Immunohistochemical Methods for Predicting Cell of Origin and Survival in Patients With Diffuse Large B-Cell Lymphoma Treated With Rituximab
Bibliographic record
Abstract
PURPOSE: Patients with diffuse large B-cell lymphoma (DLBCL) can be divided into prognostic groups based on the cell of origin of the tumor as determined by microarray analysis. Various immunohistochemical algorithms have been developed to replicate these microarray results and/or stratify patients according to survival. This study compares some of those algorithms and also proposes some modifications. PATIENTS AND METHODS: Two-hundred and sixty-two cases of de novo DLBCL treated with rituximab and cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP) or CHOP-like therapy were examined. RESULTS: The Choi algorithm and Hans algorithm had high concordance with the microarray results. Modifications of the Choi and Hans algorithms for ease of use still retained high concordance with the microarray results. Although the Nyman and Muris algorithms had high concordance with the microarray results, each had a low value for either sensitivity or specificity. The use of LMO2 alone showed the lowest concordance with the microarray results. A new algorithm (Tally) using a combination of antibodies, but without regard to the order of examination, showed the greatest concordance with microarray results. All of the algorithms divided patients into groups with significantly different overall and event-free survivals, but with different hazard ratios. With the exception of the Nyman algorithm, this survival prediction was independent of the International Prognostic Index. Although the Muris algorithm had prognostic significance, it misclassified a large number of cases with activated B-cell type DLBCL. CONCLUSION: The Tally algorithm showed the best concordance with the microarray data while maintaining prognostic significance and ease of use.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".