The Impact of Scoring Method on Accuracy and Reproducibility of Hans Cell-of-Origin Prediction in Excisional Biopsies of Diffuse Large B-Cell Lymphoma, Not Otherwise Specified
Bibliographic record
Abstract
CONTEXT.—: Aided by tissue microarray (TMA) technology, several RNA-correlated immunohistochemistry-based algorithms have been developed for cell-of-origin (COO) prediction in diffuse large B-cell lymphoma, not otherwise specified (DLBCL-NOS). However, there is currently no empirical evidence to guide the optimal application of these algorithms to whole tissue sections (WTSs). OBJECTIVE.—: To assess the impact of various scoring methods on the accuracy and reproducibility of the popular Hans algorithm. DESIGN.—: We compared 3 different WTS-based scoring methods, designated as global, selective, and hotspot scoring, to a matched TMA evaluation and gold standard RNA analysis (Lymph2Cx; germinal center B cell n = 64; activated B cell/unclassified n = 68) using a representative series of 132 excisional biopsies of de novo DLBCL-NOS. Positivity scores (10% increments) were submitted by 3 expert lymphoma pathologists, with 30% or more defining positivity. RESULTS.—: Sixty-eight of the 132 cases of DLBCL-NOS (52%) exhibited variation in Hans immunohistochemistry marker phenotype as a consequence of scoring method and/or interscorer discordance. Although this led to changes in Hans COO assignment in 27 of 132 cases (20%), none of the WTS-based scoring methods were statistically inferior to one another in terms of raw accuracy. Hotspot scoring yielded the lowest proportion of borderline scores (20%-40% range) for BCL6 transcription repressor (BCL6) and IRF4 transcription factor (MUM1) but negatively impacted the balance between sensitivity and specificity for these markers. Selective scoring was associated with significantly worse interscorer concordance compared to TMA evaluation, which it was designed to replicate. CONCLUSIONS.—: Overall, our data favor the use of global scoring for its noninferior accuracy, solid interscorer concordance, nonnegative influence on individual Hans markers, and current widespread 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 | 0.026 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".