Improving the Risk Prediction of the 2015 ATA Recurrence Risk Stratification in Papillary Thyroid Cancer
Authors' conclusion
The inclusion of lymph node features and s-Tg showed potential to enhance the predictive accuracy and clinical utility of the existing risk stratification system for PTC patients.
Study context
- Population
- Patients with PTC746 validation cohort
- Participants
- 2,539 patients
- Exposure
- Prediction models adding lymph node features (model 1) and preablation stimulated thyroglobulin (s-Tg) (model 2) vs ATA recurrence risk stratification alone
- Outcome
- Prediction of structural incomplete response (SIR)
- Follow-up
- 1-year visit
Methodological detailsprediction model development, independent validation cohort
- Prediction model development
- Independent validation cohort
Main finding
As reported in the abstract
Prediction of SIR vs recurrence risk stratification · 1 year
| Outcome | Model 1 | Model 2 |
|---|---|---|
| IDI | 0.02 | 0.07 |
| Event NRI | 42.31% | 53.54% |
Observational data: associations, not effects.
Further findings
Decision curve analysis
- Both models provided greater benefits over the risk stratification system in clinical decision-making
Validation set
- Model 2 maintained similar performance; model 1 did not significantly improve correct reclassification
Original publication
Improving the Risk Prediction of the 2015 ATA Recurrence Risk Stratification in Papillary Thyroid Cancer. J Clin Endocrinol Metab. Published 9 July 2024. doi:10.
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