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We have recomputed the classification results with argmax prediction rule, where the threshold for binary classification is 0.5, and they are different from the results in our paper, which are computed based on the cut-off value in ROC.
The performance in prediction of ALN status (N0 vs. N(+))
Methods
AUC
Accuracy (%)
Precision (%)
Recall (%)
F1-score (%)
Clinical data only
T
0.661
64.13
52.46
62.08
56.87
V
0.709
68.10
56.52
65.82
60.82
I-T
0.613
57.80
45.92
53.57
49.45
DL-CNB model
T
0.901
81.43
72.53
82.50
77.19
V
0.808
71.43
66.10
49.37
56.52
I-T
0.816
70.18
67.27
44.05
53.24
DL-CNB+C model
T
0.878
78.89
70.82
75.83
73.24
V
0.823
74.76
64.13
74.68
69.01
I-T
0.831
76.15
70.51
65.48
67.90
The performance in prediction of ALN status (N0 vs. N + (1-2))
Methods
AUC
Accuracy (%)
Precision (%)
Recall (%)
F1-score (%)
Clinical data only
T
0.638
61.58
34.08
59.38
43.30
V
0.677
64.57
36.76
56.82
44.64
I-T
0.627
61.05
29.58
55.26
38.53
DL-CNB model
T
0.912
81.66
66.67
51.56
58.15
V
0.756
70.86
42.86
47.73
45.16
I-T
0.845
80.23
54.55
63.16
58.54
DL-CNB+C model
T
0.936
85.71
75.00
63.28
68.64
V
0.789
77.14
61.11
25.00
35.48
I-T
0.878
84.88
77.27
44.74
56.67
The performance in prediction of ALN status (N0 vs. N + (>2))