FDG uptake of pulmonary lesions in synchronous primary lung cancers and lung metastases
Bibliographic record
Abstract
In lung cancer patients, the distinction between synchronous primary lung cancer and intrapulmonary metastasis can be challenging. The intensity of FDG uptake in pulmonary lesions has been shown to be potentially useful in classifying synchronous lung cancer. The aim of this retrospective study is to investigate the effectiveness of FDG uptake in differentiating metastases from synchronous primary lesions in the setting of lung cancer. Consecutive patients with primary lung cancer with two or more malignant lung lesions referred for (18F)-FDG PET-CT imaging between 2010 and 2019 were reviewed and classified into synchronous and metastasis groups. Lesional maximum standardized uptake values (SUVmax), relative differences in SUVmax and SUVmax ratios were calculated and compared using receiver operating characteristic (ROC) curve analysis. Intra-group correlation in SUVmax between lesion pairs was examined using Pearson's and Spearman's correlation analysis. 94 patients were included for analysis, divided into synchronous (n = 62; 68 lesion pairs) and metastasis (n = 32; 33 lesion pairs) groups. The correlation of FDG uptake between lesions in the metastasis group was strong (r = 0.81). A significant difference in mean relative difference in SUVmax (synchronous: 0.50±0.23 metastasis: 0.34±0.17, p = 0.001) and mean SUVmax ratio (synchronous: 2.6 ± 1.7 metastasis: 1.7 ± 0.6, p < 0.001) was observed. ROC analysis revealed a fair AUC (0.71–0.72) for these parameters, with an associated sensitivity of 59 % and specificity of 82 % at optimal cut-off values. Differences in FDG uptake intensity among multiple synchronously presenting malignant nodules may be helpful to distinguish second primary lung tumours from metastatic spread.
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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 | 0.004 |
| 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 | 0.001 |
| 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".