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Table 2 Performance metrics of AI-based models at different magnifications

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

Magnification AUC TP FP TN FN Sensitivity Specificity PPV NPV Accuracy F1 score Cut-off
10 × 0.82 45 13 58 15 75.0% 81.7% 77.6% 79.5% 78.6% 0.78 0.4
20 × 0.84 45 12 59 15 75.0% 83.1% 78.9% 79.7% 79.4% 0.79 0.5
40 × 0.85 45 13 58 15 75.0% 81.7% 77.6% 79.5% 78.6% 0.78 0.35
  1. AUC Area under curve in receiver operator characteristics curve, 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