Integration of cell of origin into the clinical CNS International Prognostic Index improves CNS relapse prediction in DLBCL
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Central nervous system (CNS) relapse carries a poor prognosis in diffuse large B-cell lymphoma (DLBCL). Integrating biomarkers into the CNS–International Prognostic Index (CNS-IPI) risk model may improve identification of patients at high risk for developing secondary CNS disease. CNS relapse was analyzed in 1418 DLBCL patients treated with obinutuzumab or rituximab plus cyclophosphamide, doxorubicin, vincristine, prednisone chemotherapy in the phase 3 GOYA study. Cell of origin (COO) was assessed using gene-expression profiling. BCL2 and MYC protein expression was analyzed by immunohistochemistry. The impact of CNS-IPI, COO, and BCL2/MYC dual-expression status on CNS relapse was assessed using a multivariate Cox regression model (data available in n = 1418, n = 933, and n = 688, respectively). High CNS-IPI score (hazard ratio [HR], 4.0; 95% confidence interval [CI], 1.3-12.3; P = .02) and activated B-cell‒like (ABC) (HR, 5.2; 95% CI, 2.1-12.9; P = .0004) or unclassified COO subtypes (HR, 4.2; 95% CI, 1.5-11.7; P = .006) were independently associated with CNS relapse. BCL2/MYC dual-expression status did not impact CNS relapse risk. Three risk subgroups were identified based on the presence of high CNS-IPI score and/or ABC/unclassified COO (CNS-IPI-C model): low risk (no risk factors, n = 450 [48.2%]), intermediate risk (1 factor, n = 408 [43.7%]), and high risk (both factors, n = 75 [8.0%]). Two-year CNS relapse rates were 0.5%, 4.4%, and 15.2% in the respective risk subgroups. Combining high CNS-IPI and ABC/unclassified COO improved CNS relapse prediction and identified a patient subgroup at high risk for developing CNS relapse. The study was registered at www.clinicaltrials.gov as #NCT01287741.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it