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.

Table 2 Studies on artificial intelligence using endoscopic ultrasound to diagnose pancreatic ductal adenocarcinoma

Ref.	Clinical question	Training set (number of subjects)	Validation set (number of subjects)		AI instrument	AUROC	Accuracy	Sensitivity	Specificity
Udristoiu et al[84], 2021	Detecting focal pancreatic masses in four EUS imaging modalities	65			CNN and Long Short-term Memory models	0.97	97.6%	98.1%	96.7%
Tonozuka et al[83], 2021	Detecting PDAC in patients with normal pancreas/Chronic pancreatitis	92			CNN	0.924	NA	90.2%	74.9%
Marya et al[78], 2021	Differentiate AIP from PDAC, chronic pancreatitis and other pancreatic diseases	336		124	CNN	0.976	NA	95%	90%
Kuwahara et al[77], 2019	Predicting malignancy in IPMN	50			CNN	0.98	94%	95.7%	92.6%
Ozkan et al[80], 2016	Differentiating pancreatic cancer from healthy pancreas	260 images		72 images	ANN	NA	87.5%	83.3%	93.3%
Saftoiu et al[81], 2015	Differentiate pancreatic cancer from chronic pancreatitis	117		25	ANN	NA	NA	94.6%	94.4%
Zhu et al[86], 2013	Differentiating pancreatic cancer from chronic pancreatitis.	194		194	SVM	NA	94.2%	96.3%	93.4%
Saftoiu et al[82], 2012	Diagnosis of focal pancreatic lesions	258 patients			ANN	0.94	84.27%	87.59%	82.94%
Zhang et al[85], 2010	Differentiate pancreatic cancer from non-tumorous tissue	108		108	SVM	NA	97.98%	94.3%	99.45%
Saftoiu et al[20], 2008 cancer	Differentiate normal pancreas, chronic pancreatitis, pancreatic cancer, and neuroendocrine tumors	68			Neural network	0.847 (for PDAC vs chronic pan-creatitis)	86.1% (for PDAC vs chronic pan-creatitis)	93.8% (for PDAC vs chronic pan-creatitis)	63.6% (for PDAC vs chronic pan-creatitis)
Das et al[19], 2008	Differentiating pancreatic adenocarcinoma from non-neoplastic tissue (includes normal pancreas and chronic pancreatitis)	160		159	ANN	0.93	NA	93%	92%
Norton et al[79], 2001	Differentiate malignancy from pancreatitis	35			ML	NA	80%	100%	50%

AUROC: Area under the receiver operating characteristic; AI: Artificial intelligence; CNN: Convolutional neural network; EUS: Endoscopic ultrasound; SVM: Support vector machines; ML: Machine learning; NA: Not available; IPMN: Intraductal papillary mucinous neoplasm; PDAC: Pancreatic ductal adenocarcinoma.