Small Samples, Big Insights: PD-L1 Experience of a Tertiary Institution
Bibliographic record
Abstract
Background PD-L1 expression guides immunotherapy decisions in non small cell lung cancer (NSCLC), yet sample type can impact assessment accuracy. This study aimed to compare PD-L1 expression between small (biopsies, cell blocks) and large (resection) NSCLC samples, assess interobserver variability, and examine whether PD-L1 scoring trends remained stable over a multi-year period. Methods A retrospective analysis was conducted on 494 NSCLC patients tested for PD-L1 (Ventana SP263) between 2018 and 2022. Sample type, tumor subtype, PD-L1 tumor proportion score (TPS), and reporting pathologist were recorded. Interobserver variability was analyzed based on routine diagnostic reports from different pathologists evaluating non-overlapping patient cohorts. Additionally, a subset of 43 patients had matched cell block and resection specimens collected from the same tumor, allowing direct comparison between preparations. Results Among the 494 NSCLC specimens, 152 were large and 342 were small samples. TPS results showed 112 samples (22%) with TPS ≥ 50%, 163 (34%) with TPS 1%–49%, and 219 (44%) with TPS < 1%. No significant differences in TPS categories were observed between cell blocks and tissue samples (p = 0.176) or between small and large samples (p = 0.326). TPS distributions across different pathologists (p = 0.260) and years (p = 0.250) remained consistent. In the matched 43 specimens, TPS concordance between cell block and resection was high (κ = 0.892). Conclusion Small biopsies and cell blocks provide reliable PD-L1 results comparable to resection specimens, supporting their use for PD-L1 testing in clinical settings to enhance timely immunotherapy access for NSCLC 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".