Machine learning and radiomics for differentiating severe from moderately severe acute necrotizing pancreatitis on contrast-enhanced computed tomography

Table 10 Performance of different models in the differential diagnosis of severe and moderately severe acute necrotizing pancreatitis in the training cohort, median (interquartile rage)

	Model	AUC	95%CI	Accuracy, %	Sensitivity, %	Specificity, %	F1-score
Pancreatic parenchyma	SVM	0.916	0.858-0.962	84.4 (78.1-90.6)	88.5 (79.0-96.4)	81.6 (72.1-90.0)	0.826 (0.734-0.893)
	RF	0.962	0.928-0.988	89.8 (84.4-94.5)	80.7 (69.8-90.4)	96.1 (91.3-100)	0.866 (0.785-0.929)
	KNN	1.000	1.000-1.000	100 (100-100)	100 (100-100)	100 (100-100)	1.000 (1.000-1.000)
	GBDT	0.996	0.989-1.000	96.9 (93.8-99.2)	92.3 (83.7-98.3)	100 (100-100)	0.960 (0.911-0.991)
	XGBoost	0.971	0.938-0.993	82.2 (77.5-86.9)	88.5 (79.5-96.1)	94.7 (89.2-988.8)	0.902 (0.835-0.956)
Peripancreatic necrotic collections	SVM	0.924	0.877-0.967	87.5 (81.3-92.9)	90.4 (81.5-97.7)	85.5 (77.6-92.9)	0.855 (0.776-0.917)
	RF	0.969	0.942-0.990	90.6 (85.2-95.3)	86.5 (77.2-949)	93.4 (87.5-98.7)	0.882 (81.3-93.9)
	KNN	1.000	1.000-1.000	100 (100-100)	100 (100-100)	100 (100-100)	1.000 (1.000-1.000)
	GBDT	1.000	0.999-1.000	100 (100-100)	100 (100-100)	100 (100-100)	1.000 (1.000-1.000)
	XGBoost	0.977	0.956-0.995	93.0 (88.3-96.8)	88.5 (78.6-96.1)	96.1 (91.5-100)	0.911 (0.846-0.961)
Combined pancreatic parenchyma and peripancreatic necrotic collections	SVM	0.951	0.904-0.986	89.8 (84.4-94.6)	86.5 (76.1-94.8)	92.1 (85.5-97.5)	0.874 (0.796-0.935)
	RF	0.973	0.950-0.991	89.8 (84.4-94.5)	84.6 (75.0-93.9)	93.4 (87.1-98.6)	0.871 (0.795-0.934)
	KNN	1.000	1.000-1.000	100 (100-100)	100 (100-100)	100 (100-100)	1.000 (1.000-1.000)
	GBDT	1.000	0.999-1.000	100 (100-100)	100 (100-100)	100 (100-100)	1.000 (1.000-1.000)
	XGBoost	0.971	0.942-0.992	93.8 (89.8-97.6)	92.3 (84.6-98.2)	94.7 (89.3-988)	0.923 (00.865-0.968)

SVM: Support vector machine; RF: Random forest; KNN: k-nearest neighbor; GBDT: Gradient boosting decision tree; XGBoost: Extreme gradient boosting; LightGBM: Light gradient boosting machine; AUC: Area under the curve; CI: Confidence interval.