Predictive ability of blood neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio in gastrointestinal stromal tumors (GIST).
Bibliographic record
Abstract
10556 Background: The immune response, specifically the neutrophil-to-lymphocyte ratio (NLR), has recently been shown to be prognostic in untreated, primary GIST. Moreover, the platelet-to-lymphocyte ratio (PLR) has been shown to predict outcome in several gastrointestinal tumors; however, no studies have examined its predictive ability in GIST. This study serves to evaluate the prognostic utility of NLR and PLR in patients undergoing surgical resection for GIST. Methods: All patients who underwent surgical resection for primary, localized GIST from 2001 to 2011 were identified from a prospectively maintained database. Demographic profile, clinicopathologic variables, laboratory values and recurrence rates were analyzed. NLR and PLR were both assessed pre-operatively. Survival curves were calculated by the Kaplan-Meier product limit method and compared by the log-rank test. Univariate and multivariate Cox proportional hazard regression models were used to identify associations with outcome variables. High/low NLR and PLR were determined using optimization techniques, and defined as ≥2.04/<2.04 and ≥245/<245, respectively. Results: 93 patients were included. On univariate analysis, PLR was associated with recurrence-free survival (RFS) (HR .271, 95% CI .078 – .938, p = .039). PLR was also associated with RFS on multivariate analysis (HR .048, 95% CI .003 – .884, p = .041). Patients with low PLR had 2- and 5-year RFS of 94 and 84%, compared with 57 and 57% in those with high PLR. RFS in patients with mitotic rate ≤5/50 HPF with low PLR was significantly longer than in those with high PLR (p = .007). Similarly, RFS in patients with tumor size <5 cm with low PLR was significantly longer than in those with high PLR (p = .004). NLR was not associated with either RFS (p = .226) or overall survival (p = .994). Conclusions: Although there was no association between NLR and survival, a low PLR was associated with improved RFS, especially in tumors with mitotic rate ≤5/50 HPF or <5 cm. The independent prognostic ability of PLR to predict disease recurrence in these patients suggests that it may play a role in risk stratification schemes when deciding which patents will benefit from adjuvant therapy.
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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 | 0.000 |
| 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) | 0.001 | 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".