The Utility of 18FDG PET/CT Versus Bone Scan for Identification of Bone Metastases in a Pediatric Sarcoma Population and a Review of the Literature
Bibliographic record
Abstract
BACKGROUND: Outcome of patients with osteosarcoma (OS) and Ewing sarcoma (EWS) is dependent on presence of metastases. Imaging guidelines for OS and EWS include radiographs, computed tomography (CT), and magnetic resonance imaging for primary tumor evaluation and CT chest and bone scintigraphy (BS) for metastatic detection. 18Fluorodeoxyglucose (18FDG) positron emission tomography (PET)/CT has become more common for disease evaluation, yet there is no consensus for its use in this population. OBJECTIVE: We aimed to compare identification of osseous metastases using BS versus 18FDG PET/CT in our patient population. We hypothesized that 18FDG PET/CT is more likely to detect osseous metastases both at diagnosis and relapse. MATERIALS AND METHODS: We performed retrospective chart reviews of pediatric sarcoma patients treated at our institution from 2008 to 2019. Paired BS and 18FDG PET/CT scans were reviewed. Review of the literature was also performed. RESULTS: Thirty-three patients had paired BS and 18FDG PET/CT during diagnosis or treatment. Fifteen patients had distant osseous metastases. In the OS cohort, 8/16 patients had osseous metastases; 100% of these patients were detected on 18FDG PET/CT and 75% on BS. Thirty-one bony lesions were seen on imaging in OS patients; 100% of these were identified on 18FDG PET/CT but only 29% on BS. In the EWS cohort, 6/15 patients had osseous metastases; 100% of these patients were detected on 18FDG PET/CT and 50% on BS. Eighteen bony lesions were seen on imaging in EWS patients; 94% of these were identified on 18FDG PET/CT, but only 28% on BS. CONCLUSION: For patients in our institution with OS or EWS, osseous metastases were more likely detected using 18FDG PET/CT.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".