Impact of bone marrow biopsy on response assessment in immunochemotherapy-treated lymphoma patients in GALLIUM and GOYA
Bibliographic record
Abstract
The utility of posttreatment bone marrow biopsy (BMB) histology to confirm complete response (CR) in lymphoma clinical trials is in question. We retrospectively evaluated the impact of BMB on response assessment in immunochemotherapy-treated patients with previously untreated follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL) in the phase 3 Study of Obinutuzumab (RO5072759) Plus Chemotherapy in Comparison With Rituximab Plus Chemotherapy Followed by Obinutuzumab or Rituximab Maintenance in Patients With Untreated Advanced Indolent Non-Hodgkin's Lymphoma (GALLIUM; NCT01332968) and A Study of Obinutuzumab in Combination With CHOP Chemotherapy Versus Rituximab With CHOP in Participants With CD20-Positive Diffuse Large B-Cell Lymphoma (GOYA; NCT01287741) trials, respectively. Baseline BMB was performed in all patients, with repeat BMBs in patients with a CR by computed tomography (CT) at end of induction (EOI) and a positive BMB at baseline, to confirm response. Positron emission tomography imaging was also used in some patients to assess EOI response (Lugano 2014 criteria). Among patients with an EOI CR by CT in GALLIUM and GOYA, 2.8% and 4.1%, respectively, had a BMB-altered response. These results suggest that postinduction BMB histology has minimal impact on radiographically (CT)-defined responses in both FL and DLBCL patients. In GALLIUM and GOYA, respectively, 4.7% of FL patients and 7.1% of DLBCL patients had a repeat BMB result that altered response assessment when applying Lugano 2014 criteria, indicating that bone marrow evaluation appears to add little value to response assessment in FL; however, its evaluation may still have merit in DLBCL.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".