Comparison of FDG PET/MRI and FDG PET/CT in Pediatric Oncology in Terms of Anatomic Correlation of FDG-positive Lesions
Bibliographic record
Abstract
The aims of our study were to compare F-18 fluorodeoxyglucose (FDG) positron-emission tomography/magnetic resonance imaging (PET/MRI) and PET/computed tomography (CT) in pediatric oncology patients in terms of anatomic correlation of FDG-positive lesions, and also to compare diffusion-weighted imaging (DWI) with PET to assess the correlation between apparent diffusion coefficient (ADC) values and standardized uptake value (SUV). Sequential PET/CT and PET/MRI images and/or whole-body DWI and ADC mapping in 34 pediatric patients were retrospectively analyzed. FDG-positive lesions were visually scored for CT, T1-weighted, T2-weighted, and DWI images separately in terms of anatomic correlation of FDG-avid lesions. Correlation analysis was performed for SUV parameters and ADC values. Among 47 FDG-positive lesions identified concurrently on PET/CT and PET/MRI, 37 were positive on CT and 46 were positive on at least one MRI sequence (P=0.012). Among 32 FDG-positive lesions for which DWI were available, 31 could be clearly depicted on DWI, resulting in significant difference compared with CT alone in the detection of FDG-positive lesions. No correlation was found between ADC and SUV. FDG PET/MRI exhibits better performance than PET/CT in terms of anatomic correlation of FDG-avid lesions. Therefore, PET/MRI may be more advantageous than PET/CT, not only due to reduced ionizing radiation dose but also for a better depiction of FDG-avid lesions in pediatric PET imaging.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".