Table 2.

Classification between malignant and benign nodules (CADx) for thoracic CT.

Study	Database	Classifier/Method	Performance
McNitt-Gray et al. [103]	HRCT scans of 17 malignant and 14 benign nodules	LDA with stepwise feature selection	Correct classification rate of 90.3%
Matsuki et al. [104]	HRCT scans of 99 malignant and 56 benign nodules	ANN with 16 radiologists’ subjective features and 7 clinical data	AUC value of 0.951 in an LOO test
Aoyama et al. [113]	Thick-slice (10 mm) screening LDCT scans of 76 malignant and 413 benign nodules	LDA with Wilks’ lambda stepwise feature selection	AUC of 0.846 in an LOO test
Mori et al. [105]	Thin-slice (2 mm) CE-CT scans of 35 malignant and 27 benign nodules	LDA with 3 features	AUC of 0.91 and 1.0 with non-CE CT and CE-CT, respectively, in an LOO test
Shah et al. [106]	Thin-slice (≤ 3 mm) CE-CT scans of 19 malignant and 16 benign nodules	Logistic regression or QDA with stepwise feature selection from 31 features	AUC of 0.92 with QDA in an LOO test
Suzuki et al. [65]	Thick-slice (10 mm) screening LDCT scans of 76 malignant and 413 benign nodules	Multiple MTANNs with pixel values in a 9×9 subregion as input	AUC of 0.88 in an LOO test
Iwano et al. [107]	HRCT (0.5–1 mm slice) scans of 52 malignant and 55 benign nodules	LDA with 2 features	Sensitivity of 76.9% and a specificity of 80%
Way et al. [108]	CT scans of 124 malignant and 132 benign nodules in 152 patients	LDA or SVM with stepwise feature selection	AUC of 0.857 in an LOO test
Chen et al. [109]	CT (slice thickness of 2.5 or 5 mm) scans of 19 malignant and 13 benign nodules	ANN ensemble with selected features	AUC of 0.915 in an LOO test
Lee et al. [110]	Thick-slice (5 mm) CT scans of 62 malignant and 63 benign nodules	GA-based feature selection and a random subspace method	AUC value of 0.889 in an LOO test