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Table 2 Performance comparison of radiomics after bagging with 30 times of bootstrap iterations

From: Inferring FDG-PET-positivity of lymph node metastases in proven lung cancer from contrast-enhanced CT using radiomics and machine learning

Classifier FSM Accuracy Sensitivity Specificity PPV NPV AUC
LDA AUC 0.79 (0.77−0.81) 0.74 (0.71−0.77) 0.8 (0.78−0.84) 0.57 (0.55−0.62) 0.9 (0.89−0.91) 0.86 (0.85−0.87)
LDA MI 0.79 (0.77−0.8) 0.73 (0.7−0.76) 0.8 (0.79−0.83) 0.58 (0.55−0.61) 0.89 (0.88−0.9) 0.86 (0.85−0.87)
LDA MRMI 0.78 (0.76−0.8) 0.72 (0.7−0.77) 0.8 (0.75−0.83) 0.56 (0.53−0.6) 0.89 (0.88−0.9) 0.86 (0.85−0.87)
LDA Wilcoxon 0.79 (0.76−0.81) 0.76 (0.72−0.79) 0.79 (0.76−0.82) 0.57 (0.53−0.61) 0.9 (0.89−0.91) 0.86 (0.86−0.87)
Logistic regression AUC 0.78 (0.76−0.8) 0.73 (0.69−0.76) 0.8 (0.78−0.82) 0.57 (0.54−0.59) 0.89 (0.88−0.9) 0.87 (0.85−0.87)
Logistic regression MI 0.78 (0.71−0.81) 0.73 (0.63−0.81) 0.8 (0.69−0.87) 0.57 (0.48−0.66) 0.89 (0.86−0.92) 0.87 (0.74−0.88)
Logistic regression MRMI 0.78 (0.74−0.8) 0.73 (0.7−0.79) 0.79 (0.75−0.82) 0.56 (0.51−0.6) 0.89 (0.88−0.91) 0.86 (0.83−0.87)
Logistic regression Wilcoxon 0.78 (0.77−0.81) 0.74 (0.71−0.77) 0.79 (0.78−0.83) 0.57 (0.54−0.61) 0.89 (0.89−0.91) 0.86 (0.85−0.87)
MLP AUC 0.78 (0.73−0.81) 0.73 (0.69−0.83) 0.79 (0.71−0.85) 0.56 (0.49−0.63) 0.89 (0.88−0.92) 0.86 (0.85−0.87)
MLP MI 0.78 (0.73−0.82) 0.71 (0.68−0.82) 0.81 (0.73−0.86) 0.57 (0.5−0.64) 0.89 (0.87−0.92) 0.86 (0.85−0.87)
MLP MRMI 0.78 (0.74−0.82) 0.73 (0.68−0.8) 0.8 (0.73−0.84) 0.57 (0.51−0.64) 0.89 (0.87−0.91) 0.86 (0.84−0.87)
MLP Wilcoxon 0.78 (0.71−0.8) 0.74 (0.68−0.8) 0.79 (0.68−0.84) 0.56 (0.47−0.61) 0.89 (0.88−0.91) 0.86 (0.82−0.88)
PLS AUC 0.78 (0.75−0.8) 0.71 (0.7−0.76) 0.8 (0.75−0.83) 0.57 (0.52−0.6) 0.89 (0.88−0.9) 0.86 (0.85−0.87)
PLS MI 0.79 (0.76−0.81) 0.71 (0.7−0.75) 0.81 (0.77−0.84) 0.57 (0.54−0.62) 0.89 (0.88−0.9) 0.86 (0.85−0.87)
PLS MRMI 0.78 (0.75−0.8) 0.73 (0.71−0.79) 0.8 (0.75−0.83) 0.57 (0.52−0.61) 0.89 (0.88−0.91) 0.87 (0.86−0.87)
PLS Wilcoxon 0.78 (0.74−0.79) 0.73 (0.71−0.77) 0.78 (0.75−0.82) 0.55 (0.51−0.59) 0.89 (0.88−0.91) 0.86 (0.86−0.87)
Rpart AUC 0.74 (0.7−0.83) 0.77 (0.63−0.81) 0.73 (0.66−0.9) 0.51 (0.46−0.7) 0.9 (0.87−0.92) 0.75 (0.75−0.83)
Rpart MI 0.77 (0.71−0.83) 0.73 (0.62−0.83) 0.78 (0.67−0.9) 0.55 (0.48−0.68) 0.89 (0.87−0.92) 0.76 (0.74−0.82)
Rpart MRMI 0.76 (0.72−0.82) 0.73 (0.62−0.8) 0.78 (0.7−0.9) 0.54 (0.48−0.68) 0.89 (0.87−0.91) 0.76 (0.74−0.81)
Rpart Wilcoxon 0.78 (0.69−0.81) 0.73 (0.68−0.8) 0.79 (0.66−0.83) 0.56 (0.44−0.62) 0.89 (0.88−0.92) 0.79 (0.75−0.84)
SVM AUC 0.78 (0.76−0.8) 0.71 (0.68−0.75) 0.8 (0.78−0.84) 0.56 (0.53−0.6) 0.88 (0.88−0.9) 0.87 (0.86−0.87)
SVM MI 0.78 (0.76−0.8) 0.71 (0.68−0.74) 0.81 (0.77−0.84) 0.57 (0.53−0.61) 0.89 (0.88−0.89) 0.86 (0.86−0.87)
SVM MRMI 0.78 (0.75−0.8) 0.71 (0.7−0.78) 0.8 (0.76−0.83) 0.56 (0.52−0.6) 0.89 (0.88−0.91) 0.86 (0.85−0.88)
SVM Wilcoxon 0.78 (0.76−0.79) 0.71 (0.69−0.75) 0.8 (0.77−0.82) 0.57 (0.53−0.59) 0.89 (0.88−0.9) 0.86 (0.86−0.87)
  1. Data are given as median and 95% confidence interval. AUC Area under curve at receiver operating characteristics analysis, FSM Feature selection method, LDA Linear discriminant analysis, MI Mutual information, MLP Multilayer perceptron (neuronal network), MRMI Maximum relevance minimum redundancy, NPV Negative predictive value, PLS Partial least squares, PPV Positive predictive value, Rpart Recursive partition, SVM Support vector machine