Late Relapses in Patients With Diffuse Large B-Cell Lymphoma Treated With Immunochemotherapy
Bibliographic record
Abstract
PURPOSE In patients with diffuse large B-cell lymphoma (DLBCL), most relapses occur within the first 2 years of diagnosis. We sought to define the rate and outcome of late relapses that occurred after achieving event-free survival at 24 months (EFS24). METHODS We prospectively followed 1,324 patients with newly diagnosed DLBCL from 2002 to 2015 and treated with immunochemotherapy. Cumulative incidences of late DLBCL and indolent lymphoma relapses were analyzed as competing events. Postrelapse survival was defined as time from first relapse to death from any cause. RESULTS In 847 patients who achieved EFS24, the cumulative incidence of late relapse was 6.9% at 3 years, 9.3% at 5 years, and 10.3% at 8 years after EFS24. The incidence of DLBCL relapse was similar in patients with DLBCL alone at diagnosis (6.3% at 5 years), compared with patients with concurrent indolent lymphoma at diagnosis (5.2%; P = .46). However, the rate of indolent lymphoma relapse was higher in patients with concurrent indolent lymphoma (7.4% v 2.1% at 5 years; P < .01). In patients with DLBCL alone, the rate of DLBCL relapse was similar in the germinal center B-cell–like (GCB) (4.1% at 5 years) and non-GCB (4.0%; P = .71) subtypes, whereas the rate of indolent lymphoma relapse was higher in patients with the GCB subtype (3.9% v 0.0% at 5 years; P = .02). Postrelapse survival was inferior for patients who relapsed with DLBCL than for those who relapsed with indolent lymphoma (median 29.9 months v unreached; P < .01). CONCLUSION Patients with DLBCL with a concurrent indolent lymphoma and those with the GCB subtype had a higher rate of late relapse, owing to increased relapses with indolent lymphoma. Patients who relapsed with DLBCL had a worse prognosis than those who relapsed with indolent lymphoma.
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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 | 0.000 | 0.002 |
| 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 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".