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Table 2 The prediction accuracy of the algorithms and NCCN criteria in multi-center validation cohort

From: DrABC: deep learning accurately predicts germline pathogenic mutation status in breast cancer patients based on phenotype data

  DrABC BRCAPROa BOADICEAa Myriada PENNIIb NCCN NCCN expansionc
BRCA1/2
 AUC (95%CI) 0.792 (0.735–0.848) 0.699 (0.635–0.763) 0.586 (0.521–0.651) 0.587 (0.537–0.637) 0.628 (0.560–0.697) NA NA
 Sensitivity 82.1% 53.8% 15.4% 9.0% 61.5% 83.3% 100%
 Specificity 63.1% 72.1% 90.2% 98.9% 61.6% 31.4% 2.5%
 Youden Indexd 45.2% 25.9% 5.6% 7.9% 23.1% 14.7% 2.5%
All cancer predisposition genes
 AUC (95%CI) 0.737 (0.687–0.787) 0.650 (0.589–0.711) 0.571 (0.510–0.631) 0.556 (0.508–0.603) 0.606 (0.543–0.668) NA NA
 Sensitivity 83.8% 45.5% 15.2% 7.1% 58.6% 78.8% 100%
 Specificity 51.3% 71.6% 90.3% 98.9% 61.9% 31.2% 2.5%
 Youden Indexd 35.1% 17.1% 5.5% 6.0% 20.5% 10.0% 2.5%
  1. aThe cutoff values were set as 5%
  2. bThe cutoff values were set as 10%
  3. cExpansion of the NCCN criteria included all women diagnosed with breast cancer younger than 65 years of age
  4. dThe Youden index was calculated as J = sensitivity+specificity-1
  5. Abbreviations: AUC area under the curve, CI confidence interval, NA not applicable