Table 2 Test set performance accuracy and 95% confidence intervals of hand- versus automated-optimized models for 1st and 2nd segmentation with radiologist comparison.

From: Automated machine learning for differentiation of hepatocellular carcinoma from intrahepatic cholangiocarcinoma on multiphasic MRI

 

ROC AUC

PR AUC

Accuracy

P value

Sensitivity

P value

Specificity

P value

Kappa

Segmentation 1

Radiomics pipeline (VBFS + LR)

0.80

0.81

0.73 (0.59–0.84)

0.05

0.75 (0.53–0.89)

0.58

0.71 (0.52–0.85)

0.007

0.45

TPOT

0.76

0.76

0.73 (0.59–0.84)

0.05

0.65 (0.43–0.82)

0.10

0.79 (0.61–0.90)

0.25

0.44

Segmentation 2

Radiomics pipeline (VBFS + LR)

0.79

0.80

0.75 (0.61–0.85)

0.11

0.70 (0.48–0.86)

0.26

0.79 (0.61–0.90)

0.25

0.49

TPOT

0.79

0.77

0.75 (0.61–0.85)

0.11

0.75 (0.53–0.89)

0.58

0.75 (0.56–0.88)

0.08

0.49

Radiologist 1

NA

NA

0.77 (0.63–0.87)

0.11

0.75 (0.53–0.89)

0.58

0.79 (0.61–0.90)

0.25

0.53

Radiologist 2

NA

NA

0.83 (0.70–0.91)

0.56

0.80 (0.58–0.93)

1.00

0.86 (0.68–0.95)

0.78

0.66

Radiologist 3

NA

NA

0.88 (0.76–0.95)

0.69

0.80 (0.58–0.93)

1.00

0.93 (0.76–0.99)

0.57

0.74

Radiologist 4

NA

NA

0.88 (0.76–0.95)

0.69

0.85 (0.63–0.96)

0.78

0.89 (0.72–0.97)

0.79

0.74

Mean radiologist

NA

NA

0.84 (0.71–0.92)

1.00

0.80 (0.58–0.93)

1.00

0.87 (0.69–0.96)

1.00

NA