Differentiation of treatment-related changes from tumour progression: a direct comparison between dynamic FET PET and ADC values obtained from DWI MRI

Werner, Jan-Michael; Stoffels, Gabriele; Lichtenstein, Thorsten; Borggrefe, Jan; Lohmann, Philipp; Ceccon, Garry; Shah, N. Jon; Fink, Gereon R.; Langen, Karl‐Josef; Kabbasch, Christoph; Galldiks, Norbert

doi:10.1007/s00259-019-04384-7

Cited by 54 publications

(70 citation statements)

References 53 publications

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“…Possible limitations of this study are the retrospective nature of the analysis as well as the relatively small sample size, even though decent compared to other studies [ 7 , 10 , 15 ]. Furthermore, the data was skewed in a way that PSP was less frequent than TP, which is not uncommon in this type of study given the low prevalence of PSP.…”

Section: Discussionmentioning

confidence: 91%

“…Furthermore, the data was skewed in a way that PSP was less frequent than TP, which is not uncommon in this type of study given the low prevalence of PSP. In fact, skewed datasets are pretty much the standard in studies investigating pseudoprogression [ 7 , 10 , 16 , 35 ]. This imbalance of classes should be considered when interpreting accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…The incidence varies according to different studies between 10 and 30% [ 8 , 9 ]. The unspecific changes in contrast-enhanced (CE) MRI may be associated with a breakdown of the blood-brain barrier due to inflammation, edema, necrosis, or ischemia [ 9 , 10 , 11 ]. The diagnosis of PSP, as per the modified Response Assessment in Neuro-Oncology (RANO) criteria [ 12 , 13 ], is based on either pathohistological confirmation through repeat surgery or a follow-up MRI (confirmatory MRI) obtained at least 4–6 weeks after the index MRI after chemoradiation completion [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

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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

Kebir

Schmidt

Weber

et al. 2020

Cancers

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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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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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

Kebir

Schmidt

Weber

et al. 2020

Cancers

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“…However, their sensitivities are comparable, and the diagnostic accuracies of these amino-acid PET tracers are at least similar to those of all other studied MRI techniques, including perfusion and diffusion MRI. Recently, it was demonstrated that 18 F-FET PET outperforms diffusion MRI in differentiating treatment-related changes from tumor progression (55). An additional consideration is that the advanced MRI methods suffer from limitations such as challenging interpretation and frequent impairment by susceptibility artifacts; in contrast, amino-acid PET scan reading is relatively easy because of high tumor-to-background contrast (9).…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Accuracy of PET Tracers for the Differentiation of Tumor Progression from Treatment-Related Changes in High-Grade Glioma: A Systematic Review and Metaanalysis

et al. 2019

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Posttreatment high-grade gliomas are usually monitored with contrast-enhanced MRI, but its diagnostic accuracy is limited as it cannot adequately distinguish between true tumor progression and treatment-related changes. According to recent Response Assessment in Neuro-Oncology recommendations, PET overcomes this limitation. However, it is currently unknown which tracer yields the best results. Therefore, a systematic review and metaanalysis were performed to compare the diagnostic accuracy of the different PET tracers in differentiating tumor progression from treatment-related changes in high-grade glioma patients. Methods: PubMed, Web of Science, and Embase were searched systematically. Study selection, data extraction, and quality assessment were performed independently by 2 authors. Metaanalysis was performed using a bivariate random-effects model when at least 5 studies were included. Results: The systematic review included 39 studies (11 tracers). 18 F-FDG (12 studies, 171 lesions) showed a pooled sensitivity and specificity of 84% (95% confidence interval, 72%-92%) and 84% (95% confidence interval, 69%-93%), respectively. O-(2-18 F-fluoroethyl)-L-tyrosine (18 F-FET) (7 studies, 172 lesions) demonstrated a sensitivity of 90% (95% confidence interval, 81%-95%) and specificity of 85% (95% confidence interval, 71%-93%). For S-11 C-methyl)-L-methionine (11 C-MET) (8 studies, 151 lesions), sensitivity was 93% (95% confidence interval, 80%-98%) and specificity was 82% (95% confidence interval, 68%-91%). The numbers of included studies for the other tracers were too low to combine, but sensitivity and specificity ranged between 93%-100% and 0%-100%, respectively, for 18 F-FLT; 85%-100% and 72%-100%, respectively, for 3,4-dihydroxy-6-18 F-fluoro-L-phenylalanine (18 F-FDOPA); and 100% and 70%-88%, respectively, for 11 C-choline. Conclusion: 18 F-FET and 11 C-MET, both amino-acid tracers, showed a comparably higher sensitivity than 18 F-FDG in the differentiation between tumor progression and treatment-related changes in high-grade glioma patients. The evidence for other tracers is limited; thus, 18 F-FET and 11 C-MET are preferred when available. Our results support the incorporation of amino-acid PET tracers for the treatment evaluation of high-grade gliomas.

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“…Using [ 18 F]FET or [ 18 F]FDOPA, especially the differentiation of radiation injury from tumor relapse in glioma patients, as well as in patients with brain metastases, can be obtained with a relatively high diagnostic accuracy between 80-90% ( Figure 2) [11,26,[67][68][69][70][71][72][73][74][75][76][77][78][79][80]. Importantly, in glioma patients, parameters derived from dynamic [ 18 F]FET PET acquisition may further increase the diagnostic accuracy [67,[74][75][76]78]. This has also been demonstrated in patients with brain metastases who underwent radiosurgery for brain metastases treatment [80,81].…”

Section: Value Of Amino Acid Pet Tracers For Brain Tumor Patientsmentioning

confidence: 99%

Current Landscape and Emerging Fields of PET Imaging in Patients with Brain Tumors

et al. 2020

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The number of positron-emission tomography (PET) tracers used to evaluate patients with brain tumors has increased substantially over the last years. For the management of patients with brain tumors, the most important indications are the delineation of tumor extent (e.g., for planning of resection or radiotherapy), the assessment of treatment response to systemic treatment options such as alkylating chemotherapy, and the differentiation of treatment-related changes (e.g., pseudoprogression or radiation necrosis) from tumor progression. Furthermore, newer PET imaging approaches aim to address the need for noninvasive assessment of tumoral immune cell infiltration and response to immunotherapies (e.g., T-cell imaging). This review summarizes the clinical value of the landscape of tracers that have been used in recent years for the above-mentioned indications and also provides an overview of promising newer tracers for this group of patients.

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Differentiation of treatment-related changes from tumour progression: a direct comparison between dynamic FET PET and ADC values obtained from DWI MRI

Cited by 54 publications

References 53 publications

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

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

Diagnostic Accuracy of PET Tracers for the Differentiation of Tumor Progression from Treatment-Related Changes in High-Grade Glioma: A Systematic Review and Metaanalysis

Current Landscape and Emerging Fields of PET Imaging in Patients with Brain Tumors

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