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Table 2 LS-SVM models for the prediction of WHEELER, pN-STAGE and CRM in rectal cancer

From: A kernel-based integration of genome-wide data for clinical decision support

Outcome Model NG* NP AUC (SE) p-value§
WHEELER      
   A MT 0 4   0.7538 (0.1085) 0.0987
  MT 1 29   0.9038 (0.0502) 0.6861
  PT 0   35 0.7423 (0.0867) 0.0540
  PT 1   11 0.9038 (0.0575) 0.7273
   B MT0-T1 32   0.6846 (0.1215) 0.0598
  PT0-T1   5 0.8654 (0.0621) 0.4135
   C MT 01 3   0.7808 (0.0985) 0.1320
  PT 01   21 0.7692 (0.0831) 0.0831
  MPT 0 3 35 0.8461 (0.0718) 0.2760
  MPT 1 25 12 0.9269 (0.0425)  
  MPT 01 2 31 0.8846 (0.0558) 0.4858
  MT 0 PT 1 2 4 0.9385 (0.0444) 0.8101¥
pN-STAGE      
   A MT 0 25   0.6493 (0.0914) 2.315e-4
  MT 1 22   0.8506 (0.0665) 0.0362
  PT 0   2 0.6753 (0.0906) 6.659e-4
  PT 1   12 0.8409 (0.0652) 0.0238
   B MT0-T1 4   0.6071 (0.0986) 1.359e-4
  PT0-T1   9 0.7662 (0.0900) 0.0153
   C MT 01 24   0.9286 (0.0450) 0.1998
  PT 01   34 0.8182 (0.0695) 0.0145
  MPT 0 27 27 0.9188 (0.0469) 0.1591
  MPT 1 21 14 0.9870 (0.0135)  
  MPT 01 23 16 0.9610 (0.0280) 0.3421
  MT 0 PT 01 26 20 1 (0) 0.3347¥
CRM      
   A MT 0 33   0.6790 (0.1016) 0.0072
  MT 1 9   0.9259 (0.0472) 0.4955
  PT 0   34 0.8518 (0.0624) 0.0935
  PT 1   34 0.7654 (0.0831) 0.0281
   B MT0-T1 6   0.9136 (0.0480) 0.4030
  PT0-T1   2 0.8272 (0.0709) 0.0849
   C MT 01 16   0.8066 (0.0846) 0.0468
  PT 01   3 0.7531 (0.0865) 0.0227
  MPT 0 7 27 0.8477 (0.0688) 0.1340
  MPT 1 7 33 0.9630 (0.0344)  
  MPT 01 2 3 0.8230 (0.0771) 0.0973
  MT 1 PT 0 16 14 0.9630 (0.0376) 1
  MT 01 PT 1 9 29 0.9876 (0.0146) 0.4924¥
  1. *Number of genes selected in each LOO iteration. Number of proteins selected in each LOO iteration. Area under the ROC curve (standard error) obtained with leave-one-out. §Comparison of AUC between each model and the best model in bold [46]. Number of features used at both time points. ¥This model is better than the model in bold we compare with.