Table 1. Summary of recent studies with radiomic models to identify benign vs. malignant pulmonary nodules.

Study	Number of scans (benign vs. malignant)	Conventional radiomics vs. deep learning	Number of features/model description	Internal vs. external validation	Model’s performance
Chen et al. (18)	33 benign 42 malignant	Conventional radiomics	- Support vector machine (SVM) was used as the classifier - 76 out of 750 features were significantly different between benign and malignant nodules - Accuracy for the selected 4-feature signature (SFS) was the highest	Internal	For SFS: Accuracy: 84% Sensitivity: 92.85% Specificity: 72.73%
Ardila et al. (19)	Training dataset from NLST: 6,630 benign 86 malignant Independent validation set: 1,112 benign 27 malignant	Deep convolutional neural network	-1,024 radiomics features - compared to expert radiologists	External	AUC of training dataset: 0.944 AUC of validation dataset: 0.955
Delzell et al. (20)	90 benign 110 malignant	Conventional radiomics	- 416 radiomic features - Combinations of the six feature selection methods and twelve classifiers were investigated by implementing a 10-fold repeated cross-validation framework with five repeats	Internal	Values for the best selection method and classifier combination: AUC: 0.747 Sensitivity: 61.6% Specificity: 72.9%
Hawkins et al. (21)	NLST dataset: 328 benign 170 malignant	Conventional radiomics	- 219 radiomic features with best model identifying 23 stable features - J48, JRIP (RIPPER), Naïve Bayes, support vector machines (SVMs), and random forest(s) classifiers tested	Internal	Best models used random forests classifier with accuracy of predicting nodules becoming cancerous in 1 and 2 years: 80% (AUC 0.83) and 79% (AUC 0.75), respectively.
He et al. (22)	60 benign 180 malignant Total: 240 (120 in primary cohort, 120 in validation cohort)	Conventional radiomics	- 150 radiomic features - Least Absolute Shrinkage and Selection Operator Method (LASSO) logistic regression model used - Divided into four groups: Group 1 = non-contrast + 1.25 mm + standard convolution kernel; Group 2 = contrast enhancement + 1.25 mm + standard convolution kernel; Group 3 = non-contrast + 5 mm + standard convolution kernel; Group 4 = non-contrast + 5 mm + lung convolution kernel	Internal	Group 1 had best performance: AUC: 0.862 Primary cohort: Sensitivity: 94.4% Specificity: 63.3% Accuracy: 85.8% Validation cohort: Sensitivity: 92.2% Specificity: 56.7% Accuracy: 83.3%
Peikert et al. (23)	NLST dataset 318 benign 408 malignant	Conventional radiomics	- LASSO logistic regression model used - 8 out of 57 features selected	Internal	AUC: 0.939
Uthoff et al. (24)	Training cohort: 289 benign 74 malignant Validation cohort: 50 benign 50 malignant	Machine learning/Artificial neural network	- Features of parenchyma surrounding the nodule were included	Internal and External	Best performing tool’s performance on validation cohort: AUC: 0.965 Accuracy: 98% Sensitivity: 100% Specificity: 96%
Xu et al. (25)	192 benign 181 malignant	Conventional radiomics	- 1160 radiomic features - Lesions classified in 3 groups based on size: T1a, T1b and T1c - Developed 3 radiomic models to predict malignancy in each group - Fivefold cross-validation was used	Internal	Model 1 for T1a: AUC: 0.84 Accuracy: 77% Sensitivity: 89% Specificity: 74% Model 2 for T1b: AUC: 0.78 Accuracy: 73% Sensitivity: 74% Specificity: 71% Model 3 for T1c: AUC: 0.79 Accuracy: 76% Sensitivity: 77% Specificity: 73%
Mao et al. (26)	Training cohort: 156 benign 40 malignant Validation cohort: 75 benign 23 malignant	Conventional radiomics	- 11 out of 385 radiomic features identified - LASSO logistic regression model used	Internal	Training cohort: AUC: 0.953 Validation cohort: AUC: 0.97 Accuracy: 89.8% Sensitivity: 81% Specificity: 92.2%
Choi et al. (27)	31 benign 41 malignant	Conventional radiomics	- 103 radiomic features - SVM-LASSO model with ten-fold cross validation - Best model had 2 radiomic features	Internal	AUC: 0.89 Accuracy: 84.6%