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Table 3 Metrics of IHC-decision tree and machine learning algorithms

From: Discriminant analysis and machine learning approach for evaluating and improving the performance of immunohistochemical algorithms for COO classification of DLBCL

  Algorithm Antibody combination Acc Sens Spec PPV NPV LR+ LR−
IHC-decision tree Nyman 3,5 0.79 0.65 0.95 0.93 0.71 12.47 0.37
Colomo 1,2,5 0.84 0.77 0.91 0.91 0.79 8.98 0.25
Hans 1,2,5 0.89 0.95 0.83 0.86 0.94 5.52 0.06
Hans* 1,5 0.86 0.95 0.76 0.81 0.94 3.94 0.06
Choi 1,2,3,4,5 0.93 1.00 0.84 0.87 1.00 6.44 0.00
Choi* 1,3,4,5 0.83 0.79 0.86 0.86 0.79 5.73 0.24
VY3 1,2,3 0.90 0.97 0.83 0.86 0.96 5.61 0.04
VY4 1,2,3,4 0.90 0.97 0.83 0.86 0.96 5.61 0.04
Machine learning PV 1,3,4,5 0.94 0.95 0.93 0.94 0.95 13.8 0.05
ANN 1,2,3,4,5 0.94 0.95 0.93 0.94 0.95 13.8 0.05
BS 1,2,3,4,5 0.94 0.95 0.93 0.94 0.95 13.8 0.05
SVM 1,2,3,4,5 0.94 0.97 0.91 0.92 0.96 11.23 0.04
SVM 1,2,3,4 0.94 0.97 0.91 0.92 0.96 11.23 0.04
  1. Metrics correspondent to eight IHC-decision tree algorithms and the best five machine learning algorithms are shown, cases of the VY subset were classified. Numeric Tags 1= CD10, 2 = BCL6, 3 = FOXP1, 4 = GCTE1, and 5 = MUM1. IHC-decision tree algorithms could not overcome any of the remarkable metrics obtained for the best five machine learning algorithms
  2. Acc: accuracy; Sens: sensitivity; Spec: specificity; PPV: positive predictive value; NPV: negative predictive values; LR+: likelihood ratio for positive test results; LR−: likelihood ratio for negative test result; PV: Perfecto–Villela; ANN: artificial neural networks; BS: Bayesian simple; SVM: support vector machine