Interpretable multimodal artificial intelligence model for predicting advanced neoplasia in pancreatic cystic lesions

Table 2 Diagnostic performance of prediction models in the validation cohort and area under the curve values of guideline-based criteria

Model/criteria	AUC (95%CI)	Sensitivity	Specificity	PPV	NPV
Core clinical-EUS model	0.87 (0.76-0.95)	76.5% (13/17)	79.5% (31/39)	61.9% (13/21)	88.6% (31/35)
Integrated multimodal model	0.91 (0.82-0.97)	82.4% (14/17)	89.7% (35/39)	77.8% (14/18)	92.1% (35/38)
Kyoto 2024 criteria1	0.79	-	-	-	-
Fukuoka 2017 criteria	0.77	-	-	-	-
European 2018 criteria	0.76	-	-	-	-
AGA 2015 criteria	0.70	-	-	-	-

¹Kyoto 2024 was evaluated in the intraductal papillary mucinous neoplasms subset (n = 161), consistent with its intended scope.

Validation distribution (model evaluation): 17 advanced neoplasia, 39 non-advanced (n = 56). DeLong tests comparing the integrated model with each guideline-based strategy were significant (all P < 0.01). AUC: Area under the receiver operating characteristic curve; PPV: Positive predictive value; NPV: Negative predictive value; EUS: Endoscopic ultrasound; AGA: American Gastroenterological Association.