The role of molecular genetics in the clinical management of sporadic medullary thyroid carcinoma: A systematic review
Bibliographic record
Abstract
BACKGROUND: The significant variation in the clinical behaviour of sporadic medullary thyroid carcinoma (sMTC) causes uncertainty when planning the management of these patients. Several tumour genetic and epigenetic markers have been described, but their clinical usefulness remains unclear. The aim of this review was to evaluate the evidence for the use of molecular genetic and epigenetic profiles in the risk stratification and management of sMTC. METHODS: MEDLINE and Embase databases were searched using the MeSH terms "medullary carcinoma", "epigenetics", "molecular genetics", "microRNAs"; and free text terms "medullary carcinoma", "sporadic medullary thyroid cancer", "sMTC", "RET", "RAS" and "miR". Articles containing less than ten subjects, not focussing on sMTC, or not reporting clinical outcomes were excluded. Risk of bias was assessed using a modified version of the Newcastle-Ottawa Scale. RESULTS: Twenty-three studies met the inclusion criteria, and key findings were summarized in themes according to the genetic and epigenetic markers studied. There is good evidence that somatic RET mutations predict higher rates of lymph node metastasis and persistent disease, and worse survival. There are also several good quality studies demonstrating associations between certain epigenetic markers such as tumour miR-183 and miR-375 expression and higher rates of lymph node and distant metastasis, and worse survival. CONCLUSIONS: There is a growing body of evidence that tumour genetic and epigenetic profiles can be used to risk stratify patients with sMTC. Further research should focus on the clinical applicability of these findings by investigating the possibility of tailoring management to an individual's tumour mutation profile.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".