Application of convolutional neural network in detecting and classifying gastric cancer

Table 1 Detailed information on studies concerning automatic detection by convolutional neural network in gastric cancer

Ref.	Endoscopic images	Training dataset	Test dataset	Resolution	Sensitivity %	Specificity %	Accuracy/AUC %	PPV %	NPV %
Hirasawa et al[10] (2018)	WLI/NBI/chromoendoscopy images	13584	2296	300 × 300	92.2	NA	NA	30.6	NA
Ishioka et al[21] (2019)	Video images	NA	68	NA	94.1	NA	NA	NA	NA
Li et al[23] (2020)	M-NBI images	20000	341	512 × 512	91.18	90.64	90.91	90.64	91.18
Ikenoyama et al[24](2021)	WLI/NBI/chromoendoscopy images	13584	2940	300 × 300	58.4	87.3	75.7	26.0	96.5

AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value; WLI: White-light imaging; NBI: Narrow-band imaging; M-NBI: Magnifying narrow-band imaging; NA: Not applicable.

Table 2 Detailed information on studies concerning histological classification by convolutional neural network in gastric cancer

Ref.	Training dataset	Test dataset	Resolution	Group	AUC %
Cho et al[25] (2019)	4205	812	1280 × 640	Five-category classification	84.6
				Cancer vs non-cancer	87.7
				Neoplasm vs non-neoplasm	92.7
Sharma et al[27] (2017)	231000 for cancer classification	NA	512 × 512	Cancer classification	69.9
	47130 for necrosis detection			Necrosis detection	81.4
Iizuka et al[28] (2020)	3628	500	512 × 512	Adenocarcinoma	98
				Adenoma	93.6
Song et al[29] (2020)	2123	3212 from PLAGH	320 × 320	Benign and malignant cases and tumour subtypes	98.6
		595 from PUMCH			99.0
		987 from CHCAMS			99.6

PLAGH: Chinese PLA General Hospital; PUMCH: Peking Union Medical College Hospital; CHCAMS: Cancer Hospital, Chinese Academy of Medical Sciences; AUC: Area under the curve.

Table 3 Detailed information on studies concerning prediction of depth of tumor invasion by convolutional neural network in gastric cancer

Ref.	Dataset	Resolution	Sensitivity %	Specificity %	Accuracy/AUC %	PPV %	NPV %
Zhu et al[11] (2019)	Development datasets: 5056; Validation datasets: 1264; Test dataset: 203	299 × 299	76.47	95.56	89.16	89.66	88.97
Yoon et al[32] (2019)	11539 images were randomly organized into five different folds, and at each fold, the training: validation: testing dataset ratio was 3:1:1	NA	79.2	77.8	85.1	79.3	77.7
Zheng et al[34] (2020)	Totally 5855, training:verification dataset ratio was 4:1	512 × 557	NA	NA	T2 stage: 90; T3 stage: 93; T4 stage: 95	NA	NA

AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value; NA: Not applicable.