Germinal Center B-Cell-Like versus Non-Germinal Center B-Cell-Like as Important Prognostic Factor for Localized Nodal DLBCL
Bibliographic record
Abstract
Diffuse large B-cell lymphoma (DLBCL) is the most common form of non-Hodgkin's lymphoma. Although many investigations have been performed on the prognostic factors of DLBCL, no reports have focused on localized nodal DLBCL. We examined the prognostic significance of 39 Japanese patients with localized nodal DLBCL with special reference to the germinal center B-cell-like (GCB) versus non-germinal center B-cell-like (NGCB) types. The median age was 65 years with 23 males and 16 females. Using Hans algorithm of immunohistochemistry, 18 patients (46%) exhibited GCB type and 21 (54%) exhibited NGCB type. Twenty-nine patients (74%) presented with disease in the neck (neck group) and 10 (26%) had disease in non-neck regions (non-neck group). Comparing Hans, Choi, and Muris algorithms, patients with GCB type showed statistically significant progression-free survival (PFS) only with Hans algorithm (P = 0.022, P = 0.100, and P = 0.130, respectively). Patient survival analyses revealed that GCB-type patients by Hans algorithm had a better PFS (P = 0.012), and neck-group patients had better PFS and overall survival (OS) (P = 0.018 and P = 0.012, respectively). Univariate analysis revealed that only neck vs. non-neck exhibited a significant difference in terms of OS (P = 0.026). Multivariate analysis revealed that GCB type by Hans algorithm and neck vs. non-neck were significantly different in terms of PFS (P = 0.025 and P = 0.033, respectively). Therefore, the subclassifications of GCB type vs. NGCB type and neck vs. non-neck are important predictive prognostic factors in localized nodal 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 | 0.001 | 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) | 0.001 | 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".