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Table 2 Performance measures of classifiers in different datasets

From: Molecular differential diagnosis of follicular thyroid carcinoma and adenoma based on gene expression profiling by using formalin-fixed paraffin-embedded tissues

Dataset (origin, method of analysis) Method Accuracy (%) PPV (%) NPV (%) Sensitivity (%) Specificity (%) Prevalence of FTC in dataset (%)
B (own, microarray) DLDA classification based on the 8 best genes chosen from 99 preselected ones.* 80 82 78 76 83 50
DLDA classification based on 45 (optimal number) best genes chosen from 99 preselected ones.* 84 85 83 83 85 50
C (own, FFPE qPCR) 5-gene DLDA classification (cut-off 0.5)** 72(95% CI: 60–82) 67(95% CI: 48–82) 76(95% CI: 60–89) 71(95% CI: 52–86) 72(95% CI: 56–85) 44
5-gene DLDA classification (cut-off 0.12)** 70 61 88 90 55 44
D (own, microarray) 5-gene DLDA classifier trained on dataset B, tested on D 73 77 69 71 75 54
E1 (Weber et al. microarray) 5-gene DLDA classifier.** 92 100 86 83 100 50
E2 (Hinsch et al. microarray) 5-gene DLDA classifier.** 83 100 67 75 100 67
  1. Accuracy, proportion of all samples that are correctly classified; PPV, positive predictive value; NPV, negative predictive value; SVM, support vector machines; DLDA, diagonal linear discriminant analysis; CI, confidence interval. *Performance assessed by 10-fold cross-validation. **Performance assessed by leave-one-out cross-validation.