Clinical Validation of Metabolite Markers for Early Lung Cancer Detection
Bibliographic record
Abstract
Abstract Non-small cell lung cancer (NSCLC), comprising 85% of lung cancers, is a leading cause of cancer mortality. Early detection enhances survival, but current screening methods are limited. This retrospective study used targeted mass spectrometry-based metabolomics on 680 plasma samples from NSCLC patients and controls (discovery cohort) and 216 samples (validation cohort). Logistic regression models with a subset of ten metabolites achieved over 90% area under the ROC curve (AUROC) for distinguishing patients from controls, including early-stage disease. Incorporating smoking history improved model performance. In the discovery cohort, AUROCs were 93.6% (all stages), 93.7% (Stage I and II), and 93.9% (Stage I). Validation confirmed the high sensitivity and specificity of the models. This study demonstrates that metabolomic biomarkers provide a minimally invasive, sensitive, and specific tool for early NSCLC detection, potentially improving screening and patient outcomes. Future studies should validate these biomarkers in diverse populations. Statement of significance This study identifies plasma metabolite biomarkers that enable sensitive and specific early detection of NSCLC using minimally invasive blood sampling. Achieving over 90% area under the ROC curve for early-stage patients, the findings promise to improve lung cancer screening methods and enhance early interventions and patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".