A Novel Composite Biomarker Panel For Detection Of Early Stage Non-small Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: To investigate a novel composite methodology of using targeted serum microRNAs (micro ribonucleic acid; miRNA) and urine metabolites for the accurate detection of early stage non-small cell lung cancer (NSCLC). METHODS: Consecutively consenting NSCLC patients and matched control subjects were recruited to provide samples of serum for miRNA and/or urine for metabolite analyses. Serum miRNA levels were measured using quantitative real-time reverse-transcription with exogenous control, and the comparative delta cycle threshold (CT) method was used to calculate relative miRNA expression of two targeted miRNAs (miR-21 and miR-223). The concentrations of six targeted urinary metabolites in patients and healthy controls were measured using proton nuclear magnetic resonance (1H NMR) spectroscopy. A composite methodology of using the 35 accruals with both serum and urine biomarkers was then established with binary logistic regression, receiver operating characteristic (ROC) models with or without artificial intelligence (AI). RESULTS: The ROC analysis of miRNA expression yielded a sensitivity of 96.4% and a specificity of 88.2% for the detection of early stage NSCLC, with area under the curve (AUC) = 0.91 (CI 95%: 0.80-1.0). Relative urinary concentrations of 4-methoxyphenylacetic acid (4MPLA) were significantly different between NSCLC and healthy control (p=0.008). The ROC analysis of 4MPLA yielded a sensitivity of 82.1% and a specificity of 88.2%, with AUC = 0.85. The composite process combining miRNA and metabolite expression demonstrated a sensitivity and specificity of nearly 100% and AUC=1. CONCLUSIONS: A highly specific, sensitive and non-invasive detection method for NSCLC was developed. Pending validation, this can potentially improve the early detection and, hence, the treatment and survival outcomes of patients.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".