Neutrophil count is associated with survival in localized prostate cancer
Bibliographic record
Abstract
BACKGROUND: Increasing evidence suggests a close relationship between systemic inflammation and cancer development and progression. The neutrophil to lymphocyte ratio (NLR) has been shown to be an independent prognostic indicator in various advanced and localized cancers. We investigated the influence of markers of systemic inflammation such as leucocyte counts and metabolic co-morbidities on overall survival (OS) after radiotherapy for localized prostate cancer. METHODS: We conducted a retrospective study of patients with localized prostate cancer treated with definitive external beam radiotherapy or brachytherapy. Univariate and multivariate cox proportional hazards models were used to investigate the influence of the following factors on OS: age, neutrophil and lymphocyte counts, neutrophil-to-lymphocyte ratio (NLR), Cancer of the Prostate Risk Assessment (CAPRA) score as well as comorbidities associated with inflammation such as cardiac history, diabetes and use of a statin. A stepwise selection of variable based on the Akaike information criterion (AIC) was used for multivariate analysis. RESULTS: In total, 1772 pts were included; blood count data was available for 950 pts. Median age was 68 years (44-87). Actuarial 5 years OS and biochemical recurrence-free survival (BRFS) for the 1772 patients were 93% and 95%, respectively, with a median follow-up of 44 months (1-156). On univariate analysis, neutrophil count (p = 0.04), cardiac history (p = 0.008), age (p = 0.001) and CAPRA (p = 0.0002) were associated with OS. Lymphocytes, NLR and comorbidities other than cardiac history were not associated with mortality. On multivariate analysis, neutrophil count (HR = 1.18, 95 % CI: 1.017-1.37, p = 0.028), age (HR = 1.06, 95 % CI: 1.01-1.1, p = 0.008) and CAPRA (HR = 1.16, 95 % CI: 1.03-1.31, p = 0.015) were independent predictors of OS. CONCLUSION: Neutrophil count, as a possible marker of systemic inflammation, appear to be an independent prognostic factor for overall mortality in localized prostate cancer. A validation cohort is needed to corroborate these results.
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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 | 0.002 |
| 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) | 0.002 | 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".