Clinical Significance of a Circulating Tumor Cell-based Classifier in Stage IB Lung Adenocarcinoma
Bibliographic record
Abstract
OBJECTIVE: To investigate the effectiveness of a CTC-based classifier in stratifying stage IB LUAD. SUMMARY OF BACKGROUND DATA: Stage IB LUADs have an approximately 70% 5-year survival rate. The clinical application of ACT is controversial due to inconsistent results in a series of trials and few useful guide biomarkers. Thus, there is a pressing need for robust biomarkers to stratify stage IB patients to define which group would most likely benefit from ACT. Methods: Two hundred twelve stage IB LUAD patients were enrolled and were divided into 3 independent cohorts. The aptamer-modified NanoVelcro system was used to enrich the CTCs. RESULTS: A cutoff of <4 or >4 CTCs as the optimal prognostic threshold for stage IB LUAD was generated to stratify the patients in a 70-patient cohort into low-risk and high-risk groups. Patients with ≥ 4 CTCs in the training cohort had shorter progression-free survival ( P < 0.0001) and overall survival ( P < 0.0001) than patients with <4 CTCs. CTC number remained the strongest predictor of progression-free survival and overall survival even in a multivariate analysis including other clinicopathological parameters. Furthermore, a nomogram based on the CTC count was developed to predict the 3-year and 5-year survival in the training cohort and performed well in the other 2 validation cohorts (C-index: 0.862, 0.853, and 0.877). CONCLUSION: The presence of >4 CTCs can define a high-risk subgroup, providing a new strategy to make optimal clinical decisions for stage IB LUAD.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".