Clinical Evaluation of Serum Tumor Markers in Patients With Advanced-Stage Non-Small Cell Lung Cancer Treated With Palliative Chemotherapy in China
Bibliographic record
Abstract
Aim: This study aims to analyze the prognostic value of 7 tumor makers and also investigate the response of palliative chemotherapy in advanced NSCLC patients with advanced disease. Methods: Medical records of 278 advanced NSCLC Chinese patients who received 6 cycles of palliative chemotherapy were retrospectively reviewed under ethical approval (JSCH2019K-011). Univariate and multivariate Cox regression analyses were performed using SPSS 24 to find the clinical value of these tumor markers and to identify the factors that were associated with progression-free survival (PFS), as well as the response to palliative chemotherapy. Results: In baseline characteristic, the high levels of CEA, CA-125, CA-199, AFP, NSE, CYFRA21-1, and CA15-3 were detected in 209 (75.18%), 139 (50.0%), 62 (22.30%), 18 (6.47 %), 155 (55.75%), 176 (63.30%) and 180 (64.74%) patients respectively. Univariate analysis revealed that patients with high vs. normal levels of all tumor markers had an increased risk of poor prognosis. In the multivariable Cox regression model, the patient with (high vs. normal) CYFRA21-1 levels (HR=1.454, P= 0.009) demonstrated an increased poor PFS. However, patients with (high vs. normal) CA19-9 levels (HR=0.524, P<0.0001) and NSE levels (HR=0.584, P<0.0001) presented a decreased risk of PFS. Also, patients receiving 3-drugs regimen had better PFS compared to those on 2-drugs regimen (P = 0.043). Conclusions: The high levels of CYFRA21-1 was correlated with a poor prognostic factor of PFS for Advanced NSCLC patients. However, the high levels of CA19-9 and NSE were associated with a better prognostic factor of PFS. Additionally, smoking habits and tumor status had a poor prognostic factor of PFS. Moreover, we found that antiangiogenic therapy has high efficacy with first-line platinum-based chemotherapy and longer PFS of NSCLC 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".