Diagnostic Accuracy of a Defined Immunophenotypic and Molecular Genetic Approach for Peripheral T/NK-cell Lymphomas
Bibliographic record
Abstract
The diagnosis of peripheral T-cell and NK-cell lymphomas (PTNKCL) is difficult with few standards for required ancillary studies. We evaluated a series of PTNKCLs using a tiered approach to immunohistochemistry and molecular genetic characterization to document diagnostic accuracy and clinical relevance. Seven hematopathologists reviewed 374 cases that included PTNKCL and non-PTNKCL cases to mimic diagnostic practice. Cases received tier 0, 1, and 2 diagnoses by 3 independent pathologists, on the basis of hematoxylin and eosin stains and progressive immunohistochemistry panels. A tier 2b diagnosis was rendered when gene rearrangement data were available, and a final consensus diagnosis was rendered after discussion of each case. Across all 374 cases, consensus agreement was 92.5%. For PTNKCLs, World Health Organization subclassification was possible in 16.5%, 37.1%, 82.8%, and 85.9% of individual reviewer diagnoses at tier 0, 1, 2, and 2b, respectively. Gene rearrangement contributed to a change in diagnosis in 51 of 647 (8%) individual reviews. Following this algorithm may provide prognostic information on the basis of individual marker expression in common PTNKCL types (CD4 in peripheral T-cell lymphoma, not otherwise specified and PD-1 in angioimmunoblastic T-cell lymphoma). This evidence-based approach to the diagnosis of PTNKCL informs practicing pathologists, clinical trial designers, and policy-makers regarding required ancillary studies.
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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 | 0.031 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".