FDG PET/CT in Initial Staging of Adult Soft-Tissue Sarcoma
Bibliographic record
Abstract
Soft-tissue sarcomas spread predominantly to the lung and it is unclear how often FDG-PET scans will detect metastases not already obvious by chest CT scan or clinical examination. Adult limb and body wall soft-tissue sarcoma cases were identified retrospectively. Ewing's sarcoma, rhabdomyosarcoma, GIST, desmoid tumors, visceral tumors, bone tumors, and retroperitoneal sarcomas were excluded as were patients imaged for followup, response assessment, or recurrence. All patients had a diagnostic chest CT scan. 109 patients met these criteria, 87% of which had intermediate or high-grade tumors. The most common pathological diagnoses were leiomyosarcoma (17%), liposarcoma (17%), and undifferentiated or pleomorphic sarcoma (16%). 98% of previously unresected primary tumors were FDG avid. PET scans were negative for distant disease in 91/109 cases. The negative predictive value was 89%. Fourteen PET scans were positive. Of these, 6 patients were already known to have metastases, 3 were false positives, and 5 represented new findings of metastasis (positive predictive value 79%). In total, 5 patients were upstaged by FDG-PET (4.5%). Although PET scans may be of use in specific circumstances, routine use of FDG PET imaging as part of the initial staging of soft-tissue sarcomas was unlikely to alter management in our series.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".