Programmed Death - Ligand 1 Expression Distinguishes Invasive Encapsulated Follicular Variant of Papillary Thyroid Carcinoma from Noninvasive Follicular Thyroid Neoplasm with Papillary-like Nuclear Features
Bibliographic record
Abstract
BACKGROUND: The noninvasive Encapsulated follicular variant of papillary thyroid cancer (EFVPTC) has been reclassified as Noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) without a significant risk for malignant behavior. However the evaluation remains a challenge for clinicians. We sought to determine whether programmed death-ligand 1 (PD-L1) expression may serve as a biomarker to predict invasiveness of EFVPTC and assist to distinguish these neoplasms from NIFTP. METHODS: Immunohistochemical staining of PD-L1 expression was performed in sections of 174 Formalin-fixed paraffin-embedded (FFPE) tissue blocks from surgery removed thyroid nodules. RESULTS: Cytoplasmic PD-L1 expression was significantly increased in invasive EFVPTC (4.76±1.49) as compared to NIFTP (3.06±2.16, p<0.001). Increased cytoplasmic PD-L1 expression was associated with invasiveness in EFVPTC (p<0.001); PD-L1 positive EFVPTC cases were at 3.16 folds higher risk in developing invasion than the PD-L1 negative cases. No significant difference in cytoplasmic PD-L1 expression was observed between NIFTP and benign nodules. CONCLUSION: PD-L1 expression may serve as a useful biomarker in predicting invasiveness of EFVPTC and distinguishing NIFTP from invasive EFVPTC. To our knowledge this is the first report suggesting the application of a protein biomarker to confirm NIFTP as benign indolent neoplasms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".