Clinical value of molecular markers as diagnostic and prognostic tools to guide treatment of thyroid cancer
Bibliographic record
Abstract
OBJECTIVE: Advances in our understanding of the molecular biology of thyroid tumours is being rapidly translated into their clinical management. This review summarizes the current use of molecular testing in thyroid tumours, focusing on their usefulness as diagnostic and prognostic tools to guide treatment with consideration of present limitations. DESIGN: Considerations about molecular testing applications for the diagnosis and treatment of thyroid tumours are divided into four sections/roles: (1) evaluating cytologically indeterminate thyroid nodules; (2) guiding extent of surgery in indeterminate thyroid nodules; (3) completing histological characterization of thyroid tumours and (4) identifying actionable mutations in advanced progressive thyroid cancers. RESULTS: Genomic testing can improve the presurgical malignancy risk assessment in indeterminate thyroid nodules. However, a prior in-depth analysis of institutional quality and outcomes of sonographical, cytological and histological characterization of thyroid tumours is necessary. Presently, it remains uncertain whether knowing the molecular profile of a cytologically indeterminate thyroid nodule might be advantageous to modify the extent of initial surgery. Molecular characterization of thyroid tumours can be a valuable adjunct to morphological diagnosis in some challenging cases, such as in low-risk follicular cell-derived neoplasms, or rare tumours. Finally, as selective kinase inhibitors are available, molecular testing in locally advanced/metastatic progressive thyroid cancers should also be integrated into the institutional clinical management pathway to improve outcomes and limit toxicity. CONCLUSIONS: Molecular testing needs to be implemented into the local evidence-based clinical management thyroid nodule/cancer pathways to improve its diagnostic and prognostic value and to optimize cost-effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".