Utility of total body FDG PET/CT imaging in the initial staging of soft-tissue sarcoma
Bibliographic record
Abstract
10531 Background: Soft-tissue sarcoma spreads predominantly to the lung. It is currently unclear how often PET scan will detect metastases not already obvious by chest imaging or clinical examination. Methods: Soft-tissue sarcoma cases were identified retrospectively. Ewing's sarcoma, rhabdomyosarcoma and GIST tumors were excluded as were patients imaged for follow-up, response assessment or recurrence. Patients all had had a diagnostic chest CT scan as part of their staging. Directed studies were requested to follow-up on abnormal findings in the clinical history or physical examination. All charts and pre-treatment imaging were reviewed retrospectively. Results: From 2004 to 2008, 75 patients met the criteria for this review. These 75 patients had total body FDG-PET/CT imaging on routine initial staging of their sarcoma. The median age was 51. In 21% of cases, the primary tumor had been removed by excisional biopsy or unplanned excision prior to staging. 97% of the previously unresected primary tumors were FDG avid (SUV ≥ 2). 81% of tumors were high-grade (FNCLCC Grade 2–3). The primary tumor was stage T2b in 68% of cases. The most common primary site was the lower extremity (55%). The most common pathological diagnoses were: leiomyosarcoma (21%), liposarcoma (19%) and synovial sarcoma (17%). At the end of staging, 17% of patients were considered to have metastatic disease. PET scans were negative for distant disease in 64/75 cases. Seven of these 64 cases had metastatic disease on chest CT (negative predictive value 89%). 8 PET scans were positive - of these, 4 patients were already known to have metastases, 2 were pathologically proven false positives and 1 was a new finding of a pulmonary metastasis (sensitivity 46%). Three patients had indeterminate PET scans (subsequently none developed metastatic disease). Two incidental benign parotid tumors were found. In total, only 1 patient was upstaged by the PET imaging (1.3%). In addition, PET did not alter management of patients already know to have M1 disease (no new organ sites identified). Conclusions: Although PET scans may be of use in specific circumstances, routine use of FDG PET imaging for detection of metastatic disease as part of the initial staging of soft-tissue sarcoma adds little to chest CT scanning and is unlikely to alter management. No significant financial relationships to disclose.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".