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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".