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Table 3 Algorithm performance metrics based on tumor grade

From: Leveraging artificial intelligence to predict ERG gene fusion status in prostate cancer

Magnification Grade Group TP FP TN FN Sensitivity Specificity PPV NPV Accuracy F1 score Cut-off
10 × 1–2 33 8 25 1 97.1% 75.8% 80.5% 96.2% 86.6% 0.88 0.4
3–5 12 5 33 14 46.2% 86.8% 70.6% 70.2% 70.3% 0.56 0.4
20 × 1–2 28 3 30 6 82.4% 90.9% 90.3% 83.3% 86.6% 0.86 0.5
3–5 17 9 29 9 65.4% 76.3% 65.4% 76.3% 71.9% 0.65 0.5
40 × 1–2 28 5 28 6 82.4% 84.8% 84.8% 82.4% 83.6% 0.84 0.35
3–5 17 8 30 9 65.4% 78.9% 68.0% 76.9% 73.4% 0.67 0.35
  1. TP True positive (correctly classified as ERG-positive), FP False positive (incorrectly classified as ERG-positive), TN True negative (correctly classified as ERG-negative), FN False negative (incorrectly classified as ERG-negative), PPV Positive predictive value, NPV: Negative predictive value, Cut-off indicates cut-off value of proportion of positive patches that gives best accuracy