[18F]Fluorodeoxyglucose positron emission tomography and its prognostic value in lung cancer✩
Bibliographic record
Abstract
OBJECTIVE: Positron emission tomography (PET) is being increasingly used as an accurate and non-invasive modality in diagnosis, staging and post-therapy assessment in patients with lung cancer. In this study, we examine whether the uptake of [(18)F]fluorodeoxyglucose (FDG), a marker of increased glucose metabolism in neoplastic cells, is of prognostic value in patients with primary lung cancer. METHODS: We have retrospectively analyzed 77 patients (mean age, 63. 0 years; male/female ratio, 53:24) with primary lung cancers who underwent whole body and localized thoracic PET as part of their diagnostic and staging procedures prior to consideration of surgical resection. The standardized uptake value (SUV) of injected FDG for each primary lesion was correlated with tumour histology and the patient's clinical outcome. RESULTS: A SUV of 20 or greater was found to be of significant prognostic value. The chance of survival (with 95% confidence intervals (CI)) at 12 months post-surgery for the various SUV groups was as follows: 75.2% (59.6-85.5) for SUV<10; 67.5% (29.0-88.2) for SUV 10-<12; 63.6% (29.7-84.5) for SUV 12-<15; 66.7% (19.5-90.4) for SUV 15-<20; 16.7% (0.01-0.52) for SUV>20. A SUV of 20 or more is associated with a 4.66 times increase in hazard, compared with lower levels of SUV. We found no significant correlation between tumour histology and SUV. CONCLUSION: We have previously reported on the significant advantages of PET in the staging and surgical care of patients with lung cancer. The present study adds further support for an additional prognostic role for PET in the management of thoracic malignancy as determined by the amount of labelled-FDG taken up by the primary lesion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".