ArticleEJNMMI physics2024
Insertion of synthetic lesions on patient data: a method for evaluating clinical performance differences between PET systems.
Article in EJNMMI physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03956459 (Evaluation of PET-CT Scanner Performances to Detect Infra-centimetric Lesions in Patients With Cancer), which is not on this map. Cited by 2 papers.
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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.
Evaluation of PET-CT Scanner Performances to Detect Infra-centimetric Lesions in Patients With Cancer
Who cites it
2 citing papers in PubMed.
- Deep learning-enhanced digital-BGO versus TOF PET/CT: comparative assessment of detection, quantitation, and overall image quality.EJNMMI physics · 2025Article
- Characterization of artificial intelligence performance for lesion detection using synthetic lesions in PET imaging.Medical physics · 2025Article
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13 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundPerformance assessment of positron emission tomography (PET) scanners is crucial to guide clinical practice with efficiency. We have already introduced and experimentally evaluated a simulation method allowing the creation of a controlled ground truth for system performance assessment. In the current study, the goal was to validate the method using patient data and demonstrate its relevance to assess PET performances accuracy in clinical conditions.
methodsTwenty-four patients were recruited and sorted into two groups according to their body mass index (BMI). They were administered with a single dose of 2 MBq/kg
resultsIn the BMI ≤ 25 group, the relative true positive rate (RTPR) for synthetic and natural lesions was 1.79 and 1.83, respectively. In the BMI > 25 group, the RTPR for synthetic and natural lesions was 2.03 and 2.27, respectively. For each BMI group, the detection rate using ISL was consistent to our estimate and with the detection rate measured on natural lesions. IOA above 80% was verified for any scenario. SUV metrics showed a good agreement between synthetic and natural lesions.
conclusionsISL proved relevant to evaluate performance differences between PET scanners. Using these synthetically modified clinical images, we can produce a controlled ground truth in a realistic anatomical model and exploit the potential of PET scanner for clinical purposes.
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