CELL OF ORIGIN COMBINED WITH CNS INTERNATIONAL PROGNOSTIC INDEX IMPROVES IDENTIFICATION OF DLBCL PATIENTS WITH HIGH CNS RELAPSE RISK AFTER INITIAL IMMUNOCHEMOTHERAPY
Bibliographic record
Abstract
Introduction: Central nervous system (CNS) relapse is a rare, and usually fatal, event in diffuse large B-cell lymphoma (DLBCL). Improved identification of patients (pts) with high CNS relapse risk is needed. The CNS International Prognostic Index (CNS IPI, Schmitz JCO 2016), a clinical prognostic model that identifies pts with higher CNS relapse risk, may be improved by integration of biomarkers. Methods: CNS relapse was analysed in DLBCL pts treated with first-line obinutuzumab (G) or rituximab (R) plus CHOP in the Phase III GOYA study (Vitolo Blood 2016; NCT01287741). Cell-of-origin (COO) was assessed using gene expression profiling (Nanostring Lymphoma Subtyping). Cumulative incidence and time to CNS relapse were estimated with Kaplan-Meier statistics. The impact of variables of interest (CNS IPI score, COO, study stratification factors – number of planned cycles, geographical region) on CNS relapse was assessed using a multivariate (MV) Cox regression model. Conclusions: CNS IPI score and ABC/unclassified COO subtypes were independent risk factors for CNS relapse in DLBCL in the GOYA study. Combining these factors improved prediction of CNS relapse vs CNS IPI alone and stratified pts into 3 risk groups, including a small but notable subgroup (8.0%) of pts with a very high risk of CNS relapse (2-yr risk: 15.2%). Keywords: diffuse large B-cell lymphoma (DLBCL); obinutuzumab; prognostic indices.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".