Table 2.

Examples of applications of AI and radiomics in lung cancer.

Clinical application	Author and year	Tumor type	Image modality	Algorithm	Outcome
Early detection
Classify cancerous nodules	Hawkins 201668	Benign and malignant nodules	CT	Random forest classifier	AUC: 0.83
	Ardila D 201982	Benign and malignant nodules	CT	Three-dimensional CNN model	AUC: 0.944
	Baldwin 202025	Benign and malignant nodules in 5 to 15 mm	CT	CNN model	AUC: 0.896
Characterization of lung cancer
Classify histology subtype	Linning 201892	AD, SCLC, SCC	CT	SVM	AUC: 0.741 and 0.822 for SCLC and NSCLC, AD and SCLC etc.
	Wu 201693	AD, SCC	CT	Naive Bayes' classifier	AUC: 0.72
	Wang 202094	AD	CT	CNN model combined with radiomic features	AUC: 0.861
Classify somatic mutations	Velazquez 201796	NSCLC	CT	Random forest classifier	AUC: 0.80 and 0.69 for EGFR+ and KRAS+, and EGFR+ and EGFR− etc.
	Wang 201997	AD	CT	CNN model derived from DenseNet	AUC: 0.81 for EGFR− and EGFR+
Prognosis prediction
Predict outcomes after surgery or radiation therapy	Wu 2016107	NSCLC	PET/CT	LASSO with Cox survival model	Prognostic CI: 0.71
	Hosny 201822	NSCLC	CT	3D CNN model	AUC: 0.70 and 0.71 for surgery and radiotherapy
Predict response to chemotherapy	Wei 2019108	SCLC	CT	Regression	AUC: 0.797
Predict response to targeted therapy	Song 2018112	NSCLC	CT	Cox regression	AUC: 0.71
Predict response to immunotherapy	Sun 2018120	Advanced solid malignant tumor	CT	Regression	AUC: 0.67 (95% CI: 0.57–0.77)
	He 2020121	Advanced NSCLC	CT	3D DenseNet for feature extraction and fully connected network as classifier	OS: HR: 0.54, 95% CI: 0.31–0.95

Abbreviations: CT, computed tomography; AUC, area under curve; CNN, convolutional neural network; AD, adenocarcinoma; SCC, squamous cell carcinoma; SCLC, small cell lung cancer; NSCLC, nonsmall cell lung cancer; EGFR⁻/EGFR⁺, epidermal growth factor receptor negative/positive; PET/CT, positron emission tomography/computed tomography; LASSO, least absolute shrinkage and selection operator.