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Table 2 Performance of segmentation models in the validation cohorts

From: Real-time automatic prediction of treatment response to transcatheter arterial chemoembolization in patients with hepatocellular carcinoma using deep learning based on digital subtraction angiography videos

Cohort

Model

Dice

Accuracy

Patient-level sensitivity

Specificity

PPV

NPV

Lesion-level sensitivity

FPR

Internal validation cohort

Baseline

0.71 (0.68–0.74)

96.8 (96.4–97.2)

80.2 (77.6–82.7)

98.2 (97.8–98.5)

75.5 (72.3–78.5)

98.1 (97.8–98.4)

84.8 (81.9–87.8)

34.0 (30.5–37.4)

Baseline + TDL

0.72 (0.70–0.75)

96.7 (96.3–97.0)

83.3 (81.0–85.6)

97.7 (97.3–98.0)

73.4 (70.2–76.3)

98.4 (98.2–98.7)

81.8 (78.6–85.0)

32.2 (28.8–35.5)

Baseline + LRS

0.73 (0.70–0.76)

97.0 (96.6–97.4)

80.0 (77.3–82.8)

98.5 (98.2–98.7)

75.5 (72.3–78.4)

98.0 (97.7–98.4)

83.2 (80.1–86.3)

22.7 (19.3–26.1)

FFS

0.75 (0.73–0.78)

97.1 (96.8–97.5)

82.3 (79.8–84.8)

98.4 (98.1–98.6)

77.9 (75.0–80.6)

98.3 (97.9–98.6)

87.2 (84.4–89.9)

23.8 (20.4–27.3)

External validation cohort

Baseline

0.71 (0.68–0.73)

96.8 (96.5–97.2)

73.1 (70.5–75.5)

99.3 (99.2–99.4)

83.3 (81.3–85.5)

97.2 (96.8–97.6)

90.1 (87.7–92.5)

34.7 (31.7–37.7)

Baseline + TDL

0.72 (0.70–0.75)

96.6 (96.3–97.0)

86.6 (84.8–88.4)

97.9 (97.6–98.1)

71.0 (68.5–73.7)

98.5 (98.1–98.8)

92.0 (89.8–94.2)

44.0 (40.9–47.0)

Baseline + LRS

0.71 (0.69–0.74)

96.9 (96.6–97.2)

78.2 (75.6–80.4)

98.7 (98.5–98.9)

77.0 (74.2–79.6)

97.9 (97.6–98.2)

92.5 (90.4–94.7)

34.1 (31.0–37.3)

FFS

0.73 (0.71–0.75)

97.1 (96.7–97.4)

79.0 (76.4–81.1)

98.7 (98.5–98.9)

79.6 (76.9–82.0)

98.1 (97.8–98.4)

94.3 (92.4–96.2)

30.8 (27.9–33.7)

  1. The data in parentheses are 95% confidence interval
  2. TDL Temporal difference learning, LRS Liver region segmentation, FFS Final fusion segmentation, PPV Positive predictive value, NPV Negative predictive value, FPR False-positives ratio