Skip to main content
. 2025 Feb 25;15:1491843. doi: 10.3389/fonc.2025.1491843

Table 2.

The detailed results of different classifiers among various deep models for predicting pathologic response following NAC in breast cancer patients.

Deep learning model Model AUC 95% CI Sensitivity Specificity PPV NPV
ViT SVM train 0.90 0.86-0.94 0.69 0.92 0.88 0.78
test 0.73 0.61-0.86 0.54 0.77 0.65 0.67
KNN train 0.77 0.71-0.82 0.63 0.76 0.69 0.71
test 0.63 0.49-0.76 0.54 0.59 0.52 0.61
RandomForest train 1.00 1.00-1.00 0.95 1.00 1.00 0.96
test 0.74 0.61-0.87 0.50 0.82 0.70 0.66
ExtraTrees train 1.00 1.00-1.00 1.00 1.00 1.00 1.00
test 0.59 0.45-0.74 0.32 0.71 0.47 0.56
XGBoost train 0.99 1.00-1.00 0.98 0.985 0.983 0.985
test 0.72 0.59-0.85 0.61 0.67 0.61 0.67
LightGBM train 0.93 0.89-0.96 0.80 0.89 0.86 0.84
test 0.74 0.61-0.87 0.57 0.73 0.64 0.676
MLP train 0.80 0.74-0.85 0.59 0.86 0.78 0.71
test 0.78 0.67-0.89 0.50 0.79 0.67 0.66
VGG16 SVM train 0.92 0.88-0.95 0.76 0.89 0.85 0.81
test 0.76 0.63-0.90 0.82 0.77 0.74 0.84
KNN train 0.82 0.77-0.86 0.65 0.85 0.78 0.74
test 0.70 0.57-0.83 0.68 0.71 0.66 0.73
RandomForest train 1.00 1.00-1.00 0.96 1.00 1.00 0.97
test 0.67 0.54-0.81 0.50 0.71 0.58 0.63
ExtraTrees train 1.00 1.00-1.00 1.00 1.00 1.00 1.00
test 0.64 0.50-0.78 0.57 0.74 0.64 0.68
XGBoost train 1.00 1.00-1.00 0.99 1.00 1.00 0.99
test 0.71 0.57-0.84 0.57 0.74 0.64 0.68
LightGBM train 0.97 0.95-0.99 0.85 0.95 0.93 0.88
test 0.67 0.54-0.81 0.57 0.62 0.55 0.64
MLP train 0.85 0.80-0.90 0.66 0.82 0.76 0.74
test 0.79 0.67-0.90 0.75 0.79 0.75 0.79
ShuffleNet_v2 SVM train 0.92 0.89-0.96 0.79 0.92 0.89 0.84
test 0.81 0.70-0.92 0.55 0.91 0.84 0.71
KNN train 0.80 0.74-0.85 0.75 0.73 0.70 0.77
test 0.70 0.57-0.83 0.62 0.71 0.64 0.69
RandomForest train 1.00 1.00-1.00 0.94 1.00 1.00 0.95
test 0.65 0.51-0.79 0.45 0.82 0.68 0.64
ExtraTrees train 1.00 1.00-1.00 1.00 1.00 1.00 1.00
test 0.67 0.54-0.81 0.45 0.79 0.65 0.63
XGBoost train 1.00 1.00-1.00 1.00 1.00 1.00 1.00
test 0.77 0.65-0.89 0.55 0.85 0.76 0.69
LightGBM train 0.94 0.92-0.97 0.80 0.94 0.92 0.85
test 0.74 0.61-0.86 0.41 0.82 0.67 0.62
MLP train 0.84 0.80-0.89 0.67 0.90 0.85 0.76
test 0.81 0.69-0.92 0.45 0.97 0.93 0.67
ResNet18 SVM train 0.96 0.94-0.98 0.88 0.94 0.93 0.90
test 0.81 0.70-0.92 0.72 0.82 0.78 0.78
KNN train 0.86 0.82-0.90 0.62 0.91 0.86 0.74
test 0.64 0.51-0.78 0.52 0.68 0.58 0.62
RandomForest train 1.00 0.99-1.00 0.97 0.99 0.98 0.98
test 0.61 0.48-0.75 0.45 0.65 0.52 0.58
ExtraTrees train 1.00 1.00-1.00 1.00 1.00 1.00 1.00
test 0.61 0.47-0.75 0.45 0.79 0.65 0.63
XGBoost train 1.00 1.00-1.00 1.00 1.00 1.00 1.00
test 0.59 0.44-0.73 0.69 0.47 0.53 0.64
LightGBM train 0.96 0.94-0.98 0.80 0.96 0.94 0.85
test 0.59 0.44-0.73 0.31 0.59 0.39 0.50
MLP train 0.87 0.82-0.91 0.77 0.81 0.77 0.80
test 0.87 0.78-0.96 0.83 0.74 0.73 0.83
MobileNet_v2 SVM train 0.91 0.87-0.95 0.76 0.90 0.87 0.82
test 0.72 0.59-0.85 0.52 0.91 0.83 0.69
KNN train 0.78 0.72-0.83 0.69 0.75 0.70 0.74
test 0.59 0.46-0.73 0.35 0.65 0.46 0.54
RandomForest train 1.00 1.00-1.00 0.97 0.99 0.99 0.98
test 0.63 0.49-0.76 0.35 0.77 0.56 0.58
ExtraTrees train 1.00 1.00-1.00 1.00 1.00 1.00 1.00
test 0.57 0.42-0.71 0.41 0.65 0.50 0.56
XGBoost train 1.00 1.00-1.00 1.00 1.00 1.00 1.00
test 0.70 0.57-0.83 0.48 0.74 0.61 0.63
LightGBM train 0.94 0.91-0.97 0.83 0.94 0.92 0.87
test 0.70 0.57-0.83 0.45 0.88 0.77 0.65
MLP train 0.83 0.78-0.88 0.63 0.85 0.78 0.73
test 0.74 0.62-0.87 0.62 0.79 0.72 0.71
MnasNet-0.5 SVM train 0.87 0.83-0.92 0.61 0.92 0.86 0.74
test 0.65 0.52-0.79 0.38 0.74 0.55 0.58
KNN train 0.77 0.71-0.83 0.66 0.76 0.70 0.73
test 0.55 0.41-0.69 0.41 0.68 0.52 0.58
RandomForest train 1.00 1.00-1.00 0.97 0.99 0.99 0.97
test 0.58 0.44-0.72 0.41 0.65 0.50 0.56
ExtraTrees train 1.00 1.00-1.00 1.00 1.00 1.00 1.00
test 0.68 0.55-0.81 0.45 0.74 0.59 0.61
XGBoost train 1.00 1.00-1.00 1.00 0.99 0.99 1.00
test 0.66 0.51-0.80 0.55 0.79 0.70 0.68
LightGBM train 0.92 0.89-0.96 0.75 0.91 0.88 0.81
test 0.75 0.63-0.88 0.41 0.88 0.75 0.64
MLP train 0.79 0.73-0.84 0.54 0.85 0.76 0.69
test 0.65 0.50-0.79 0.35 0.65 0.46 0.54
GoogleNet SVM train 0.93 0.90-0.96 0.74 0.91 0.88 0.81
test 0.80 0.68-0.91 0.62 0.77 0.69 0.70
KNN train 0.80 0.74-0.85 0.69 0.80 0.75 0.75
test 0.66 0.53-0.80 0.59 0.77 0.68 0.68
RandomForest train 1.00 0.99-1.00 0.96 0.99 0.98 0.96
test 0.69 0.56-0.82 0.52 0.74 0.63 0.64
ExtraTrees train 1.00 1.00-1.00 1.00 1.00 1.00 1.00
test 0.65 0.51-0.78 0.55 0.65 0.57 0.63
XGBoost train 1.00 1.00-1.00 0.99 1.00 1.00 0.99
test 0.68 0.55-0.81 0.59 0.71 0.63 0.67
LightGBM train 0.95 0.92-0.97 0.79 0.93 0.91 0.84
test 0.74 0.61-0.86 0.69 0.68 0.65 0.72
MLP train 0.84 0.79-0.88 0.64 0.84 0.77 0.74
test 0.79 0.67-0.90 0.62 0.77 0.69 0.70
DenseNet121 SVM train 0.96 0.94-0.98 0.70 0.80 0.75 0.76
test 0.75 0.63-0.87 0.62 0.77 0.69 0.70
KNN train 0.82 0.77-0.87 0.97 0.99 0.99 0.97
test 0.72 0.60-0.85 0.38 0.79 0.61 0.60
RandomForest train 1.00 1.00-1.00 1.00 1.00 1.00 1.00
test 0.67 0.54-0.80 0.48 0.79 0.67 0.64
ExtraTrees train 1.00 1.00-1.00 1.00 1.00 1.00 1.00
test 0.69 0.56-0.82 0.69 0.68 0.65 0.72
XGBoost train 1.00 1.00-1.00 0.79 0.93 0.90 0.84
test 0.73 0.60-0.86 0.52 0.71 0.60 0.63
LightGBM train 0.95 0.92-0.97 0.70 0.85 0.79 0.77
test 0.72 0.59-0.84 0.62 0.74 0.67 0.69
MLP train 0.88 0.83-0.92 0.70 0.80 0.75 0.76
test 0.74 0.62-0.87 0.62 0.77 0.69 0.70
AlexNet SVM train 0.94 0.92-0.97 0.79 0.93 0.91 0.84
test 0.84 0.74-0.94 0.69 0.82 0.77 0.76
KNN train 0.83 0.78-0.88 0.76 0.79 0.75 0.79
test 0.62 0.47-0.76 0.62 0.59 0.56 0.65
RandomForest train 1.00 1.00-1.00 0.97 1.00 1.00 0.97
test 0.70 0.57-0.83 0.52 0.74 0.63 0.64
ExtraTrees train 1.00 1.00-1.00 1.00 1.00 1.00 1.00
test 0.59 0.45-0.73 0.31 0.71 0.47 0.55
XGBoost train 1.00 1.00-1.00 0.99 1.00 1.00 0.99
test 0.74 0.61-0.87 0.72 0.74 0.70 0.76
LightGBM train 0.95 0.93-0.98 0.78 0.95 0.93 0.84
test 0.62 0.48-0.77 0.59 0.71 0.63 0.67
MLP train 0.87 0.82-0.91 0.73 0.88 0.84 0.80
test 0.84 0.73-0.94 0.72 0.88 0.84 0.79