Evaluation of 167 Gene Expression Classifier ( GEC ) and ThyroSeq v2 Diagnostic Accuracy in the Preoperative Assessment of Indeterminate Thyroid Nodules: Bivariate/HROC Meta-analysis

Abstract

The objective of this meta-analysis was to evaluate the performance of the Gene Expression Classifier (GEC) and ThyroSeq v2 (ThyroSeq) in the preoperative diagnosis of thyroid nodules with indeterminate fine-needle aspiration biopsy results. We searched literature databases from January 2001 to April 2018. The bivariate model analysis was performed to estimate pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR−), positive predictive value (PPV), and negative predictive value (NPV). Pooled data from 1086 nodules with histopathologic confirmation from 16 GEC studies enabled calculation of diagnostic parameters (95% confidence interval): sensitivity 98% (96–99%), specificity 12% (8–20%), PPV 45% (37–53%), and NPV 91% (85–96%). Pooled data from five ThyroSeq studies assessing 459 nodules showed sensitivity of 84% (74–91%), specificity 78% (50–92%), PPV 58% (31–81%), and NPV 93% (89–97%). When both tools were compared, GEC had a significantly higher sensitivity (p = 0.003), while ThyroSeq had a significantly higher specificity (p < 0.001) and accuracy (p = 0.015). Pooled LR+ was higher for ThyroSeq: 3.79 (1.40–10.27) vs. 1.12 (1.05–1.20). Pooled LR− was higher for GEC, 0.20 (0.10–0.39) vs. 0.13 (0.05–0.31). The bivariate summary estimates of sensitivity and specificity for GEC and ThyroSeq and their pooled accuracy showed a superiority of the ThyroSeq test. The GEC with a high sensitivity and NPV may be helpful in ruling out malignancy in cases of indeterminate thyroid nodule cytology. ThyroSeq has a significantly higher specificity and accuracy with an acceptable sensitivity so that it has the potential for use as an all-round test of malignancy of thyroid nodules.

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