Pretreatment Neutrophil-to-Lymphocyte Ratio as a Predictive Marker of Response to Atezolizumab Plus Bevacizumab for Hepatocellular Carcinoma
Bibliographic record
Abstract
Background: Combination therapy with anti-programmed death-ligand 1 monoclonal antibody atezolizumab plus anti-vascular endothelial growth factor agent bevacizumab (Atezo/Bev) was approved in 2020 as a first-line treatment for unresectable hepatocellular carcinoma (HCC). Atezo/Bev therapy is relatively well tolerated; however, factors that can predict its response have not yet been reported. Thus, we aimed to investigate whether the pretreatment neutrophil-to-lymphocyte ratio (NLR) could predict the therapeutic response in patients with HCC treated with Atezo/Bev therapy. Methods: We analyzed the course of 40 patients with HCC who received Atezo/Bev therapy at our hospital and attempted to identify pretreatment factors that could predict response by comparing those who achieved disease control with those who did not. Results: The pretreatment NLR value in patients who achieved disease control was significantly lower than that in patients with disease progression (2.47 vs. 4.48, p = 0.013). Using the optimal NLR cut-off value for predicting response (3.21) determined by receiver operating characteristic curve analysis, patients with NLR ≤ 3.21 had significantly better progression-free survival than those with NLR > 3.21 (p < 0.0001), although there were no significant differences in liver function or tumor-related background factors between the two groups. Conclusions: The pretreatment NLR value may be a useful predictor of response to Atezo/Bev therapy for HCC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".