Table 3.

Representative research of AI for lung cancer treatment based on pathologic images.

Application	Author	Year	Sample size of WSIs	Algorithm	Task	Performance
Classification	Coudray et al.18	2018	1634	Deep CNN (Inception-V3)	Classification of LUAD, LUSC, or normal lung tissue and prediction of 10 most commonly mutated genes in LUAD	AUCs: 0.97 for classification, 0.733–0.856 for genomic prediction on testing dataset
	Khosravi et al.92	2018	12,139	CNN Inception-V1 and Inception-V3	Discrimination of LUAD and LUSC	AUC: 0.92 on testing dataset
	Wang et al.93	2020	939	Weakly Supervised Deep Learning	Classification of carcinoma types	Accuracy: 0.973 on testing dataset
	Yang et al. 94	2021	741, 318, and 212 from two hospitals, and 422 from TCGA	EfficientNet-B5- and ResNet-50-based deep learning methods	Classification of LUAD, LUSC, SCLC, pulmonary tuberculosis, organizing pneumonia, and normal lung tissue	AUCs: 0.918–0.978 on testing dataset
	Chen et al.95	2021	9662	Deep learning	Classification of lung cancer types	AUCs: 0.9594 for LUAD, and 0.9414 for LUSC on testing dataset
	Zheng et al.96	2021	4818	Graph-transformer	Classification of LUAD, LUSC, or normal lung tissue	Accuracy: 0.912 on five-fold cross-validation
	Wang et al.97	2019	1337 from TCGA, 345 from NLST, 102 from CHCAMS, and 130 from SPORE	CNN	Transformation of pathological images	Accuracy: 0.901 on independent testing dataset
Enhancement of Oncology Care	Yu et al.98	2016	2186 from TCGA, and 294 from TMA	Machine learning	Prognosis prediction	AUC: 0.81 on testing dataset
	Fu et al.99	2020	17,355 histopathology slide images from 28 cancer types	Inception-V4 deep learning and transfer learning	Classification of cancer types	Average AUC: 0.99 for 14 cancers on held-back validation dataset
	Kapil et al.100	2021	151	Deep learning	Assessment of PD-L1 expression and survival analysis	C-index: 0.93 on testing dataset
	Qaiser et al.101	2022	1122	Weakly supervised CNN	Prediction of disease outcome	C-index: 0.7033 on testing dataset
	Choi et al.102	2022	802	Deep learning	Assessment of PD-L1 expression	C-index: 0.902 for pathologists with AI assistance
	Lee et al.27	2022	3950 patients with kidney, breast, lung, and uterine cancers	Graph DNN	Prognosis prediction	C-index: 0.731 on NLST dataset, 0.709 on TCGA dataset
	Chen et al.103	2022	6592 gigapixel WSIs from 5720 patient samples across 14 cancer types from the TCGA	AMIL network for processing WSIs, SNN for processing molecular data features, and deep-learning-based MMF	Prognosis prediction	C-index: 0.578 for AMIL, 0.606 for SNN, and 0.644 for MMF on five-fold cross-validation

AI: Artificial intelligence; AMIL: Attention-based multiple-instance learning; AUC: Area under the curve; CHCAMS: Chinese Academy of Medical Sciences; CNNs: Convolutional neural networks; DNN: Deep neural networks; LSCC: Lung squamous cell carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; MMF: Multimodal fusion; NLST: National Lung Screening Trial; NSCLC: Non-small cell lung cancer; PD-L1: Programed death ligand-1; SCLC: Small cell lung cancer; SNN: Self-normalizing network; SPORE: Specialized Programs of Research Excellence; TCGA: The Cancer Genome Atlas; TMA: Stanford tissue microarray; WSIs: Whole slide images.