Impact of Molecular Testing on Surgical Decision-Making in Indeterminate Thyroid Nodules: A Systematic Review and Meta-Analysis of Recent Advancements
Bibliographic record
Abstract
Background: The management of indeterminate thyroid nodules (Bethesda III/IV) has evolved with molecular testing, aiming to reduce unnecessary surgeries. However, the comparative effectiveness of different platforms in influencing surgical decision-making remains unclear. This systematic review and meta-analysis evaluate the impact of molecular testing on surgical avoidance rates. Methods: A systematic literature search was conducted across eight electronic databases, including Embase, PubMed, and Cochrane Library, from January 2019 to December 2024, following PRISMA guidelines to encompass most recent advancements in the last 5 years. Studies evaluating Afirma Gene Expression Classifier (GEC), Afirma Genomic Sequencing Classifier (GSC), ThyroSeq V2, ThyroSeq V3, and ThyGenX/ThyraMIR were included. The primary outcome was surgical avoidance, analyzed using a random-effects model. Results: Thirty-one studies comprising 4464 indeterminate thyroid nodules met inclusion criteria. Pooled surgical avoidance rates varied across platforms: ThyroSeq V2 (50.3%, 95% CI: 20.8–79.6%), ThyroSeq V3 (62.5%, 95% CI: 54.8–70.0%), Afirma GEC (58.8%, 95% CI: 43.6–73.1%), Afirma GSC (50.6%, 95% CI: 34.3–66.8%), and ThyGenX/ThyraMIR (68.6%, 95% CI: 63.1–73.9%). ThyGenX/ThyraMIR had the highest surgical avoidance rate and lowest heterogeneity (I2 = 51.2%), while ThyroSeq showed improvement from V2 to V3. Conclusions: Molecular testing reduces unnecessary thyroid surgeries, with avoidance rates ranging from 50.3% to 68.6%. While ThyGenX/ThyraMIR showed the highest avoidance rate, its limited representation warrants cautious interpretation. Standardized protocols are needed to optimize clinical application. Further prospective studies should compare platforms and assess long-term outcomes and cost-effectiveness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".