Best Practices in CD30 Immunohistochemistry Testing, Interpretation, and Reporting: An Expert Panel Consensus
Bibliographic record
Abstract
CONTEXT.—: Although CD30 testing is an established tool in the diagnostic workup of lymphomas, it is also emerging as a predictive biomarker that informs treatment. The current definition of CD30 positivity by immunohistochemistry is descriptive and based on reactivity in lymphomas that are defined by their universal strong expression of CD30, rather than any established threshold. Challenges include inconsistencies with preanalytic variables, tissue processing, pathologist readout, and with the pathologist and oncologist interpretation of reported results. OBJECTIVE.—: To develop and propose general best practice recommendations for reporting CD30 expression by immunohistochemistry in lymphoma biopsies to harmonize practices across institutions and facilitate assessment of its significance in clinical decision-making. DESIGN.—: Following literature review and group discussion, the panel of 14 academic hematopathologists and 2 clinical/academic hematologists/oncologists divided into 3 working groups. Each working group was tasked with assessing CD30 testing by immunohistochemistry, CD30 expression readout, or CD30 expression interpretation. RESULTS.—: Panel recommendations were reviewed and discussed. An online survey was conducted to confirm the consensus recommendations. CONCLUSIONS.—: CD30 immunohistochemistry is required for all patients in whom classic Hodgkin lymphoma and any lymphoma within the spectrum of peripheral T-cell lymphoma are differential diagnostic considerations. The panel reinforced and summarized that immunohistochemistry is the preferred methodology and any degree of CD30 expression should be reported. For diagnostic purposes, the interpretation of CD30 expression should follow published guidelines. To inform therapeutic decisions, report estimated percent positive expression in tumor cells (or total cells where applicable) and record descriptively if nontumor cells are positive.
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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 | 0.397 | 0.325 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.015 | 0.015 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".