Role of neutrophil-to-lymphocyte ratio in prediction of Gleason score upgrading and disease upstaging in low-risk prostate cancer patients eligible for active surveillance
Bibliographic record
Abstract
INTRODUCTION: Active surveillance (AS) is an option for management of low-risk prostate cancer (PCa). However, grade and stage progression is an important consideration. Neutrophil-to-lymphocyte ratio (NLR) is a useful marker of cancer-related inflammation. In this study, we aimed to identify the roles of neutrophil count (NC), lymphocyte count (LC), and NLR to predict Gleason score (GS) upgrading, disease upstaging, and biochemical recurrence rates (BCR) in low-risk PCa patients. METHODS: We retrospectively evaluated data of 210 low-risk PCa patients eligible for AS, but who underwent radical prostatectomy. The roles of NC, LC, and NLR on the GS upgrading, disease upstaging, and BCR rates were investigated. Univariate and multivariate models were used to determine the effect of these parameters. RESULTS: There were 104 and 106 patients in the NLR <2.5 and NLR ≥2.5 groups, respectively. GS upgrading in the NLR ≥2.5 group was more common than in the NLR<2.5 group (p=0.04). The NLR ≥2.5 group had significantly higher GS (8-10; p=0.03). With regard to NLR, the groups were found to have similar rates of disease upstaging (9/104 in NLR <2.5 vs. 16/106 in NLR ≥2.5; p=0.200). BCR rates were also significantly different between groups (p=0.033). NC an LC were not found to be associated with GS upgrading, disease upstaging, or BCR. CONCLUSIONS: NLR is a predictor of GS upgrading and BCR, but not disease upstaging in patients with low-risk PCa. Furthermore, higher NLR was found to be associated with higher GS PCa. NLR is a cost-effective and easily accessible tool that can be used in the decision-making process for treatment of low-risk PCa cases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".