Table 1.

Classifications component in CADe schemes for detection of lung nodules in CT.

Study	Database	Classifier/Method	Performance
Giger et al. [75]	Thick-slice diagnostic CT scans of 8 patients with 47 nodules	Comparison of geometric features	Sensitivity of 94% with 1.25 FPs per case
Armato et al. [13, 27]	Thick-slice (10 mm) diagnostic CT scans of 43 patients with 171 nodules	Rule-based scheme and LDA with 9 2D and 3D features	Sensitivity of 70% with 42.2 FPs per case in an LOO test
Kanaza wa et al. [76]	Thick-slice (10 mm) screening CT scans of 450 patients with 230 nodules	Rule-based scheme	Sensitivity of 90%
Gurcan et al. [77]	Thick-slice (2.5–5 mm) diagnostic CT scans of 34 patients with 63 nodules	Rule-based scheme and LDA with 6 2D and 3D features	Sensitivity of 84% with 74.4 FPs per case in an LOO test
Lee et al. [78]	Thick-slice (10 mm) diagnostic CT scans of 20 patients with 98 nodules	Rule-based scheme and LDA with 13 features	Sensitivity of 72% with 30.6 FPs per case
Suzuki et al. [60]	Thick-slice (10 mm) screening LDCT scans of 63 patients with 71 nodules with solid, part-solid and non-solid patterns, including 66 cancers	Multiple MTANNs with pixel values in a 9×9 subregion (local window or patch) as input	Sensitivity of 80.3% with 4.8 FPs per case in a validation test
Arimura et al. [12]	106 thick-slice (10 mm) screening LDCT scans of 73 patients with 109 cancers with solid, part-solid and non-solid patterns	Rule-based scheme followed by multiple MTANNs with pixel values in a 9×9 subregion as input (or LDA with Wilks’ lambda stepwise feature selection)	Sensitivity of 83% with 5.8 FPs per case in a validation test (or an LOO test for LDA)
Farag et al. [82]	Thin-slice (2.5 mm) screening LDCT scans of 16 patients with 119 nodules and 34 normal patients	Template modeling approach using level sets	Sensitivity of 93.3% with an FP rate of 3.4%
Ge et al. [83]	82 thin-slice (1.0–2.5 mm) CT scans of 56 patients with 116 solid nodules	LDA with Wilks’ lambda stepwise feature selection from 44 features	Sensitivity of 80% with 14.7 FPs per case in an LOO test
Matsumoto et al. [84]	Thick-slice (5 or 7 mm) diagnostic CT scans of 5 patients (4 of which used contrast media) with 50 nodules	LDA with 8 features	Sensitivity of 90% with 64.1 FPs per case in an LOO test
Yuan et al. [85]	Thin-slice (1.25 mm) CT scans of 150 patients with 628 nodules	ImageChecker CT LN-1000 by R2 Technology	Sensitivity of 73% with 3.2 FPs per case in an independent test
Bi et al. [86]	HRCT scans of 86 patients with 48 nodules	Asymmetric cascade of classifiers with column generation boosting feature selection	Sensitivity of 88% with 0.7 FPs per case in a validation test
Pu et al. [87]	Thin-slice (2.5 mm) screening CT scans of 52 patients with 184 nodules including 16 non-solid nodules	Scoring method based on the similarity distance combined with a marching cube algorithm	Sensitivity of 81.5% with 6.5 FPs per case
Retico et al. [88]	Thin-slice (1 mm) screening CT scans of 39 patients with 102 nodules	Voxel-based neural approach (MTANN) with pixel values in a subvolume as input	Sensitivities of 80–85% with 10–13 FPs per case
Ye et al. [89]	Thin-slice (1 mm) screening CT scans of 54 patients with 118 nodules including 17 non-solid nodules	Rule-based scheme followed by a weighted SVM with 15 features	Sensitivity of 90.2% with 8.2 FPs per case in an independent test
Golosio et al. [90]	Thin-slice (1.5–3.0 mm) CT scans of 83 patients with 148 nodules that one radiologist detected from LIDC database	Fixed-topology ANN with 42 features from multithreshold ROI	Sensitivity of 79% with 4 FPs per case in an independent test
Murphy et al. [92]	Thin-slice screening CT scans of 813 patients with 1,525 nodules	k-nearest-neighbor classifier with features selected from 135 features	Sensitivity of 80 with 4.2 FPs per case in an independent test
Tan et al. [93]	Thin-slice CT scans of 125 patients with 80 nodules that 4 radiologists agreed from LIDC database	Feature-selective classifier based on a genetic algorithm and ANNs with 45 initial features	Sensitivity of 87.5% with 4 FPs per case in an independent test
Messay et al. [94]	Thin-slice CT scans of 84 patients with 143 nodules from LIDC database	LDA and QDA with feature selection from 245 features	Sensitivity of 83% with 3 FPs per case in a 7-fold cross-validation test
Riccardi et al. [95]	Thin-slice CT scans of 154 patients with 117 nodules that 4 radiologists agreed on from LIDC database	Heuristic approach and SVM with maximum-intensity projection data from a volume of interest	Sensitivity of 71% with 6.5 FPs per case in a 2-fold cross-validation test