Is Sclerosing Angiomatoid Nodular Transformation an IgG4-associated Sclerosing Disease?
Bibliographic record
Abstract
Plain Language Summary Hans’ algorithm (HA) is the most frequently used surrogate biomarker scheme for subtyping Diffuse large B-cell lymphoma (DLBCL) by the cell-of-origin (COO) into GCB and non-GCB subtypes. The originally published positive and negative predictive value (PPV and NPV) against gene expression profiling (GEP) subtypes were less than perfect and were not fully reproducible in published literature. Furthermore, little is known about how the HA performs in clinical practice. The Canadian Association of Pathologists National Standard Committee for High Complexity Testing (CAP-ACP NSCHCT) initiated a Canada-wide project to assess the current diagnostic accuracy of the HA in clinical practice, harmonize the analytical phase of the IHC assays used for HA, and to optimize its overall diagnostic sensitivity and specificity against GEP subtypes. We divided a DLBCL cohort (n=96), where COO was defined by GEP (Lymph2CX, NanoString technologies) into training (TC, N=45) and validation cohort (VC, N=51) and tissue microarray (TMA)-TC and TMA-VC were constructed. Selected major Canadian laboratories applied their routine CD10, Bcl-6, and MUM1 IHC protocols and sent stained slides to the reference laboratory for review. The IHC protocols from laboratories were designated as “weak” (1/10), “moderate” (4/10), and “strong” (5/10) based on their overall analytical sensitivity. The results of the central review HA readouts were compared with GEP results. Furthermore, in TC, the original HA readout was modified to adjust the cutoff to overall IHC protocol sensitivity. The new readout criteria were also assessed in the VC set. The original HA readout showed good results against GEP for low sensitivity protocol only. For all other laboratories that had IHC protocols with moderate and high analytical sensitivity, a readout was adjusted to higher IHC protocol analytical sensitivity using a cutoff of >30% of >2+ staining intensity. This modification yielded significantly improved diagnostic accuracy against GEP even without any changes to the IHC protocols and was widely applicable to the range of analytical sensitivities of IHC protocols, which are currently in use. IHC biomarkers for HA can be highly accurate and harmonized across different laboratories for clinical application if the following criteria are met: (i) testing laboratories use standardized reference materials to set up and monitor analytical sensitivity, and (ii) the pathologist’s readout and cutoff are adjusted to the overall IHC protocol analytical sensitivity. Plain Language SummaryThis Canada-wide study evaluated how well Hans’ algorithm (HA) classifies Diffuse large B-cell lymphoma (DLBCL) cell-of-origin (COO) into GCB and non-GCB in real-world practice, compared with gene expression profiling (GEP; Lymph2CX). Ninety-six GEP-defined DLBCL cases were split into a training cohort (N=45) and validation cohort (N=51), and tissue microarrays were stained with routine CD10, Bcl-6, and MUM1 IHC protocols from 10 laboratories, categorized as weak (1/10), moderate (4/10), or strong (5/10) sensitivity. The original HA worked well only with low-sensitivity IHC. Adjusting HA readouts to a cutoff of >30% of >2+ staining markedly improved accuracy across moderate and strong protocols, showing that HA can be highly accurate if IHC sensitivity is standardized and readout cutoffs are aligned with protocol strength. Text is machine generated and may contain inaccuracies. FAQ
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".