<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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".