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Table 1 Performance metrics of classification of IHC-decision tree algorithms and LDA

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 trees Nyman 3,5 0.72 0.52 0.91 0.84 0.67 5.56 0.53
Colomo 1,2,5 0.78 0.71 0.84 0.81 0.75 4.56 0.34
Hans 1,2,5 0.85 0.91 0.78 0.80 0.91 4.21 0.11
Hans* 1,5 0.82 0.94 0.70 0.75 0.92 3.14 0.09
Choi 1,2,3,4,5 0.88 0.94 0.84 0.84 0.93 5.70 0.08
Choi* 1,3,4,5 0.79 0.74 0.83 0.80 0.77 4.30 0.31
VY3 1,2,3 0.88 0.92 0.84 0.85 0.92 5.92 0.09
VY4 1,2,3,4 0.88 0.93 0.84 0.85 0.92 5.80 0.09
Linear discriminant analysis As in Hans* 1,5 0.84 0.77 0.91 0.89 0.81 8.59 0.25
As in Nyman 3,5 0.77 0.81 0.74 0.75 0.81 3.10 0.25
As in VY3 1,2,3 0.89 0.87 0.91 0.90 0.88 9.19 0.15
As in Hans/Colomo 1,2,5 0.87 0.86 0.88 0.87 0.87 7.25 0.16
1,4,5 0.87 0.81 0.92 0.90 0.84 9.93 0.20
As in VY4 1,2,3,4 0.87 0.84 0.90 0.89 0.86 8.24 0.17
As in Choi* 1,3,4,5 0.88 0.86 0.91 0.90 0.87 9.09 0.16
As in Choi 1,2,3,4,5 0.89 0.87 0.91 0.90 0.88 9.23 0.14
  1. The upper section corresponds to the performance of the IHC-decision tree algorithms. Lower section corresponds to equivalent combinations of antibodies, but with LDA classification, this includes the rest of combinations not reported by IHC-decision tree algorithms. Choi, VY3, and VY4 algorithms reached the most considerable accuracy, representing the most balanced options of sensibility and specificity, with similar performance metrics
  2. Numeric tags 1 = CD10, 2 = BCL6, 3 = FOXP1, 4 = GCTE1, and 5 = MUM1
  3. 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