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Table 5 Accuracy, sensitivity, specificity and AUC scores of MVI predictive models for training and validation sets

From: Predicting microvascular invasion in hepatocellular carcinoma: a deep learning model validated across hospitals

Model Patient number (Train: Valid)   Training set    Validation set  
Accuracy Sensitivity Specificity AUC Accuracy Sensitivity Specificity AUC
SVM with LASSO in Ma et al. (AP) 157 (117:47) 0.72 0.62 0.77 0.70 0.68 0.61 0.72 0.68
SVM with LASSO in Ma et al. (AP + PVP + DP) 157 (117:47) 0.78 0.74 0.81 0.85 0.6 0.41 0.7 0.62
SVM with LASSO in Ma et al. (AP + PVP + DP + CF) 157 (117:47) 0.84 0.76 0.88 0.88 0.66 0.5 0.76 0.68
XGB in Jiang et al.
(Radiological + radiomics + CF)
405 0.97 0.85 0.82 0.89 0.9
3D-CNN in Jiang et al.
(AP + PVP + DP)
405 0.98 0.85 0.93 0.76 0.91
ResNet-18 in this study (AP) 309 (216:93) 0.95 0.91 0.97 0.98 0.68 0.96 0.56 0.82
ResNet-18 in this study
(AP + CF)
309 (216:93) 0.97 0.94 0.98 0.97 0.72 0.96 0.62 0.85
  1. Scores that were not reported in the study were represented by ‘-’. SVM support vector machine; LASSO least absolute shrinkage and selection operator; 3D-CNN three-dimensional convolutional neural network; XGB extreme gradient boosting; AP arterial phase; PVP portal venous phase; DP delay phase; CF clinical factors; AUC area under the curve.