O-(2-[18F]fluoroethyl)-l-tyrosine PET in gliomas: influence of data processing in different centres

Filß, Christian; Albert, Nathalie L.; Böning, Guido; Kops, Elena Rota; Suchorska, Bogdana; Stoffels, Gabriele; Galldiks, Norbert; Shah, N. Jon; Mottaghy, Felix M.; Bartenstein, Peter; Tonn, Jörg C.; Langen, Karl‐Josef

doi:10.1186/s13550-017-0316-x

Cited by 18 publications

(11 citation statements)

References 26 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We here demonstrate that the identification of two distinct prognostic subgroups among patients with IDH-wildtype astrocytic gliomas of WHO grades III or IV is possible using a TTP threshold of 25 min. As suggested previously [45], one reason might be the slightly different dynamic FET PET imaging protocol used in different centres, e.g. scanning time of 50 min in our centre compared with 40 min in other centres [18,26,27].…”

Section: Discussionmentioning

confidence: 87%

Prediction of survival in patients with IDH-wildtype astrocytic gliomas using dynamic O-(2-[18F]-fluoroethyl)-l-tyrosine PET

Bauer

Stoffels

Blau

et al. 2020

Eur J Nucl Med Mol Imaging

Self Cite

View full text Add to dashboard Cite

Purpose Integrated histomolecular diagnostics of gliomas according to the World Health Organization (WHO) classification of 2016 has refined diagnostic accuracy and prediction of prognosis. This study aimed at exploring the prognostic value of dynamic O-(2-[ 18 F]fluoroethyl)-L-tyrosine (FET) PET in newly diagnosed, histomolecularly classified astrocytic gliomas of WHO grades III or IV. Methods Before initiation of treatment, dynamic FET PET imaging was performed in patients with newly diagnosed glioblastoma (GBM) and anaplastic astrocytoma (AA). Static FET PET parameters such as maximum and mean tumour/brain ratios (TBR max/mean ), the metabolic tumour volume (MTV) as well as the dynamic FET PET parameters time-to-peak (TTP) and slope, were obtained. The predictive ability of FET PET parameters was evaluated concerning the progression-free and overall survival (PFS, OS). Using ROC analyses, threshold values for FET PET parameters were obtained. Subsequently, univariate Kaplan-Meier and multivariate Cox regression survival analyses were performed to assess the predictive power of these parameters for survival. Results Sixty patients (45 GBM and 15 AA patients) of two university centres were retrospectively identified. Patients with isocitrate dehydrogenase (IDH)-mutant or O 6 -methylguanine-DNA-methyltransferase (MGMT) promoter-methylated tumours had a significantly longer PFS and OS (both P < 0.001). Furthermore, ROC analysis of IDH-wildtype glioma patients (n = 45) revealed that a TTP > 25 min (AUC, 0.90; sensitivity, 90%; specificity, 87%; P < 0.001) was highly prognostic for longer PFS (13 vs. 7 months; P = 0.005) and OS (29 vs. 12 months; P < 0.001). In contrast, at a lower level of significance, TBR max , TBR mean , and MTV were only prognostic for longer OS (P = 0.004, P = 0.038, and P = 0.048, respectively). Besides complete resection and a methylated MGMT promoter, TTP remained significant in multivariate survival analysis (all P ≤ 0.02), indicating an independent predictor for OS. Conclusions Our data suggest that dynamic FET PET allows the identification of patients with longer OS among patients with newly diagnosed IDH-wildtype GBM and AA.

show abstract

Section: Discussionmentioning

confidence: 87%

Prediction of survival in patients with IDH-wildtype astrocytic gliomas using dynamic O-(2-[18F]-fluoroethyl)-l-tyrosine PET

Bauer

Stoffels

Blau

et al. 2020

Eur J Nucl Med Mol Imaging

Self Cite

View full text Add to dashboard Cite

show abstract

“…There is no consensus on how the adaptive thresholding should be applied. Filss et al showed that the threshold choice applied had logically a large importance on tumor volume [ 26 ]. It is important to note that because of the low resolution and manual segmentation, the absence of thresholding (either fixed or adaptive) would have introduced contour overflow and may also have decreased performance.…”

Section: Discussionmentioning

confidence: 99%

Automatic lesion detection and segmentation of 18F-FET PET in gliomas: A full 3D U-Net convolutional neural network study

et al. 2018

View full text Add to dashboard Cite

IntroductionAmino-acids positron emission tomography (PET) is increasingly used in the diagnostic workup of patients with gliomas, including differential diagnosis, evaluation of tumor extension, treatment planning and follow-up. Recently, progresses of computer vision and machine learning have been translated for medical imaging. Aim was to demonstrate the feasibility of an automated 18F-fluoro-ethyl-tyrosine (18F-FET) PET lesion detection and segmentation relying on a full 3D U-Net Convolutional Neural Network (CNN).MethodsAll dynamic 18F-FET PET brain image volumes were temporally realigned to the first dynamic acquisition, coregistered and spatially normalized onto the Montreal Neurological Institute template. Ground truth segmentations were obtained using manual delineation and thresholding (1.3 x background). The volumetric CNN was implemented based on a modified Keras implementation of a U-Net library with 3 layers for the encoding and decoding paths. Dice similarity coefficient (DSC) was used as an accuracy measure of segmentation.ResultsThirty-seven patients were included (26 [70%] in the training set and 11 [30%] in the validation set). All 11 lesions were accurately detected with no false positive, resulting in a sensitivity and a specificity for the detection at the tumor level of 100%. After 150 epochs, DSC reached 0.7924 in the training set and 0.7911 in the validation set. After morphological dilatation and fixed thresholding of the predicted U-Net mask a substantial improvement of the DSC to 0.8231 (+ 4.1%) was noted. At the voxel level, this segmentation led to a 0.88 sensitivity [95% CI, 87.1 to, 88.2%] a 0.99 specificity [99.9 to 99.9%], a 0.78 positive predictive value: [76.9 to 78.3%], and a 0.99 negative predictive value [99.9 to 99.9%].ConclusionsWith relatively high performance, it was proposed the first full 3D automated procedure for segmentation of 18F-FET PET brain images of patients with different gliomas using a U-Net CNN architecture.

show abstract

“…Textural feature analysis may thus prove useful as part of the diagnostic work-up rather than as a stand-alone measure. In order to assess the value of textural feature analysis in clinical routine, comparative studies evaluating the effect of different PET scanners and parameters on the comparability and reproducibility of textural feature analysis warrants further investigation 26 – 28 .…”

Section: Discussionmentioning

confidence: 99%

Predicting IDH genotype in gliomas using FET PET radiomics

Lohmann

Lerche

Bauer

et al. 2018

Sci Rep

Self Cite

View full text Add to dashboard Cite

Mutations in the isocitrate dehydrogenase (IDH mut) gene have gained paramount importance for the prognosis of glioma patients. To date, reliable techniques for a preoperative evaluation of IDH genotype remain scarce. Therefore, we investigated the potential of O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET radiomics using textural features combined with static and dynamic parameters of FET uptake for noninvasive prediction of IDH genotype. Prior to surgery, 84 patients with newly diagnosed and untreated gliomas underwent FET PET using a standard scanner (15 of 56 patients with IDH mut) or a dedicated high-resolution hybrid PET/MR scanner (11 of 28 patients with IDH mut). Static, dynamic and textural parameters of FET uptake in the tumor area were evaluated. Diagnostic accuracy of the parameters was evaluated using the neuropathological result as reference. Additionally, FET PET and textural parameters were combined to further increase the diagnostic accuracy. The resulting models were validated using cross-validation. Independent of scanner type, the combination of standard PET parameters with textural features increased significantly diagnostic accuracy. The highest diagnostic accuracy of 93% for prediction of IDH genotype was achieved with the hybrid PET/MR scanner. Our findings suggest that the combination of conventional FET PET parameters with textural features provides important diagnostic information for the non-invasive prediction of the IDH genotype.

show abstract

O-(2-[18F]fluoroethyl)-l-tyrosine PET in gliomas: influence of data processing in different centres

Cited by 18 publications

References 26 publications

Prediction of survival in patients with IDH-wildtype astrocytic gliomas using dynamic O-(2-[18F]-fluoroethyl)-l-tyrosine PET

Prediction of survival in patients with IDH-wildtype astrocytic gliomas using dynamic O-(2-[18F]-fluoroethyl)-l-tyrosine PET

Automatic lesion detection and segmentation of 18F-FET PET in gliomas: A full 3D U-Net convolutional neural network study

Predicting IDH genotype in gliomas using FET PET radiomics

Contact Info

Product

Resources

About