Lack of reproducibility of histopathological features in <i>MYC</i>‐rearranged large B cell lymphoma using digital whole slide images: a study from the Lunenburg lymphoma biomarker consortium
Bibliographic record
Abstract
AIMS: Subclassification of large B cell lymphoma (LBCL) is challenging due to the overlap in histopathological, immunophenotypical and genetic data. In particular, the criteria to separate diffuse large B cell lymphoma (DLBCL) and high-grade B cell lymphoma (HGBL) are difficult to apply in practice. The Lunenburg Lymphoma Biomarker Consortium previously reported a cohort of over 5000 LBCL that included fluorescence in-situ hybridisation (FISH) data. This cohort contained 209 cases with MYC rearrangement that were available for a validation study by a panel of eight expert haematopathologists of how various histopathological features are used. METHODS AND RESULTS: Digital whole slide images of haematoxylin and eosin-stained sections allowed the pathologists to visually score cases independently as well as participate in virtual joint review conferences. Standardised consensus guidelines were formulated for scoring histopathological features and included overall architecture/growth pattern, presence or absence of a starry-sky pattern, cell size, nuclear pleomorphism, nucleolar prominence and a range of cytological characteristics. Despite the use of consensus guidelines, the results show a high degree of discordance among the eight expert pathologists. Approximately 50% of the cases lacked a majority score, and this discordance spanned all six histopathological features. Moreover, none of the histological variables aided in prediction of MYC single versus double/triple-hit or immunoglobulin-partner FISH-based designations or clinical outcome measures. CONCLUSIONS: Our findings indicate that there are no specific conventional morphological parameters that help to subclassify MYC-rearranged LBCL or select cases for FISH analysis, and that incorporation of FISH data is essential for accurate classification and prognostication.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".