ArticleEJNMMI research2014
Insight on automated lesion delineation methods for PET data.
Article in EJNMMI research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
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- Radiotherapy planning of lymphomas: role of metabolic imaging with PET/CT.Annals of nuclear medicine · 2022Review
- Impact of time of flight and point spread function on quantitative parameters of lung lesions inBMC medical imaging · 2021Article
- Correlation analysis between metabolic tumor burden measured by positron emission tomography/computed tomography and the 2015 World Health Organization classification of lung adenocarcinoma, with a risk prediction model of tumor spread through air spaces.Translational cancer research · 2020Article
- Design and evaluation of an accurate CNR-guided small region iterative restoration-based tumor segmentation scheme for PET using both simulated and real heterogeneous tumors.Medical & biological engineering & computing · 2020Article
- Baseline PET features to predict prognosis in primary mediastinal B cell lymphoma: a comparative analysis of different methods for measuring baseline metabolic tumour volume.European journal of nuclear medicine and molecular imaging · 2019Article
- Multi-modal image analysis for semi-automatic segmentation of the total liver and liver arterial perfusion territories for radioembolization.EJNMMI research · 2019Article
- Predictive Value of Preoperative Volume-Based 18F-2-Fluoro-2-Deoxy-d-Glucose Positron Emission Tomography/Computed Tomography Parameters in Patients with Resectable Lung Adenocarcinoma.Nuclear medicine and molecular imaging · 2018Article
- Partial volume correction for improved PET quantification inJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2018Article
- A semi-automatic technique to quantify complex tuberculous lung lesions onEJNMMI research · 2018Article
- Quantitative analysis of phantom studies ofEJNMMI physics · 2018Article
- Article
- Prognostic value of metabolic variables of [18F]FDG PET/CT in surgically resected stage I lung adenocarcinoma.Medicine · 2017Observational
- Verification of the tumor volume delineation method using a fixed threshold of peak standardized uptake value.Radiological physics and technology · 2017Article
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3 authors.
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Abstract
backgroundDefining tumour volume for treatment response and radiotherapy planning is challenging and prone to inter- and intra-observer variability. Various automated tumour delineation methods have been proposed in the literature, each having abilities and limitations. Therefore, there is a need to provide clinicians with practical information on delineation method selection.
methodsSix different automated positron emission tomography (PET) delineation methods were evaluated and compared using National Electrical Manufacturer Association image quality (NEMA IQ) phantom data and three in-house synthetic phantoms with clinically relevant lesion shapes including spheres with necrotic core and irregular shapes. The impact of different contrast ratios, emission counts, realisations and reconstruction algorithms on delineation performance was also studied using similarity index (SI) and percentage volume error (%VE) as performance measures.
resultsWith the NEMA IQ phantom, contrast thresholding (CT) performed best on average for all sphere sizes and parameter settings (SI = 0.83; %VE = 5.65% ± 24.34%). Adaptive thresholding at 40% (AT40) was the next best method and required no prior parameter tuning (SI = 0.78; %VE = 23.22% ± 70.83%). When using SUV harmonisation filtering prior to delineation (EQ.PET), AT40 remains the best method without prior parameter tuning (SI = 0.81; %VE = 11.39% ± 85.28%). For necrotic core spheres and irregular shapes of the synthetic phantoms, CT remained the best performing method (SI = 0.83; %VE = 26.31% ± 38.26% and SI = 0.62; %VE = 24.52% ± 46.89%, respectively). The second best method was fuzzy locally adaptive Bayesian (FLAB) (SI = 0.83; %VE = 29.51% ± 81.79%) for necrotic core sphere and AT40 (SI = 0.58; %VE = 25.11% ± 32.41%) for irregular shapes. When using EQ.PET prior to delineation, AT40 was the best performing method without prior parameter tuning for both necrotic core (SI = 0.83; %VE = 27.98% ± 59.58%) and complex shapes phantoms (SI = 0.61; %VE = 14.83% ± 49.39%).
conclusionsCT and AT40/AT50 are recommended for all lesion sizes and contrasts. Overall, considering background uptake information improves PET delineation accuracy. Applying EQ.PET prior to delineation improves accuracy and reduces coefficient of variation (CV) across different reconstructions and acquisitions.
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