Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma

Table 1 Comprehensive list of artificial intelligence-based investigations in pancreatic ductal adenocarcinoma

Ref.	Modality	Type of algorithm	Sensitivity (%)	Specificity (%)	ROC-AUC (or accuracy %)
PDAC risk prediction
Boursi et al[25], 2021	7 clinical variables	Logistic regression	66.53	54.91	0.71
Appelbaum et al[29], 2021	18 risk factors	Logistic regression	NA	NA	0.71
Muhammad et al[30], 2018	Personal health data (18 features)	ANN	80.7	80.7	0.85
Hsieh et al[28], 2018	ICD-9 code	Logistic regression	NA	NA	0.727
Boursi et al[26], 2017	10 clinical variables	Logistic regression	44.7	94	0.82
Cai et al[27], 2011	5 clinical variables	Logistic regression	NA	NA	0.72
Early diagnosis of PDAC
Zhang et al[34], 2020	Nine-gene signature	Support vector machine	98.65	100	93.3
Zhang et al[83], 2020	CT	DCNN	83.76	91.79	0.9455
Si et al[42], 2021	CT	Fully end-to-end deep learning	86.8	69.5	0.871
Liu et al[54], 2020	CT	CNN	79 (United States)	97.6 (United States)	0.920 (United States)
Ma et al[84], 2020	CT	CNN	98.2	91.6	95
Chu et al[85], 2019	CT	Deep learning (details are NA)	94.1	98.5	NA
Liu et al[53], 2019	CT	CNN	NA	NA	0.9632
Tonozuka et al[86], 2021	EUS	CNN	90.2	74.9	0.924
Ozkan et al[87], 2016	EUS	ANN	83.3	93.3	87.5
Săftoiu et al[88], 2015	EUS	ANN	94.64	94.44	NA
Zhu et al[63], 2013	EUS	Support vector machine	92.52	93.03	NA
Zhang et al[62], 2010	EUS	Support vector machine	94.32	99.45	NA
Das et al[61], 2008	EUS	ANN	93	92	0.93
Săftoiu et al[89] 2008	EUS elastography	NN	91.4	87.9	89.7
Norton et al[60], 2001	EUS	NN	73	NA	83
Alizadeh Savareh et al[40], 2020	Circulating microRNA signatures	PSO + ANN + NCA	93	92	93
Urman et al[90], 2020	Bile juice	NN	88	100	0.98
Pancreatic fistula after pancreaticoduodenectomy
Kambakamba et al[71], 2020	CT	k-NN, random forest classifier, etc	96	98	0.95
Mu et al[72], 2020	CT	CNN	86.7	87.3	0.89
Pathological tumor response to neoadjuvant chemotherapy
Watson et al[80], 2020	CT and CA19-9	CNN	NA	NA	0.785
Survival model
Zhang et al[77], 2020	CT	CNN	NA	NA	11.81% in IPA
Alizadeh Savareh et al[40], 2020	Circulating microRNA signatures	PSO + ANN + NCA	NA	NA	NA
Kaissis et al[66], 2019	MRI	Random forest	87	80	0.90
Walczak et al[79], 2017	14 clinical variables	ANN	91	38	0.6576
Molecular subtype
Kaissis et al[68], 2020	CT	Random forest	84	92	0.93
Tumor subtype (QM vs non-QM)
Kaissis et al[67], 2019	MRI	Gradient boosting decision tree	90	92	0.93
Molecular subtype (KRT81 positive vs negative)
Microsatellite instability status
Li et al[19], 2020	PreMSIm (15-gene signature)	k-NN	85	97	95

AI: Artificial intelligence; PDAC: Pancreatic ductal adenocarcinoma; NA: Not available; ROC-AUC: Area under the receiver operating characteristic curve; ICD-9: International Classification of Diseases 9^th Revision; ANN: Artificial neural network; CT: Computed tomography; DCNN: Deep convolutional neural network; EUS: Endoscopic ultrasound; NN: Neural network; CA19-9: Carbohydrate antigen 19-9; IPA: Index of prediction accuracy; MRI: Magnetic resonance imaging; QM: Quasi-mesenchymal; PSO: Particle swarm optimization; NCA: Neighborhood components analysis; k-NN: k-Nearest neighbor.