Advances in the application of artificial intelligence in solid tumor imaging

Table 1 Summary of artificial intelligence application in clinical imaging examination

Publish date	Ref.	AI	Application scenarios	Sensitivity	Accuracy	Specificity	PPV	NPV	Detection time	Variation	Volume	AUC	DSC
10/2020	Fukuda et al[16]	CNN	Diagnosis of esophagus squamous cell cancer	91.1%	88.3%
05/2020	Zhang et al[7]	CNN	Diagnosis of chronic atrophic gastritis	94.5%	94.2%	94.0%						0.99
10/2020	Horiuchi et al[19]	CAD	Diagnosis of early gastric cancer	87.4%	85.1%	82.8%	83.5%	86.7%				0.8684
02/2020	Wang et al[23]	Faster R-CNN	Circumferential resection margin of rectal cancer	83.8%	93.2%	95.6%
03/2020	Shen et al[28]	RF	Pathological complete response of rectal cancer		95.3%
01/2021	Abe et al[18]	CNN	Diagnosis of gastric cancer	58.4%		87.3%	26.0%		45.5 s
01/2020	Zhou et al[29]	CNN	Lymph node metastasis prediction from primary breast cancer	> 80%		> 70%						0.9
03/2020	Penco et al[32]	DWI	MRI-guided vacuum-assisted breast biopsy	84.0%	94.0%	77.0%		97.0%
05/2020	Adachi et al[31]	RetinaNet	Diagnosis of breast cancer	92.6%		82.8%						0.925
		Readers without RetinaNet		84.7%		84.1%						0.884
		Readers with RetinaNet		88.9%		82.3%						0.899
02/2020	Sasaki et al[35]	Experts	Diagnosis of breast cancer	89.0%
		Experts with Transpara system		95.0%
06/2020	Mango et al[30]	US	Diagnosis of BI-RADS 3 to BI-RADS 4A or above of breast cancer							13.6%
		US+DS								10.8%
02/2020	Barczyński et al[39]	Doctors without CAD	Classification of thyroid tumor		76.0%
		Doctors with CAD			82.0%
06/2020	Lee et al[41]	CAD	Diagnosis of thyroid neck lymph node metastasis	80.2%	82.8%	83.0%	83.0%	80.2%				0.884
03/2020	Polymeri et al[50]	CNN	Prostate gland uptake in PET/CT								71 mL
10/2020	Raciti et al[43]	Paige Prostate Alpha	Diagnosis of prostate cancer	90.0%
07/2020	Chauvie et al[51]	Binomial visual analysis	Lung DTS	95.0%			14.0%
		Pulmonary-RADS		65.0%			19.0%
		Logistic regression		20.0%			29.0%
		RF		30.0%			40.0%
		Neural network		90.0%			95.0%
07/2020	Tau et al[52]	CNN	Diagnosis of lymph node metastasis of lung cancer	74% ± 32%	80% ± 17%	84% ± 16%
			Predicting of distal metastasis of lung cancer	45% ± 8%	63% ± 5%	79% ± 6%	54.5%	68.6%
01/2020	Peng et al[54]	Transfer learning	Predicting of TACE treatment response of hepatocellular carcinoma		> 82.8%							> 0.94
09/2013	Wolz et al[59]	Multi atlas technology	Segmentation of the pancreas										70.0%
08/2020	Gibson et al[62]	Deep learning technology											78.0%
		iFCN											72.3% ± 11.4%
		Artificial segmentation											15 min to 87.5% DSC

AI: Artificial intelligence; PPV: Positive predictive value; NPV: Negative predictive value; AUC: Area under the curve; DSC: Dice similarity coefficient; CNN: Convolutional neural network; Faster R-CNN: Faster region-based convolutional neural network; RF: Random forest; DWI: Diffusion-weighted imaging; US: Ultrasound; DS: Decision support; CAD: Computer-aided design; DTS: Digital tomosynthesis; TACE: Transarterial chemoembolization; iFCN: Interactive fully convolutional network; BI-RADS: Breast Imaging Reporting and Data System; MRI: Magnetic resonance imaging; PET/CT: Positron emission tomography/computed tomography; RADS: Reporting and Data System.