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Table 2 Sensitivity, specificity, AUC (mean ± standard deviation) obtained for each dataset (images, radiomics with and without batch effect normalisation) and classifier combination over cross-validation obtained using 10 cross-validation folds on the test cohort

From: Prediction of lipomatous soft tissue malignancy on MRI: comparison between machine learning applied to radiomics and deep learning

Dataset Classifier Sensitivity (%) Specificity (%) AUC
Images CNN 90 ± 0.21 10 ± 0.10 0.53 ± 0.09
ResNet50 81.9 ± 0.06 57.7 ± 0.07 0.8 ± 0.11
FE + XGB 80.3 ± 0.12 57 ± 0.11 0.78 ± 0.13
Radiomics LR 77 ± 0.19 62.5 ± 0.24 0.84 ± 0.12
SVM 75 ± 0.14 62.5 ± 0.26 0.83 ± 0.43
RF 84 ± 0.01 62.5 ± 0.12 0.79 ± 0.65
GB 72.5 ± 0.05 72.5 ± 0.16 0.83 ± 0.12
Radiomics with batch correction LR 70 ± 0.22 77.5 ± 0.31 0.86 ± 0.13
SVM 75 ± 0.17 70 ± 0.25 0.82 ± 0.15
RF 100 ± 0.18 92.5 ± 0.33 0.96 ± 0.04
GB 98 ± 0.20 87.5 ± 0.13 0.99 ± 0.02
  1. AUC area under the curve, CNN convolutional neural networks, FE feature extraction, XGB Xgboost, GB gradient boosting, LR logistic regression, RF random forest, SVM support vector machine