Serum tumor biomarkers as a surrogate for radiographic assessment of non-small cell lung cancer.
Bibliographic record
Abstract
e21076 Background: Conventional tumor markers are positioned to serve as a useful adjunct in lung cancer management. However, most studies in this field have been specific to a particular disease stage or treatment regimen. Thus, the purpose of this research was to assess whether three minimally invasive, low-cost serum tumor markers (CEA, CA19-9, and CA-125) held associations with radiographic and clinical outcomes in non-small cell lung cancer (NSCLC) patients who received systemic therapy in a more comprehensive patient population. Methods: This was a single-center retrospective study of NSCLC patients treated between January 2016 and August 2020. Serum tumor markers were statistically analyzed for differences in patients who responded or progressed (RECIST 1.1 or iRECIST criteria), associations with demographic and clinical characteristics, and all-cause mortality in pre-defined populations. Disease response was assessed radiographically using RECIST 1.1 criteria. Results: From 533 NSCLC patients screened, 165 met inclusion criteria. Of these, 50.9% were male and 49.1% female. 69.7% had stage IV disease at baseline. The proportion of patients with an elevated CEA, CA-125, and CA19-9 at baseline were 58.8%, 50.9%, and 30.3%, respectively. A subset of 92 patients had paired tumor markers and radiographic scans, from which median (IQR) fold-change in tumor markers from nadir to progression was 2.13 (IQR 1.24 - 3.02; p < 0.001) for CEA (n = 47), 1.46 (IQR 1.13 - 2.18; p < 0.001) for CA19-9 (n = 46), and 1.53 (IQR 0.96 - 2.12; p < 0.001) for CA-125 (n = 47). Median (IQR) fold-change in tumor markers from baseline to radiographic response was 0.50 (IQR 0.27, 0.95; p < 0.001) for CEA (n = 39), 1.08 (IQR 0.74, 1.61; p = 0.99) for CA19-9 (n = 35), and 0.47 (IQR 0.18, 1.26; p = 0.008) for CA-125 (n = 35). Lastly, an elevated baseline CEA was not associated with a difference in overall survival (HR 1.05, 95% CI 0.63-1.74, p = 0.84) in patients with stage IV disease within the total population. Conclusions: Serum CEA, CA-125, and CA19-9 levels were significantly different than nadir in patients who progressed. CEA and CA-125 were significantly different than baseline in those who responded. These cost-effective tumor markers may serve as an important adjunct to clinical decision making. Analysis within a controlled clinical trial is warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".