Clinicopathological Predictors of the Presence of Blood Circulating Tumor DNA in Early-Stage Non–Small Cell Lung Cancers
Bibliographic record
Abstract
The implementation of lung cancer screening programs across the world has drawn considerable attention to improving early-stage lung cancer detection and prognostication. Several blood-based assays detecting circulating tumor DNA (ctDNA) recently emerged as noninvasive methods to detect malignancies. However, their limited sensitivity and predictive value remain a hurdle to their clinical use. We aimed to evaluate the association between clinicopathological parameters and presurgical ctDNA detection in clinical stage I non-small cell lung cancer patients to further understand ctDNA shedding biology. The cohort included 180 adenocarcinomas (LUAD) and 80 squamous cell carcinomas (LUSC) stage I patients who underwent lung cancer resection. Patients' clinical and pathological features were collected. A multicancer early-detection test (GRAIL LLC) was used to detect ctDNA using targeted methylation patterns. The association between the cell-free DNA tumor methylated fraction (TMeF) and the clinicopathological predictors was evaluated using univariate and multivariate modeling. LUSC was associated with a higher TMeF than LUAD. Pathological stage, tumor grade, and tumor volume were key determinants of ctDNA detection in both LUSC and LUAD. In LUAD, ctDNA detection also correlated with histologic pattern composition, necrosis, acute inflammation, and, to a lesser degree, spread through alveolar spaces and lymphovascular invasion. Based on our results, we propose classification methods for both LUAD (using histologic pattern composition) and LUSC (using tumor grade and pathological stage) to identify patients likely to have high ctDNA levels. These results confirm previous findings and suggest that previously unidentified factors, including histologic pattern composition and acute inflammation, influence ctDNA levels. These results will help in understanding the ctDNA shedding process and may allow identification of patients eligible for ctDNA detection-based follow-up.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".