Table 1.

Representative research of AI for lung cancer diagnosis based on CT images.

Application	Authors	Year	Dataset/sample size	Imaging modality	Algorithm	Task	Performance
Nodule detection	Gu et al. 30	2018	1186 nodules in the LUNA16 database	CT	3D deep CNN combined with a multi-scale prediction strategy	Automatic lung nodule detection	Sensitivity: 0.9293 on testing dataset
	Xu et al. 31	2019	1590 nodules	CT	Multiple neural network models	Automatic lung nodule detection	Sensitivity: 0.9695 and 0.9117 for ThinSet and ThickSet on testing dataset
Malignancy evaluation	Ardila et al. 16	2019	6716 patients from NLST and 1139 cases for validation	CT	End-to-end three-dimensional deep learning	Malignancy prediction and risk bucket score of lung nodules	AUC: 0.944 on held-out NLST testing set
	Xu et al.32	2019	548 nodules from LIDC-IDRI dataset	CT	MSCS neural networks – DeepLN	Malignancy evaluation of lung nodules	AUC: 0.94 on testing dataset
	Massion et al.33	2020	14,761 benign nodules and 932 malignant nodules	CT	LCP-CNN	Risk stratification of lung nodules	AUC: 0.921 on internal validation dataset
	Baldwin et al.34	2020	1397 nodules (5–15 mm)	CT	LCP-CNN	Malignancy evaluation of lung nodules	AUC: 0.896 on external validation dataset
	Venkadesh et al.35	2021	16,077 nodules from NLST	CT	Deep learning	Malignancy risk estimation of lung nodules	AUC: 0.93 on external validation dataset
	Shi et al.36	2021	3038 nodules	CT	Semi-supervised Deep Transfer Learning	Benign–malignant lung nodule diagnosis	AUC: 0.795 on independent testing dataset
	Park et al.37	2021	359 patients	CT and FDG PET/CT	Deep learning classification models based on ResNet-18	Malignant lung nodule diagnosis	AUC: 0.837 on five-fold cross-validation
	Shao et al.38	2022	12,360 participants	CT	Deep learning	Detection and risk stratification of lung nodules	AUC: 0.8516 on testing dataset
Subtype classification	Wu et al.39	2016	350 patients	CT	Radiomics	Histologic subtypes (LUAD vs. LUSC) prediction	The highest AUC: 0.72 on validation dataset
	Zhao et al.40	2018	651 nodules	CT	Dense Sharp Network	Tumor invasiveness prediction	ACC: 0.641 on testing dataset
	Hyun et al.41	2019	396 patients	18F-FDG PET/CT	4 clinical features (age, sex, tumor size, and smoking status) and 40 radiomic features	Histologic subtypes (LUAD vs. LUSC) prediction	AUC: 0.859 on testing dataset
	Han et al.42	2021	867 LUAD and 552 LUSC patients	PET/CT	10 feature selection techniques, 10 machine learning models, and the VGG16 deep learning algorithm	Histologic subtypes (LUAD vs. LUSC) prediction	The highest AUC: 0.903 on testing dataset
	Ren et al.43	2021	315 NSCLC patients	PET/CT	Clinico-biological features and FDG-PET/CT radiomic-based nomogram via machine learning	Histologic subtypes (LUAD vs. LUSC) prediction	AUC: 0.901 on validation dataset
	Wang et al.44	2021	1222 patients with LUAD	CT	Deep learning and radiomics	Adenocarcinoma subtype classification	AUC: 0.739–0.940 on internal validation dataset
	Choi et al.45	2021	817 patients with clinical stage I LUAD	CT	Deep learning	Prediction of visceral pleural invasion	AUC: 0.75 on temporal validation dataset
	Zhong et al.46	2022	3096 patients	CT	Deep learning	N2 metastasis prediction and prognosis stratification	AUC: 0.81 on prospective testing dataset

AI: Artificial intelligence; ACC: Accuracy; AUC: Area under the curve; CNN: Convolutional neural network; ¹⁸F-FDG PET/CT: Fluorine-18-fluorodeoxyglucose (FDG) positron emission tomography (PET) computed tomography (CT); FROC: Free-response receiver operating characteristic; IPNs: Indeterminate pulmonary nodules; LCP-CNN: Lung Cancer Prediction-Convolutional Neural Network; LIDC: Lung Image Database Consortium; IDRI: Image Database Resource Initiative; LUAD: Lung adenocarcinoma; LUNA16: Lung Nodule Analysis 16; LUSC: Lung squamous cell carcinoma; MSCS: Multi-scale cost-sensitive; NLST: National Lung Screening Trial; NSCLC: Non-small cell lung cancer.