Table 3 Summary estimate of pooled performance of artificial intelligence in image-based lung cancer diagnosis

From: Systematic review and meta-analysis of artificial intelligence for image-based lung cancer classification and prognostic evaluation

Subgroup

No. of studies (datasets)

Tau2

Sensitivity

Specificity

P valueb

   

Sensitivity

P valuea

I2 (95%CI)

Specificity

P valuea

I2 (95%CI)

 

Overall

209 (251)

0.0063

0.86 (0.84–0.87)

< 0.05

94.71 (94.30–95.12)

0.86 (0.84–0.87)

< 0.05

97.35 (97.19–97.51)

 

Objective

        

0.0001

Malignant/benign

128 (151)

0.0058

0.88 (0.86–0.90)

< 0.05

96.53 (96.23–96.84)

0.88 (0.85–0.90)

< 0.05

98.37 (98.26–98.48)

 

ADC/SCC

19 (22)

0.0243

0.81 (0.76–0.85)

< 0.05

77.03 (67.73–86.33)

0.80 (0.74–0.84)

< 0.05

76.20 (66.47–85.93)

 

Invasive/pre-invasive

16 (23)

0.0256

0.86 (0.82–0.89)

< 0.05

62.91 (46.28–79.55)

0.82 (0.79–0.84)

< 0.05

40.33 (10.58–70.08)

 

EGFR mutant/wild

46 (55)

0.0291

0.78 (0.75–0.81)

< 0.05

73.50 (66.50–80.51)

0.81 (0.77–0.84)

< 0.05

85.49 (82.25–88.73)

 

Cohort

        

0.0231

Internal validation cohort

(195)

0.0113

0.86 (0.85–0.88)

< 0.05

93.93 (93.38–94.48)

0.86 (0.84–0.88)

< 0.05

96.54 (96.28–96.80)

 

External validation cohort

(56)

0.0622

0.82 (0.78–0.86)

< 0.05

96.35 (95.81–96.88)

0.84 (0.79–0.88)

< 0.05

98.61 (98.47–98.76)

 

Algorithm

        

0.0630

Machine learning

114 (136)

0.0616

0.84 (0.82–0.86)

< 0.05

88.58 (87.07–90.08)

0.83 (0.81–0.86)

< 0.05

96.35 (96.01–96.69)

 

Deep learning

95 (115)

0.0054

0.87 (0.85–0.89)

< 0.05

96.98 (96.69–97.27)

0.87 (0.85–0.89)

< 0.05

97.41 (97.18–97.65)

 

3D deep learning

19 (22)

0.0845

0.87 (0.82–0.90)

< 0.05

97.26 (96.68–97.85)

0.89 (0.85–0.92)

< 0.05

96.31 (95.43–97.18)

 

2D deep learning

76 (93)

0.0018

0.87 (0.85–0.90)

< 0.05

96.70 (96.34–97.06)

0.87 (0.84–0.89)

< 0.05

97.71 (97.48–97.93)

 

Imaging variable only

        

0.7667

Yes

133 (155)

0.0079

0.87 (0.85–0.88)

< 0.05

96.07 (95.65–96.48)

0.87 (0.84–0.89)

< 0.05

98.36 (98.25–98.47)

 

No

76 (96)

0.0315

0.83 (0.81–0.85)

< 0.05

84.28 (81.57–86.99)

0.83 (0.80–0.85)

< 0.05

85.52 (83.08–87.96)

 

Nodule segmentation

        

0.7535

Manual

78 (94)

0.0331

0.84 (0.82–0.86)

< 0.05

85.31 (82.80–87.82)

0.82 (0.80–0.85)

< 0.05

89.91 (88.36–91.46)

 

Algorithm

131 (157)

0.008

0.86 (0.84–0.88)

< 0.05

96.32 (96.00–96.64)

0.87 (0.84–0.89)

< 0.05

98.22 (98.09–98.34)

 

Image quality control

        

0.1355

Yes

63 (72)

0.019

1.00 (0.99–1.00)

< 0.05

95.14 (94.46–95.83)

0.85 (0.82–0.88)

< 0.05

97.93 (97.71–98.15)

 

No

145 (179)

0.01

0.86 (0.84–0.88)

< 0.05

94.97 (94.52–95.43)

0.86 (0.83–0.87)

< 0.05

96.54 (96.27–96.82)

 
  1. aP-Value for heterogeneity within each subgroup.
  2. bP-Value for heterogeneity between subgroups with multivariable meta-regression analysis.
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