Trial reportPloS one2020
Optimization of time frame binning for FDOPA uptake quantification in glioma.
Trial report in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Usefulness ofCancer imaging : the official publication of the International Cancer Imaging Society · 2020Pooled it
- Non-invasive quantification ofEJNMMI research · 2024Article
- Non-invasive quantification ofResearch square · 2023Article
- Diagnostic accuracy of glioma pseudoprogression identification with positron emission tomography imaging: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 2023Article
- Review
- Impact of point-spread function reconstruction on dynamic and staticQuantitative imaging in medicine and surgery · 2022Article
- CombiningJournal of neuro-oncology · 2021Article
- Additive Value of Dynamic FDOPA PET/CT for Glioma Grading.Frontiers in medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introduction3,4-dihydroxy-6-[18F]fluoro-L-phenylalanine (FDOPA) uptake quantification in glioma assessment can be distorted using a non-optimal time frame binning of time-activity curves (TAC). Under-sampling or over-sampling dynamic PET images induces significant variations on kinetic parameters quantification. We aimed to optimize temporal time frame binning for dynamic FDOPA PET imaging.
methodsFourteen patients with 33 tumoral TAC with biopsy-proven gliomas were analysed. The mean SUVmax tumor-to-brain ratio (TBRmax) were compared at 20 min and 35 min post-injection (p.i). Five different time frame samplings within 20 min were compared: 11x10sec-6x15sec-5x20sec-3x300sec; 8x15sec- 2x30sec- 2x60sec- 3x300sec; 6x20sec- 8x60sec- 2x300sec; 10x30sec- 3x300sec and 4x45sec- 3x90sec- 5x150sec. The reversible single-tissue compartment model with blood volume parameter (VB) was selected using the Akaike information criterion. K1 values extracted from 1024 noisy simulated TAC using Monte Carlo method from the 5 different time samplings were compared to a target K1 value as the objective, which is the average of the K1 values extracted from the 33 lesions using an imaging-derived input function for each patient.
resultsThe mean TBRmax was significantly higher at 20 min p.i. than at 35 min p.i (respectively 1.4 +/- 0.8 and 1.2 +/- 0.6; p <0.001). The target K1 value was 0.161 mL/ccm/min. The 8x15sec- 2x30sec- 2x60sec- 3x300sec time sampling was the optimal time frame binning. K1 values extracted using this optimal time frame binning were significantly different with K1 values extracted from the other time frame samplings, except with K1 values obtained using the 11x10sec- 6x15sec -5x20sec-3x300sec time frame binning.
conclusionsThis optimal sampling schedule design (8x15sec- 2x30sec- 2x60sec- 3x300sec) could be used to minimize bias in quantification of FDOPA uptake in glioma using kinetic analysis.
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