Prospective Validation of ThyroSPEC Molecular Testing of Indeterminate Thyroid Nodule Cytology Following Diagnostic Pathway Optimization
Bibliographic record
Abstract
Background: Molecular testing for cytologically indeterminate thyroid nodules (ITNs) is often reported with incomplete data on clinical assessment and ultrasound malignancy risk (USMR) stratification. This study aimed to clinically validate the diagnostic accuracy of a novel molecular test, assess the incremental preoperative malignancy risk of other clinical factors, and measure the impacts of introducing molecular testing at the population level. Methods: Comprehensive clinical data were collected prospectively for the first 615 consecutive patients with ITNs in a centralized health care system following implementation of a reflexive molecular test. Clinical data include patient history, method of nodule discovery, clinical assessment, USMR, cytology, molecular testing, and surgery or follow-up along with surgeon notes on surgical decision-making. Accuracy of molecular testing and the impact of the introduction of molecular testing were calculated. A multivariable regression model was developed to identify which clinical factors have the most diagnostic significance for ITNs. Results: A locally developed, low-cost molecular test achieved a negative predictive value (NPV) of 76–91% [confidence interval, CI 66–95%] and a positive predictive value (PPV) of 46–65% [CI 37–75%] in ITNs using only residual material from standard liquid cytology fine-needle aspiration (FNA). Sensitivity was highest (80%; [CI 63–92%]) in the American Thyroid Association (ATA) intermediate-suspicion ultrasound category, and lowest (46%; [CI 19–75%]) in the ATA high-suspicion ultrasound category. Following implementation of molecular testing, diagnostic yield increased by 14% ( p = 0.2442) and repeat FNAs decreased by 24% ( p = 0.05). Mutation was the primary reason for surgery in 76% of resected, mutation-positive patients. High-risk mutations were associated with a 58% ( p = 0.0001) shorter wait for surgery. Twenty-six percent of patients with a negative molecular test result underwent surgery. Multivariable regression highlighted molecular testing and USMR as significantly associated with malignancy. Conclusions: Molecular testing improves preoperative risk stratification but requires further stratification for intermediate-risk mutations. Incorporation of clinical factors (especially USMR) with molecular testing may increase the sensitivity for detection of malignancy. Introduction of molecular testing offers some clinical benefits even in a low resection rate setting, and directly influences surgical decision-making. This study illustrates the importance of the local diagnostic pathway in ensuring appropriate integrated use of molecular testing for best outcomes.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".