Detection of circulating tumor cells as a prognostic factor in patients undergoing radical surgery for non‐small‐cell lung carcinoma: comparison of the efficacy of the CellSearch Assay™ and the isolation by size of epithelial tumor cell method
Bibliographic record
Abstract
Comparison of the efficacy of different enrichment methods for detection of circulating tumor cells (CTCs) before radical surgery is lacking in non-small-cell lung carcinoma (NSCLC) patients. Detection and enumeration of CTCs in 210 consecutive patients undergoing radical surgery for NSCLC were evaluated with the CellSearch Assay™ (CS), using the CellSearch Epithelial Cell Kit, and by the isolation by size of epithelial tumor (ISET) method, using double immunolabeling with anti-cytokeratin and anti-vimentin antibodies. CTCs were detected in 144 of 210 (69%) patients using CS and/or ISET and in 104 of 210 (50%) and 82 of 210 (39%) patients using ISET and CS, respectively. Using ISET, 23 of 210 (11%) patients had vimentin-positive cells with cytological criteria of malignancy. Disease-free survival (DFS) was worse for patients with CTCs compared to patients without CTCs detected by CS alone (p < 0.0001; log rank = 30.59) or by ISET alone (p < 0.0001; log rank = 33.07). The presence of CTCs detected by both CS and ISET correlated even better with shorter DFS at a univariate (p < 0.0001; log rank = 42.15) and multivariate level (HR, 1.235; 95% CI, 1.056-1.482; p < 0.001). CS and ISET are complementary methods for detection of CTCs in preoperative radical surgery for NSCLC. CTC detection in resectable NSCLC patients using CS and/or ISET could be a prognostic biomarker of great interest and may open up new avenues into improved therapeutic strategies for lung carcinoma 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".