<scp>PD‐L1</scp> expression in fine‐needle aspiration cell blocks of head and neck squamous‐cell carcinoma and its cytohistological concordance
Bibliographic record
Abstract
BACKGROUND: PD-L1 immunoexpression in head and neck squamous-cell carcinoma (HNSCC) determines immunotherapy eligibility. Patients are often diagnosed using fine-needle aspiration (FNA) of metastatic lymph nodes, however, the cytohistologic correlation of the combined positive score (CPS) is largely unknown. METHODS: This study retrospectively identified 96 paired histologic (HS) and cytologic specimens (CyS), between 2016 and 2020, diagnosed with HNSCC. Cases with <100 tumor cells (n = 54) or missing block(s) (n = 8) were excluded. All 34 case pairs were scored with CPS using the PD-L1 22C3 pharmDx assay at clinically relevant cut-offs of <1%, 1%-19%, and ≥20% independently by three observers blinded to the case pairs (CyS with corresponding HS). RESULTS: The CPS (<1/1-19/≥20) for CyS and HS were as follows: 10(29.4%)/10(29.4%)/14(41.2%) and 2(5.9%)/13(38.2%)/19(55.9%), respectively. There was fair overall cytohistologic agreement (OA) of 76.5% (k = 0.261) at the CPS cut-off of 1. The OA did not differ significantly between site-matched (n = 13) and -unmatched (n = 21) case pairs (p = .4653). CyS has a specificity and positive predictive value (PPV) of 100% but a negative predictive value (NPV) of only 20% as compared to its paired HS. CONCLUSIONS: Our study demonstrates fair CPS cytohistologic correlation in HNSCC specimens using the PD-L1 IHC 22C3 pharmDx assay with high PPV but low NPV. This suggest that determining PD-L1 status in FNA specimens can play an important role in the clinical management of HNSCC patients.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".