Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios as predictive and prognostic markers in patients with locally advanced rectal cancer treated with neoadjuvant chemoradiation
Bibliographic record
Abstract
BACKGROUND: A standard therapy for locally advanced rectal cancer (LARC) includes fluoropyrimidine (FP)-based neoadjuvant chemoradiation (nCRT). Previous studies have inconsistently demonstrated that baseline neutrophil- and platelet-to-lymphocyte ratios (NLR and PLR) are predictive of response to nCRT or prognostic of outcomes in LARC. METHODS: We reviewed patients with LARC undergoing nCRT followed by surgery from 2005 to 2013 across 8 Canadian cancer centres. Outcome measures of interest were pathological complete response (pCR), disease-free survival (DFS) and overall survival (OS). Logistic regression and Cox proportional hazard models were used to assess for associations between baseline hematologic variables and outcomes. RESULTS: Of 1527 identified patients, 1237 (81%) were included in the DFS/OS analysis. Median age was 62 (range 23-88), 69% were male, and 80% had performance status (PS) 0-1. Twenty-six percent had elevated NLR (≥ 4), and 66% had elevated PLR (≥ 150). Ninety-seven percent of patients received FP-based nCRT, with 96% receiving ≥44 Gy. 81% completed neoadjuvant chemotherapy and 95% completed neoadjuvant radiotherapy, with a pCR rate of 18%. After a median follow-up time of 71 months, 8% developed local recurrence, 22% developed distant recurrence and 24% died. 5-year DFS and OS were 69% (95% CI 66-72%) and 79% (95% CI 77-82%), respectively. In multivariate analyses, elevated baseline NLR and PLR were neither prognostic for DFS and OS nor predictive of pCR. CONCLUSIONS: NLR and PLR were not found to be independently prognostic for DFS or OS and did not predict for pCR in patients with LARC undergoing nCRT followed by surgery.
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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 | 0.003 |
| 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) | 0.001 | 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".