Development of a Nomogram to Integrate Molecular Testing and Clinical Variables to Improve Malignancy Risk Assessment Among Cytologically Indeterminate Thyroid Nodules
Bibliographic record
Abstract
Background:The introduction of molecular testing (MT) of cytologically indeterminate thyroid nodules (ITNs) alone has not impacted thyroidectomy rates. Due to this, we evaluated the incremental diagnostic value of various clinical variables in addition to MT results, in predicting the risk of malignancy (ROM) among ITNs. Methods:This prospective observational study included 1024 consecutive ITNs that underwent reflexive ThyroSPEC MT between Jul 30, 2020, and Oct 30, 2023. A multivariable logistic regression model was built to assess the relationship between histology outcomes and clinical variables, including nodule discovery by palpation, ultrasound risk categories, maximum nodule size, Bethesda category, Bethesda atypia, and ThyroSPEC categories. A total of 332 out of 1024 patients who underwent surgery and had complete data for all variables were included in the model. A nomogram was subsequently developed based on the model. Results:The model achieved a cross-validated AUC of 0.831 (95% confidence intervals: 0.787–0.874). Patients with high-risk mutations or malignant molecular markers exhibited significantly higher odds (152.79 times) of malignancy compared to those with mutation-negative or benign molecular marker results. Patients with maximum nodule size >5 cm have 4.34 times higher odds of malignancy than those 0–2 cm. The presence of nuclear atypia increased the odds of malignancy by 4.26 times, while ultrasound malignancy risk category 5 increased the odds of malignancy by 2.89 times compared to categories 1–3. Positive palpation discovery increased the odds by 1.83 times. The integrated ROM estimated from the regression model is significantly associated with the surgery type (p < 0.001). In the low (0–30%) and intermediate ROM (31–70%) categories, lobectomy alone is the most common surgery (61% and 70%, respectively), while in the high ROM (>70%) category, total thyroidectomy dominates (62%). Conclusions:Although MT alone played an important role in decision-making regarding surveillance versus surgery in our study population, integrating MT results with additional clinical variables improved the malignancy risk prediction for ITNs. Our results highlight the importance of contextualizing MT results within an integrated interdisciplinary thyroid nodule diagnostic pathway.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".