Nomogram Based on Monocyte-to-Lymphocyte Ratio to Predict Survival of Unresectable Esophageal Squamous Cell Carcinoma Who Receive First-Line PD-1/PD-L1 Inhibitors Combined with Chemotherapy
Bibliographic record
Abstract
Background. Chemoimmunotherapy has become the first-line treatment for unresectable esophageal squamous cell carcinoma (ESCC). Still, reliable biomarkers to identify patients who could benefit from this combined therapy remain uncertain. This study focused on elucidating the predictive significance of the monocyte-to-lymphocyte ratio (MLR) and establishing the prognostic nomogram for unresectable ESCC treated with chemoimmunotherapy. Methods. Data of clinical features, peripheral blood parameters, and treatment records were collected in unresectable ESCC patients who received PD-1/PD-L1 inhibitors plus chemotherapy as the first-line treatment from September 2017 to August 2021. The nomogram based on MLR and clinical parameters for predicting the overall survival (OS) was developed and validated. Results. Out of 81 patients enrolled, patients with a lower MLR had significantly longer progression-free survival (PFS) and OS than patients with a higher pretreatment MLR (p = 0.0067; p = 0.00069). The OS nomogram integrating MLR, performance status (PS) score, and body mass index (BMI) achieved a C-index of 0.770 (95%CI 0.645–0.896). The area under the ROC curve (AUC) value of the nomogram predicting 12-, 18-, and 24-month OS rates were 0.855, 0.792, and 0.744, respectively, which were higher than the clinical TNM staging system or the MLR. Stratified by the nomogram-generated scores, three risk groups (low, moderate, and high) in survival curves manifested a distinct difference (p < 0.0001). Conclusion. MLR emerged as an independent predictive factor for PFS and OS in treatment-naive unresectable ESCC patients treated with chemoimmunotherapy. The constructed nomogram of MLR and clinical parameters was a reliable model for prognostic estimation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".