmidt, Teresa |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Weber, Matthias |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Lazaridis, Lazaros |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Galldiks, Norbert |0 P:(DE-Juel1)143792 |b 4 |
| 700 | 1 | _ | |a Langen, Karl-Josef |0 P:(DE-Juel1)131777 |b 5 |
| 700 | 1 | _ | |a Kleinschnitz, Christoph |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Hattingen, Elke |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Herrlinger, Ulrich |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Lohmann, Philipp |0 P:(DE-Juel1)145110 |b 9 |
| 700 | 1 | _ | |a Glas, Martin |0 P:(DE-HGF)0 |b 10 |e Corresponding author |
| 773 | _ | _ | |a 10.3390/cancers12113080 |g Vol. 12, no. 11, p. 3080 - |0 PERI:(DE-600)2527080-1 |n 11 |p 3080 - |t Cancers |v 12 |y 2020 |x 2072-6694 |
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