Immunohistochemical Subcellular Localization of Protein Biomarkers Distinguishes Benign from Malignant Thyroid Nodules: Potential for Fine-Needle Aspiration Biopsy Clinical Application
Bibliographic record
Abstract
BACKGROUND: It is of critical clinical importance to select accurately for surgery thyroid nodules at risk for malignancy and avoid surgery on those that are benign. Using alterations in subcellular localization for seven putative biomarker proteins (identified by proteomics), this study aimed to define a specific combination of proteins in surgical tissues that could distinguish benign from malignant nodules to assist in future surgical selection by fine-needle aspiration biopsy (FNAB). METHODS: Immunohistochemical subcellular localization (IHC) analyses of seven proteins were retrospectively performed on surgical tissues (115 benign nodules and 114 papillary-based thyroid carcinomas [TC]), and a risk model biomarker panel was developed and validated. The biomarker panel efficacy was verified in 50 FNAB formalin-fixed and paraffin-embedded cell blocks, and 26 cytosmears were prepared from fresh surgically resected thyroid nodules. RESULTS: Selection modeling using these proteins resulted in nuclear phosphoglycerate kinase 1 (PGK1) loss and nuclear Galectin-3 overexpression as the best combination for distinguishing TC from benign nodules (area under the curve [AUC] 0.96 and 0.95 in test and validation sets, respectively). A computed malignancy score also accurately identified TC in benign and indeterminate nodules (test and validation sets: AUC 0.94, 0.90; specificity 98%, 99%). Its efficacy was confirmed in surgical FNAB cell blocks and cytosmears. CONCLUSION: Using surgical tissues, it was observed that a combination of PGK1 and Galectin-3 had high efficiency for distinguishing benign from malignant thyroid nodules and could improve surgical selection for TC among indeterminate nodules. Further validation in prospective preoperative FNAB will be required to confirm such a clinical application.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".