Biomarker panel diagnosis of thyroid cancer: a critical review
Bibliographic record
Abstract
The accurate preoperative diagnosis of thyroid cancer continues to be a significant challenge for those individuals who present with nodular thyroid disease, particularly for tumors with indeterminate cytomorphological features by fine-needle aspiration biopsy. In an effort to develop improved diagnostic tools, a number of studies have investigated the discriminatory potential of many different RNA and protein molecules. However, no individual thyroid cancer biomarker has been found with sufficient sensitivity and specificity. Therefore, research focus has shifted to panels of multiple markers with the hope of improved performance and robustness. A panel comprised of GAL3, CK19 and HBME1 is by far the most studied to date and offers some improvement over individual marker performance alone. However, relatively few marker panels have been studied and their performances and application as diagnostic tests have not been consistently reported. We present a comprehensive review of molecular marker panel studies for thyroid tumors and current issues and challenges. In the future, studies evaluating larger numbers of biomarkers in large patient cohorts are required for the development and validation of a clinically applicable test.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".