Cytology cell blocks are suitable for immunohistochemical testing for PD-L1 in lung cancer
Bibliographic record
Abstract
Background: PD-L1 immunohistochemistry (IHC) testing is usually carried out on tissue blocks from core needle biopsy or surgical resections. In this study, we assessed the feasibility of using cytology cell blocks for PD-L1 IHC assay. Methods: A total of 1419 consecutive cases of non-small-cell lung cancer (NSCLC), including 371 cytology cell blocks, 809 small biopsies, and 239 surgical specimens, were included in the study. The cytology cell blocks were prepared with formalin only, methanol/alcohol only or both. PD-L1 expression was examined by staining with Dako PD-L1 IHC 22C3 pharmDx kit. A Tumor Proportion Score (TPS) was categorized as <1%, 1%-49% and ≥50% tumor cells. A total of 100 viable tumor cells were required for adequacy. Results: Of the cytology cell blocks, 92% of the specimens had an adequate number of tumor cells, not significantly different from small biopsies. The rate of TPS ≥50% differed between sample types and was observed in 42% of cytology cell blocks versus 36% of small biopsies (P = 0.04), and 29% of surgical resections (P = 0.001). The fixative methods did not affect the immunostaining, with overall PD-L1 high expression (TPS ≥50%) rates of 42% in formalin-fixed specimens versus 40% in specimens with combined fixation by methanol/alcohol and formalin (NS). The PD-L1 high expression rate was not associated with EGFR, ALK or KRAS molecular alterations. Higher stage (IV) was associated with higher PD-L1 TPS (P= 0.001). Conclusion: Our results show that when the TPS ≥50% is used as the end point, PD-L1 IHC performs well with cytology cell blocks. Cell blocks should be considered as a valuable resource for PD-L1 testing in advanced NSCLC. The clinical significance of higher PD-L1 IHC scores in cytology specimens needs to be evaluated prospectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".