Diagnostic significance of the novel biomarker combination for early-stage non-small cell lung cancer detection: results of the blind clinical study
Bibliographic record
Abstract
Background: NSCLC can be cured in up to 65% of cases if detected early. However, most of the lung cancer (LC) cases are diagnosed at an advanced stage. Objective: The assessment of various tumor markers in retrospective double-blind clinical study and their possible combinations for detection of early-staged non-small cell lung cancer (NSCLC); evaluation of the best TM panel as a pre-screening tool for LC before Low-Dose CT scan; the development of the protocol for future prospective clinical study. Methods: A double-blind clinical study was conducted on 304 clinically verified patients, including 141 NSCLC, 133 healthy volunteers and 30 patients with COPD. Quantitative measurement of various TM was carried out using commercial immunoassays. Results: Unlike other tumor markers, which are expressed proportionally to the tumor growth, CA-62 demonstrated the highest values at Stage I and II of NSCLC. The use of CA-62 for early-staged NSCLC achieves 92% sensitivity at 95% specificity (AUC = 0.973). The diagnostic value of the best TM signature (CA-62, CEA and CYFRA 21-1): 100% Specificity, 90% Sensitivity, and 94% test accuracy, AUC=0.990. Conclusions: The results of the study demonstrated that the TM combination allows increasing the Specificity for patients with indeterminate pulmonary nodules detected by CT scans and improves the accuracy of differential diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".