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Table 2 The performance indicators of three 3D-CNNs

From: Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules

Model

Accuracy

Sensitivity

Specificity

PPV

NPV

Training set

     

CT 3D-CNN

0.84 ± 0.03

0.90 ± 0.07

0.62 ± 0.16

0.90 ± 0.04

0.63 ± 0.15

PET 3D-CNN

0.92 ± 0.02

0.97 ± 0.03

0.76 ± 0.15

0.94 ± 0.04

0.88 ± 0.09

PET/CT 3D-CNN

0.93 ± 0.01

0.98 ± 0.01

0.76 ± 0.06

0.94 ± 0.02

0.90 ± 0.09

Testing set

     

CT 3D-CNN

0.67

0.70

0.50

0.86

0.27

PET 3D-CNN

0.76

0.85

0.33

0.85

0.33

PET/CT 3D-CNN

0.85

0.96

0.33

0.87

0.67

  1. The results in the table are expressed as mean ± SD
  2. 3D-CNN, three-dimensional convolutional neural network; PPV, positive predictive value; NPV, negative predictive value