Prognostic value of FDG-PET scans at diagnosis in small cell lung cancer
Bibliographic record
Abstract
Background: Small cell lung carcinoma (SCLC) is one of the most aggressive solid tumors. The predictive and prognostic role of positron emission tomography (PET) scans in SCLC is under investigation and is yet to be determined. Method: We retrospectively analyzed the correlation between overall survival and 2-[18F]-fluoro-2-deoxy-d-glucose- PET (FDG-PET) results at diagnosis. We also introduced the concept of the total standardized uptake value (SUV) as a possible biomarker for the total burden disease. In addition, we proposed a new staging concept using PET scan based on whether tumor uptake is or is not limited to parenchymal hemithorax. Results: Between March 2004 and February 2009, 46 patients with histologically confirmed SCLC were included in the analysis. Thirty patients were found to have Eastern Cooperative Oncology Group (ECOG) Performance Status ≤ 2, and most (65%, n = 30) of them had limited stage disease using conventional clinical staging criteria. There was a fair correlation between PET results and conventional staging by CT scan (kappa = 0.330). Although there was a trend toward upstaging by PET, it was not predictive of survival. There was a direct correlation between total SUV and maximum SUV and overall survival (hazard ratio [HR] = 1.003 and 1.085, respectively). Conclusion: PET scan results at diagnosis could play an important role in the management of SCLC. Total SUV could represent a good biomarker for the disease burden in SCLC. Further prospective studies are needed to clarify the application of total SUV in SCLC.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".