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Table 6 The sensitivity, specificity, and positive likelihood ratio of predicting the index MDT decisions using the best machine learning model versus using ESMO and NCCN guidelines

From: Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach

MDT recommendation Accuracy of prediction by
Modality Recommended Best ML modela ESMO Guidelines NCCN Guidelines
Strategy N (%) Sens/Spec LR+ Sens/Spec LR+ Pb Sens/Spec LR+ Pb
Chemotherapy
 Aggressive 582 (60) 0.93/0.89 8.8 0.55 c /0.78 c 2.5 <0.01 0.97/0.12c 1.1 <0.01
 Conservative 342 (35) 0.86/0.95 16.7 0.60 c /0.82 c 3.3 <0.01 0.82/0.71c 2.9 <0.01
Endocrine
 Aggressive 916 (91) 0.98/0.85 6.5 0.98/0.81 5.2 0.68 0.97/0.75 3.9 0.25
 Conservative 794 (79) 0.97/0.65 2.8 0.99/0.36c 1.5 0.30 0.96/0.50 1.9 0.37
Trastuzumab
 Aggressive 93 (20) 0.98/0.99 77.9 0.97/0.97 33.3 0.60 0.97/0.98 45.2 0.73
 Conservative 86 (19) 0.95/0.99 122.9 0.97/0.96 24.0 0.24 0.92/0.98 37.7 0.28
  1. The sensitivity (sens), specificity (spec), and the positive likelihood ratio (LR+) when using the best machine learning models or guideline to predict MDT recommendations
  2. Note: aThe best models were ripple down rules for the chemotherapy decisions, polynomial SVM for the aggressive endocrine decisions, and ADTree for the remaining groups
  3. bpairwise comparisons of likelihood ratios using two-sided z-test (i.ebest model vs. guideline)
  4. cthe best model performed better than the guideline
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