Role of artificial intelligence in early detection and screening for pancreatic adenocarcinoma

Table 1 Studies on artificial intelligence using computed tomography or MRI imaging to diagnose pancreatic ductal adenocarcinoma

Ref.	Clinical question	Training set (number of subjects)	Validation set (number of subjects)	AI instrument	AUROC	Accuracy	Sensitivity	Specificity
Watson et al[66], 2021	Detection of pancreatic cystic neoplasms (including PDAC) vs benign cysts	18	9	CNN	NA	NA	NA	NA
Si et al[65], 2021	Detection of pancreatic cancer (including PDAC, IPMN, PNET)	319	347	DL	0.871	87.6% for PDAC	86.8% for pancreatic cancer	69.5% for pancreatic cancer
Park et al[64], 2020	Distinguishing pancreatic cancer tissue from autoimmune pancreatitis	120	62	Random forest machine learning	0.975	95.2%	89.7%	100%
Ma et al[63], 2020	Differentiate pancreatic cancer from benign tissue	330	41	CNN	0.9653 (plain scan)	95.47% (plain scan),95.76% (arterial scan), 95.15% (venous phase)	91.58% (plain scan), 94.08% (arterial scan), 92.28% (venous phase)	98.3% (plain scan), 97.6% (arterial scan), 97.9% (venous phase)
Zhang et al[67], 2020	Detection of pancreatic cancer	2650 images	240 images	CNN	0.9455	90.2%	83.8%	91.8%
Liu et al[69], 2020	Differentiating pancreatic cancer tissue from non-cancerous pancreatic tissue	412	139	CNN	0.92	83.2%	79.0%	97.6%
Gao et al[71], 2020	To differentiate pancreatic diseases in pancreatic lesions	398	106	CNN	0.9035 (includes PDAC, adenosquamous carcinoma, acinar cell carcinoma, colloid carcinoma, myoepithelial carcinoma, undifferentiated carcinoma with osteoclast-like giant cells, mucinous cystadenocarcinoma, pancreatoblastoma, pancreatic neuroendocrine carcinoma and metastatic carcinoma)	NA	NA	NA
Chu et al[70], 2019	Differentiating PDAC from normal pancreas	255	125	Random forest	NA	93.6%	95%	92.3%
Zhu et al[72], 2019	Detecting PDAC from normal pancreas	205	234	CNN	NA	57.3%	94.1%	98.5%
Liu et al[73], 2019	Diagnosis of pancreatic cancer	238	100	CNN	0.9632	NA	NA	NA
Corral et al[21], 2019	Identify and stratify IPMN lesions	139		DL	0.783	NA	75% (for PDAC or high grade dysplasia)	78% (for PDAC or high grade dysplasia)
Chu et al[74], 2019	Differentiating PDAC from normal pancreas	456		DL	NA	NA	94.1%	98.5%
Fu et al[75], 2018	Pancreas segmentation (including PDAC, IPMN, Pancreatic Neuroendocrine Tumors, Serous Cyst Adenoma, and Solid Pseudopapillary Tumour of the pancreas)	59		CNN	NA	NA	82.5%	76.22 (PPV)

AUROC: Area under the receiver operating characteristic; AI: Artificial intelligence; CNN: Convolutional neural network; DL: Deep learning; NA: Not available; IPMN: Intraductal papillary mucinous neoplasm; PNET: Pancreatic neuroendocrine tumour; PDAC: Pancreatic ductal adenocarcinoma.