Performance
ThyGeNEXT + ThyraMIR accurately ruled-in disease in four out of five nodules tested–including RAS-positive nodules1
ThyGeNEXT + ThyraMIR is the only testing platform that utilizes both mutational and microRNA markers
Demonstrated High Sp/PPV and Comparable Sn/NPV to Other Marketed Tests1-3*
Performance in Bethesda III and IV (n=178)
| Unadjusted | Spectrum Effect Adjusted† | Sensitivity (Sn) | 93% | 95% |
|---|---|---|
| Specificity (Sp) | 90% | 90% |
| NPV (Negative Predictive Value) | 95% | 97% |
| PPV (Positive Predictive Value) | 74% | 75% |
*Multicenter, retrospective, blinded validation study of 309 subjects with indeterminate thyroid nodules (Bethesda III, IV, or V) and corresponding surgical histology. Gold-standard unanimous consensus histopathology diagnosis (n=197) among three pathologists was used, and all molecular testing was performed using archived cytology smears. Results were reported in 3 categories (negative, moderate, or positive) based on the combined test results from both ThyGeNEXT® (mutation panel) and ThyraMIR® (microRNA risk classifier). The proportion of benign and malignant histopathologic subtypes within this study vastly differed from those found within a recent prospective study.1,2 Histopathologic subtype prevalence adjustments were used to determine the impact of these differences on test performance.4 The analysis showed that test performance was optimal after prevalence adjustments.
The 3-category microRNA and mutation panel combination test had a high PPV (positive predictive value) at 75% and high NPV (negative predictive value) at 97% (n=178) after prevalence adjustments.1 Prevalence-unadjusted, PPV and NPV were 74% and 95%, respectively, for the 3-category approach.1
microRNA classification further risk-stratified patients with weak driver mutations, including RAS, resulting in four out of five nodules being accurately ruled-in or ruled-out for disease.1
The Spectrum Effect4
The Spectrum Effect describes the variation in performance of tests for diagnosis of disease among different population subgroups.
Measures of test performance can vary not only by the prevalence of disease, but also by large differences between study cohorts.
Adjusting for subgroup differences aids the comparison and interpretation of test performance for the intended use population.
