Role of Serum Lymphocyte-derived Biomarkers in Nonmetastatic Muscle-invasive Bladder Cancer Patients Treated with Trimodal Therapy
Bibliographic record
Abstract
The role of serum lymphocyte-based biomarkers, such as the neutrophil-to-lymphocyte (NLR), lymphocyte-to-monocyte (LMR), and platelet-to-lymphocyte (PLR) ratios, was previously studied in patients with muscle-invasive bladder cancer (MIBC) treated with radical cystectomy but remains underexplored in patients treated with trimodal therapy (TMT). To analyze the impact of serum lymphocyte-based biomarkers on main oncological outcomes after TMT for MIBC. A retrospective study, including 176 patients treated with TMT for nonmetastatic MIBC (cT2–4/cN0–2) between 2001 and 2017 at a tertiary academic center, was conducted. TMT, consisting of initial maximal transurethral resection of the bladder tumor, followed by radiotherapy with concurrent chemotherapy. Clinicopathological characteristics, serum laboratory tests, and imaging reports were collected. NLR, LMR, and PLR were calculated before and at the end of TMT. Dynamic patterns of NLR, LMR, and PLR during TMT were studied. Multivariable regression models were performed to estimate the effect of these biomarkers on complete response (CR) to TMT and survival. The median age was 75 yr (interquartile range 66–82). Staging was cT2 in 156 (89%) and cN0 in 159 (90%) patients. A pretreatment NLR (pre-NLR) of ≥4.0 was independently associated with lower CR rates (odds ratio 0.32; p = 0.013). In addition, a pre-NLR of ≥4.0 was associated with worse cancer-specific survival (hazard ratio [HR] 1.88; p = 0.032) and overall survival (OS; HR 1.61; p = 0.033) together with other factors such as hydronephrosis, Eastern Cooperative Oncology Group performance status, and cT stage 3-4a. When both pre- and post-treatment variables were considered, an increase in NLR beyond 75% during TMT (HR 1.63; p = 0.035) was associated with worse OS. This study was limited by its retrospective design. A high pre-NLR value was independently associated with lower rates of CR and worse survival in MIBC patients undergoing TMT. Prospective validation is needed to implement NLR into clinical practice. In this study, we reported the oncological outcomes of patients with muscle-invasive bladder cancer treated with trimodal therapy. We found that the neutrophil-to-lymphocyte ratio, a cheap and available blood-derived biomarker, was associated with response to trimodal therapy and survival outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 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".