Baseline Total Metabolic Tumor Volume is Prognostic for Refractoriness to Immunochemotherapy in DLBCL: Results From GOYA
Bibliographic record
Abstract
INTRODUCTION: A good response to initial therapy is key to maximizing survival in patients with diffuse large B-cell lymphoma (DLBCL), but patients with chemorefractory disease and early progression have poor outcomes. PATIENTS AND METHODS: Data from the GOYA study in patients with DLBCL who received first-line rituximab or obinutuzumab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP) were analyzed. Positron emission tomography/computed tomography (PET/CT)-derived characteristics associated with total metabolic tumor volume (TMTV) and clinical risk factors for primary chemorefractory disease and disease progression within 12 months (POD12) were explored. RESULTS: Of those patients fulfilling the criteria for analysis, 108/1126 (10%) were primary chemorefractory and 147/1106 (13%) had POD12. Primary chemorefractory and POD12 status were strongly associated with reduced overall survival. After multivariable analysis of clinical and imaging-based risk factors by backward elimination, only very high TMTV (quartile [Q] 1 vs. Q4 odds ratio [OR]: 0.45; P = .006) and serum albumin levels (low vs. normal OR of 1.86; P = .004) were associated with primary chemorefractoriness. After additionally accounting for BCL2/MYC translocation in a subset of patients, TMTV and BCL2/MYC double-hit status remained as significant predictors of primary chemorefractoriness (Q1 vs. Q4 OR: 0.32, P = .01 and double-hit vs. no-hit OR of 4.47, P = .02, respectively). Risk factors including very high TMTV, high sum of the product of the longest diameters (SPD), geographic region (Asia), short time since diagnosis, extranodal involvement and low serum albumin were retained for POD12. CONCLUSION: PET-derived TMTV has prognostic value in identifying patients at risk of early treatment failure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".