248. SYSTEMIC INFLAMMATORY MARKERS AS PROGNOSTIC FACTORS IN ESOPHAGEAL CANCER: A MEXICAN PERSPECTIVE
Bibliographic record
Abstract
Abstract Background Esophageal cancer (EC) represents the eighth most commonly diagnosed cancer, and sixth cause of cancer-related deaths worldwide. Several studies have demonstrated an association between systemic inflammation, anti-cancer immunity, and poor oncological outcomes in patients with EC. Biomarkers including neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR) may reflect the balance between pro-cancer inflammation and anti-cancer immune response and have prognostic implications in Mexican esophageal cancer patients. Methods A retrospective review of a prospectively collected database of all EC patients from 2011–2022 was performed. Baseline pre-treatment NLR, LMR and PLR was collected. The date of last follow-up or death was ascertained from medical records. ROC curves were used to calculate cut-off values for NLR and PLR, while the median-value was used for LMR. Serum albumin of <3.5 g/dL was considered malnutrition. Kaplan–Meier (univariate) and Cox regression (multivariate) were performed to evaluate survival against prognostic factors. Results 498 patients were included during the study period, 80.1% men and 19.9% women, median age 60 (±12.2) years old. Adenocarcinoma (49.8%) was the most common histological subtype, followed by squamous cell carcinoma (31.7%). Clinical stage included: I (3%), IIA-B (11.8%), III (37.6%), IVA (2.6%), and IVB (44.2%). Neoadjuvant chemoradiotherapy was the predominant initial therapy (50.4%), followed by chemotherapy (41.7%). ROC curves revealed the following cut-off value: 2.92 for NLR, 174 for IPL, and 3.5 for LMR. In the univariate analysis, four prognostic factors were significant for overall survival (NLR, PLR, LMR, albumin) (Table 1), while in the multivariate analysis only NLR and albumin remained independent factors for survival (Table 2). Conclusion In our population, low NLR and high albumin can be considered independent prognostic markers in patients with esophageal cancer. These common pretreatment biomarkers can serve to identify patients with good prognostic factors, contributing to an improved consent process and therapeutic decision making. Further research should focus on more accurate cut-off values for survival prognostication.
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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 | 0.001 |
| 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".