Table 2.

Main properties and results of each artificial intelligence model

Model	Type of features	Number of radiological featuresa	Number of relevant radiological featuresa	Test set Accuracy (mean ± standard deviation)	Test set AUC (mean ± standard deviation)
Model1	RF1 (2L)	21	10	0.713 ± 0.004	0.768 ± 0.032
Model2	QM (2L and 4 GS)	102	26	0.724 ± 0.006	0.800 ± 0.026
Model3	RF1 + RF2 (2L)	141	24	0.776 ± 0.003	0.867 ± 0.008
Model4	RF1 + QM (2L and 4 GS) + RF2 (2L)	241	32	0.796 ± 0.005	0.870 ± 0.011

Principal characteristics of each model developed. The type of features, the number of initial radiological features, and the final relevant radiological features after LASSO regression used for building each classifier are reported together with results of accuracy and AUC obtained in the test set. Mean and standard deviation values of results were calculated after a 4-fold cross-validation iterated ten times. ^aPatient age and sex were added as clinical metrics in all models. AUC Area under the receiving operating characteristic curve, 2L Two lungs, GS Geometrical subdivisions, QM Quantitative metrics, RF1 First-order radiomic features, RF2 Second-order radiomic features