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

Table 11 Performance of different models in the differential diagnosis of severe and moderately severe acute necrotizing pancreatitis in the test cohort, median (interquartile rage)

	Model	AUC	95%CI	Accuracy, %	Sensitivity, %	Specificity, %	F1-score	Brier score
Pancreatic parenchyma	SVM	0.829	0.706-0.932	75.0 (62.5-85.7)	85.0 (66.7-100)	69.4 (52.7-83.3)	0.708 (0.540-0.836)	0.164 (0.117-0.214)
	RF	0.840	0.719-0.936	78.6 (67.8-89.3)	75.0 (52.9-93.7)	80.6 (67.7-93.5)	0.714 (0.524-0.851)	0.159 (0.116-0.209)
	KNN	0.814	0.690-0.918	75.0 (64.3-85.7)	65.0 (43.4-86.4)	80.6 (66.7-91.9)	0.650 (0.452-0.810)	0.175 (0.139-0.215)
	GBDT	0.814	0.682-0.940	73.2 (62.5-83.9)	85.0 (66.7-100)	66.7 (51.4-81.3)	0.694 (0.524-0.821)	0.172 (0.119-0.233)
	XGBoost	0.828	0.694-0.933	78.6 (67.8-89.3)	75.0 (55.6-93.7)	80.6 (66.7-923)	0.714 (0.542-0.850)	0.161 (0.109-0.211)
Peripancreatic necrotic collections	SVM	0.857	0.744-0.942	75.0 (62.5-85.7)	65.0 (43.7-86.4)	80.5 (65.8-92.7)	0.650 (0.462-0.809)	0.147 (0.096-0.199)
	RF	0.868	0.756-0.960	82.1 (71.4-91.1)	65.0 (43.7-86.4)	91.7 (81.1-99.8)	0.722 (0.522-0.872)	0.146 (0.089-0.206)
	KNN	0.825	0.695-0.923	75.0 (64.3-85.7)	60.0 (36.8-81.8)	83.3 (70.3-94.3)	0.632 (0.424-0.784)	0.159 (0.112-0.211)
	GBDT	0.817	0.686-0.922	75.0 (67.9-89.3)	70.0 (50.0-59.5)	83.3 (70.6-94.6)	0.700 (0.513-0.850)	0.168 (0.104-0.238)
	XGBoost	0.847	0.733-0.947	80.4 (69.6-0.911)	70.0 (47.6-88.9)	86.1 (74.2-95.1)	0.718 (0.526-0.857)	0.147 (0.102-0.195)
Combined pancreatic parenchyma and peripancreatic necrotic collections	SVM	0.879	0.780-0.959	80.4 (69.6-91.1)	70.0 (50.0-90.0)	86.1 (74.4-96.9)	0.718 (0.540-0.864)	0.142 (0.094-0.189)
	RF	0.896	0.778-0.977	83.9 (74.9-92.9)	65.0 (42.9-0.857)	94.4 (86.1-100)	0.743 (0.600-0.889)	0.134 (0.097-0.173)
	KNN	0.849	0.735-0.945	82.1 (71.4-928)	60.0 (38.1-82.6)	94.4 (85.4-100)	0.706 (0.500-0.875)	0.156 (0.113-0.199)
	GBDT	0.854	0.744-0.947	78.6 (67.9-89.3)	70.0 (50.0-88.9)	83.3 (71.1-94.7)	0.700 (0.533-0.840)	0.166 (0.085-0.247)
	XGBoost	0.853	0.744-0.949	78.6 (67.8-87.5)	70.0 (50.0-88.2)	83.3 (70.3-94.3)	0.700 (0.519-0.833)	0.149 (0.086-0.221)

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.