Comparison of primary tumor maximal standardized uptake value (SUV<sub>max</sub>) on preoperative [18F]fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) and histological subtype in patients with non-small cell lung cancer (NSCLC)
Bibliographic record
Abstract
7571 Background: The routine preoperative use of PET/CT in patients with potentially resectable NSCLC is rapidly expanding. The SUVmax is a semiquantitative measure of metabolic activity that can distinguish benign from malignant tissue but published data are conflicting regarding its ability to discriminate between major histological subtypes. Methods: Pathology and PET/CT reports of 39 NSCLC patients who underwent a preoperative scan and curative resection at the McGill University Health Centre were reviewed. Only patients with Adenocarcinoma (AC), Squamous Cell carcinoma (SC), or Large Cell carcinoma (LC), and definitive pathological staging, were included. The SUVmax values for each histological subtype, along with primary tumor sizes, were compared using F test and t-test analyses. Results: The 15 patients with SC and 5 with LC histology were found to have significantly greater preoperative SUVmax values than the 19 patients with AC (mean 12.7 and 17.2 vs. 9.4, respectively, P < 0.05), despite the fact that no significant differences in tumor size were observed between histological subtypes. Patients with LC histology displayed higher SUVmax values than patients with SC histology, but this was not found to be significant (P = 0.057).Conclusions: These data suggest that SC pulmonary tumors have significantly greater uptake on PET/CT than AC tumors. This finding may be helpful in the future when sufficient tissue cannot be obtained for pathological diagnosis or to identify the predominant pathology of mixed tumors. Larger studies are required to confirm our results. [Table: see text] 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".