Role of artificial intelligence in the characterization of indeterminate pancreatic head mass and its usefulness in preoperative diagnosis

Table 1 Studies on differentiation of indeterminate lesions using artificial intelligence

No.	Ref.	Number of patients	Organ of interest	Sub-type of AI	Outcome
1	Ippolito et al[12], 2004	453	Thyroid nodule (benign vs malignant)	ANN	Refinement of risk stratification of FNAB and clinical data
2	Daniels et al[13], 2020	121	Indeterminant thyroid nodule	ML	ML and ultrasonography can identify genetically high risk lesions
3	Becker et al[14], 2018	632	Breast lesion (benign vs malignant)	Generic DLS	Aids diagnosing cancer on breast ultrasound images with an accuracy comparable to radiologists
4	Scott et al[15], 2019	125	Lung GGO (benign vs malignant)	ANN	Improve diagnostic ability using CT scan, PET, and clinical data
5	Guo et al[16], 2022	20	Indeterminant small lung lesions	DNN	DNN based method may detect small lesions < 10 mm at an effective radiation dose < 0.1 mSv.
6	Yasaka et al[17], 2018	460	Liver mass (HCC vs others)	CNN	High diagnostic performance in differentiation of liver masses using dynamic CT
7	Moawad et al[18], 2021	40	Adrenal incidentaloma (benign vs malignant)	ML	Machine learning and CT texture analysis can differentiate between benign and malignant indeterminate adrenal tumors
8	Stanzione et al[19], 2021	55	Indeterminant solid adrenal lesions	ML	MRI handcrafted radiomics and ML can be used to different adrenal incidentalomas
9	Massa'a et al[20], 2022	160	Indeterminant solid renal mass (benign vs malignant)	ML	MRI-based radiomics and ML can be useful in differentiation
10	Saraiva et al[21], 2022	85	Indeterminant biliary strictures	CNN	CNN can accurately differentiate benign strictures from malignant ones

AI: Artificial intelligence; ANN: Artificial neural network; CNN: Convolutional neural network; CT: Computed tomography; DNN: Deep neural network; DLS: Deep learning software; FNAB: Fine needle aspiration biopsy; GGO: Ground glass opacities; HCC: Hepatocellular carcinoma; ML: Machine learning; MRI: Magnetic resonance imaging.

Table 2 Studies on differentiation of indeterminate lesions using artificial intelligence algorithms on computed tomography images

No.	Ref.	Number of patients	Primary objective	Sub-type of AI used	Outcome
1	Qureshi et al[36], 2022	108	Identification of PDAC	ML	Accuracy: 86%
2	Ebrahimian et al[121], 2022	103	Differentiation of benign vs malignant pancreatic lesions	RF	AUC: 0.94
3	Chakraborty et al[59], 2018	103	High risk vs low risk IPMN	RF, SVM	AUC: 0.81
4	Polk et al[60], 2020	29	High risk vs low risk IPMN	LR	AUC: 0.90
5	Ikeda et al[122], 1997	71	PDAC vs pancreatitis	NN	AUC: 0.916
6	Chen et al[58], 2021	100	SCN vs MCN	LASSO and RFE_Linear SVC	AUC: 0.932
7	Yang et al[57], 2019	53	SCN vs MCN	LASSO	AUC: 0.66
8	Yang et al[123], 2022	63	SCN vs MCN	MMRF-ResNet	AUC: 0.98
9	Ren et al[124], 2020	112	PDAC vs pancreatic adenosquamous carcinoma	RF	AUC: 0.98
10	Xie et al[125], 2021	226	MCN vs ASCN	RF	AUC: 0.734
11	Ziegelmayer et al[126], 2020	86	AIP vs PDAC	CNN, ML	AUC: 0.90
12	Li et al[62], 2022	97	Focal-type AIP vs PDAC	LASSO regression	AUC: 0.97
13	Gao et al[127], 2021	170	MCN vs SCN	mRMR + LASSO	AUC: 0.91
14	Dmitriev et al[53], 2017	134	Classification of pancreatic cyst	RF, CNN	Accuracy: 83.6%
15	Li et al[54], 2019	206	Classification of pancreatic cysts	DNN (Dense-Net)	Accuracy: 72.8%
16	Wei et al[56], 2019	260	SCN vs other cystic neoplasms	ML	AUC: 0.767

AI: Artificial intelligence; AIP: Autoimmune pancreatitis; ASCN: Atypical serous cystic neoplasm; AUC: Area under the curve; CNN: Convolutional neural network; DNN; Deep neural network; IPMN: Intraductal papillary mucinous neoplasm; LASSO: Least absolute shrinkage and selection operator; LR: Logistic regression; MCN: Mucinous cystic neoplasm; ML: Machine learning; PDAC: Pancreatic ductal adenocarcinoma; RFE: Recursive feature elimination; RF: Random forest; SCN: Serous cystic neoplasm; SVM: Support vector machine; NN: Neural network; mRMR: Minimum redundancy maximum relevance; SVC: Support vector classifier; MMRF: Multi-channel-multiclassifier-random forest.

Table 3 Studies on differentiation of indeterminate lesions using artificial intelligence algorithms on magnetic resonance images

No.	Ref.	Number of patients	Primary objective	Sub-type of AI used	Outcome
1	Li et al[62], 2022	267	PDAC detection	UDA + meta learning + GCN	DSC (62.08%, T1), (61.35%, T2), (61.88%, DWI), (60.43%, AP)
2	Chen et al[63], 2022	73	PDAC detection	Spiral-ResUNet	DSC: 65.60%, Jaccard index: 49.64%
3	Liang Y et al[128], 2020	56	PDAC detection	CNN	DSC: 71%
5	Cui et al[129], 2021	202	Grading-BD IPMN	LASSO	AUC: 0.903
6	Corral et al[67], 2019	139	Classification of IPMN	CNN	AUC: 0.783
7	Cheng et al[68], 2022	60	Malignant IPMN	LR, SVM	MRI + SVM: AUC: 0.940, CT + SVM: AUC: 0.864
8	Hussein et al[130], 2019	171	Classification of IPMN	SVM, RF, 3D, CNN	Accuracy 84.22%

AI: Artificial intelligence; AP: Arterial phase; AUC: Area under the curve; CT: Computed tomography; CNN: Convoluted neural network; DSC: Dice similarity coefficient; DWI: Diffusion weighted image; GCN: Graph convolutional network; IPMN: Intraductal papillary mucinous neoplasm; LASSO: Least absolute shrinkage and selection operator; LR: Logistic regression; MRI: Magnetic resonance and imaging; PDAC: Pancreatic ductal adenocarcinoma; RF: Random forest; SVM: Support vector machine; UDA: Unsupervised data augmentation.

Table 4 Studies on differentiation of indeterminate lesions using artificial intelligence algorithms on endoscopic ultrasonography images

No.	Ref.	Number of patients	Primary outcome	Sub type of AI used	Outcome
1	Zhu et al[78], 2013	262	PDAC vs CP	SVM	Accuracy: 94.2%
2	Zhu et al[131], 2015	100	AIP vs CP	SVM	Accuracy: 89.3%
3	Zhang et al[74], 2010	216	Normal pancreas vs PDAC	SVM	Accuracy: 97.98%
4	Ozkan et al[76], 2016	332	Recognition of pancreatic cancer amongst various age group	ANN	Accuracy: Average: 87.5% (all ages), Min: 88.46% (40-60 yr), Max: 92% (< 40 yr)
5	Kuwahara et al[83], 2019	50	Benign vs malignant IPMN	CNN	Accuracy: 94%
6	Das et al[75], 2008	56	PDAC vs normal pancreas vs CP	ANN	AUC: 0.93
7	Săftoiu et al[80], 2008	68	Benign vs malignant pancreatic lesion	ANN	Accuracy: 89.7%
8	Tonozuka et al[132], 2021	139	PDAC vs CP	CNN	AUC: 0.94
9	Marya et al[133], 2021	583	PDAC vs benign causes of pancreatic SOL	CNN	AUC: 0.976
10.	Xu et al[134], 2013	Systemic Analysis of 6 studies	Benign vs malignant pancreatic lesion	-	AUC: 0.962

AI: Artificial intelligence; AIP: Autoimmune pancreatitis; ANN: Artificial neural network; CNN: Convoluted neural network; CP: Chronic pancreatitis; IPMN: Intraductal papillary mucinous neoplasm; PDAC: Pancreatic ductal adenocarcinoma; SOL: Space occupying lesion; SVM: Support vector machine.

Table 5 Studies on differentiation of indeterminate lesions using artificial intelligence algorithms on different biomarkers

No.	Ref.	Number of samples	Type of biomarker used	Sub-type of AI used	Conclusion
1	Chen et al[104], 2019	28	Exosomes	LDA	Accuracy: 100%
2	Zheng et al[105], 2022	220	Exosomes	ANN	AUC: 0.86
3	Ko et al[106], 2017	28	Exosomes	LDA	Accuracy: 100%
4	Cristiano et al[107], 2019	34	Cell-free DNA	GBM	AUC: 0.86
5	Yu et al[108], 2020	501	extracellular vesicles long RNA	SVM	AUC: 0.96
6	Gao et al[109], 2012	199	Proteomes	SVM, KNN, ANN	AUC: 0.971
7	Yu et al[110], 2005	100	Proteomes	DT	Sensitivity: 88.9%, specificity: 74.1%
8	Qiao et al[112], 2022	136	Proteomes	CNN	Accuracy: 87.63%
9	Alizadeh et al[113], 2020	671	Circulating micro RNA	ANN	Accuracy: 0.86

AI: Artificial intelligence; ANN: Artificial neural network; CNN: Convoluted neural network; DT: Digital transformation; KNN: K-nearest neighbor; GBM: Gradient boosting machine; LDA: Linear discriminant analysis; SVM: Support vector machine.

Table 6 Studies demonstrating impact of artificial intelligence on increasing efficacy of diagnostic modalities

No.	Ref.	Objective	Modality	Sensitivity	Specificity	Accuracy
1	Corral et al[67], 2019	Differentiate cystic SOL of pancreas	Fukuoka guideline	62%	77	77.5%
			Deep learning	75%	78%	78.3%
2	Kuwahara et al[83], 2019	Detection of malignant IPMN	Human pre-operative diagnosis (Clinical + lab + imaging)	95.7%	22.2%	56%
			Artificial intelligence	95.7%	92.66	94%
3	Gao et al[135], 2020	Ability to differentiate pancreatic disease	CE-MR	NA	NA	83.93%
			GAN	NA	NA	76.79%
4	Rigiroli et al[136], 2021	Detection of pancreatic cancer and SMA involvement	CT scan	NA	NA	71%
			Artificial intelligence	62%	77%	54%
5	Chen et al[137], 2023	Detection of pancreatic cancer	CT scan	89.9%	95.9%	AUC: 0.96
			CNN	90%	93%	NA
6	Tang et al[138], 2023	Pancreatic mass diagnosis	EUS FNA	81.6%	100%	87.9%
			CE EUS Master-guided FNA	90.9%	100%	93.8%

CE-MR: Contrast enhanced-magnetic resonance; CT: Computed tomography; CNN: Convoluted neural network; EUS: Endoscopic ultrasound; FNA: Fine needle aspiration; GAN: Generative adversarial network; IPMN: Intraductal papillary mucinous neoplasm; NA: Not available; SMA: Superior mesenteric artery; SOL: Space occupying lesion.