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

doi:10.4240/wjgs.v18.i5.115903

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World Journal of Gastrointestinal Surgery

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1948-9366

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Retrospective Cohort Study

Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Gastrointest Surg. May 27, 2026; 18(5): 115903
Published online May 27, 2026. doi: 10.4240/wjgs.v18.i5.115903

Table 1 Computed tomography scanners and parameters

Scanning models	Tube voltage (kV)	Tube current (mA)	Acquisition matrix	Pitch (mm)	Slice thickness (mm)	Slice interval (mm)
Siemens SOMATOM Force computed tomography	120	200	512 × 512	0.6	5	5
United Imaging uCT 710 (64-slice)	120	108	512 × 512	1.0	5	5
Brilliance 64	120	200	512 × 512	0.8	5	5

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Table 2 Characteristics of moderately severe and severe acute necrotizing pancreatitis patients in the training cohort, n (%)/mean ± SD/median (interquartile rage)

Characteristics		MSAP (n = 76)	SAP (n = 52)	Z/χ²/t/Fisher’s exact test	P value
Age (years)		45.01 ± 13.58	52.88 ± 14.68	-3.116	0.002^b
Gender	Male	50 (65.79)	34 (65.38)	0.002	0.962
Gender	Female	26 (34.21)	18 (34.62)	0.002	0.962
Alcohol consumption history	No	47 (61.84)	37 (71.15)	1.187	0.276
Alcohol consumption history	Yes	29 (38.16)	15 (28.85)	1.187	0.276
WBC (× 10⁹/L)		14.12 ± 4.21	15.47 ± 6.24	-1.369	0.175
Serum amylase (× 10 U/L)		23.80 (7.18-43.35)	47.31 (10.83-116.60)	-2.159	0.031^a
Serum lipase (× 10 U/L)		27.45 (11.23-94.00)	59.41 (15.76-117.91)	-1.356	0.175
MCTSI score		8.00 (6.50-8.00)	8.00 (8.00-10.00)	-2.879	0.004^b
Pancreatic necrosis types	Only pancreatic parenchymal necrosis type	11 (14.47)	5 (9.62)	0.730	0.781
	Mixed type	60 (78.95)	44 (84.61)
	Only peripancreatic necrosis type	5 (6.58)	3 (5.77)

^aP < 0.05.

^bP < 0.01.

WBC: White blood cell; MCTSI: Modified Computed Tomography Severity Index; MSAP: Moderately severe acute pancreatitis; SAP: Severe acute pancreatitis.

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Table 3 Characteristics of moderately severe and severe acute necrotizing pancreatitis patients in the test cohort, n (%)/mean ± SD/median (interquartile rage)

Characteristics		MSAP (n = 76)	SAP (n = 52)	Z/χ²/t/Fisher’s exact test	P value
Age (years)		44.39 ± 11.15	50.30 ± 17.46	-1.367	0.182
Gender	Male	24 (66.67)	12 (60.00)	0.249	0.618
Gender	Female	12 (33.33)	8 (40.00)	0.249	0.618
Alcohol consumption history	No	17 (47.22)	13 (65.00)	1.634	0.201
Alcohol consumption history	Yes	19 (52.78)	7 (35.00)	1.634	0.201
WBC (× 10⁹/L)		13.38 ± 4.22	16.46 ± 4.60	-2.535	0.014^a
Serum amylase (× 10 U/L)		12.85 (6.98-36.53)	47.00 (19.83-126.78)	-2.539	0.011^a
Serum lipase (× 10 U/L)		20.80 (9.93-82.75)	50.10 (23.65-145.43)	-1.351	0.177
MCTSI score		8.00 (8.00-8.00)	9.00(8.00-10.00)	-2.492	0.013^a
Pancreatic necrosis types	Only pancreatic parenchymal necrosis type	6 (16.67)	1 (5.00)	1.820	0.475
	Mixed type	29 (80.55)	18 (90.00)
	Only peripancreatic necrosis type	1 (2.78)	1 (5.00)

^aP < 0.05.

WBC: White blood cell; MCTSI: Modified Computed Tomography Severity Index; MSAP: Moderately severe acute pancreatitis; SAP: Severe acute pancreatitis.

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Table 4 Radiomic features of the pancreatic parenchyma

Feature type	Feature count	Median ICC	IQR	ICC < 0.75 (%)
First-order statistics	316	0.959	0.891-0.985	17.7% (56/316)
Shape features	14	0.944	0.937-0.971	7% (1/14)
GLCM features	408	0.928	0.834-0.974	23.5% (96/408)
GLSZM features	272	0.745	0.581-0.889	52.2% (142/272)
GLRLM features	272	0.894	0.744-0.960	25.4% (69/272)
GLDM features	238	0.902	0.728-0.966	28.2% (67/238)
Total	1520	0.912	0.730-0.972	28.3% (431/1520)

GLCM: Gray-level co-occurrence matrix; GLSZM: Gray-level size zone matrix; GLRLM: Gray-level run-length matrix; GLDM: Gray-level dependence matrix; ICC: Intraclass correlation coefficient; IQR: Interquartile rage.

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Table 5 Radiomic features of peripancreatic necrotic collections

Feature type	Feature count	Median ICC	IQR	ICC < 0.75 (%)
First-order statistics	316	0.942	0.841-0.975	23.4% (74/316)
Shape features	14	0.972	0.841-0.975	0% (0/14)
GLCM features	408	0.871	0.693-0.938	33.6% (137/408)
GLSZM features	272	0.883	0.706-0.970	35.6% (97/272)
GLRLM features	272	0.980	0.803-0.992	21.7% (59/272)
GLDM features	238	0.944	0.824-0.988	21.8% (52/238)
Total	1520	0.923	0.744-0.978	27% (419/1520)

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Table 6 Optimal radiomic features of pancreatic parenchyma

Feature number	Optimal radiomic features
1	Wavelet-LLL_glcm_Correlation_qcut
2	Wavelet-HHL_gldm_DependenceVariance_qcut
3	Original_shape_Maximum2DDiameterRow_qcut
4	Wavelet-LHL_glszm_SmallAreaEmphasis_qcut
5	Wavelet-LHL_glszm_SmallAreaEmphasis_cut
6	Logarithm_firstorder_10Percentile_qcut
7	Log-sigma-5-0-mm-3D_glcm_Imc1_cut
8	Gradient_gldm_GrayLevelVariance_cut
9	Wavelet-LHL_firstorder_Mean_qcut
10	Log-sigma-5-0-mm-3D_glcm_Imc1_qcut

The “_cut” indicates equal-width binning; “_qcut” indicates equal-frequency binning.

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Table 7 Optimal radiomic features of peripancreatic necrotic collections

Feature number	Optimal radiomic features
1	Wavelet-HHH_glszm_GrayLevelNonUniformity_qcut
2	Log-sigma-3-0-mm-3D_glcm_Idm_qcut
3	Original_glszm_ZoneEntropy_qcut
4	Square_gldm_DependenceVariance_qcut
5	Exponential_gldm_DependenceVariance_qcut
6	Wavelet-LLL_glrlm_LongRunLowGrayLevelEmphasis_qcut
7	Log-sigma-5-0-mm-3D_glcm_Idmn_qcut
8	Log-sigma-2-0-mm-3D_firstorder_10Percentile_qcut
9	Original_shape_Flatness_qcut

The “_cut” indicates equal-width binning; “_qcut” indicates equal-frequency binning.

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Table 8 Optimal radiomic features of combined regions

Feature number	Optimal radiomic features
1	a_wavelet-LLH_gldm_LowGrayLevelEmphasis_cut
2	p_gradient_glrlm_ShortRunEmphasis_qcut
3	a_log-sigma-4-0-mm-3D_glcm_MCC_qcut
4	p_wavelet-HLL_glcm_Imc1_qcut
5	p_wavelet-HLL_firstorder_Mean_qcut
6	a_wavelet-HHH_glszm_GrayLevelNonUniformity_qcut
7	a_log-sigma-2-0-mm-3D_glszm_ZoneEntropy_qcut
8	a_log-sigma-2-0-mm-3D_firstorder_Mean_qcut
9	p_wavelet-HLL_firstorder_Mean_cut
10	a_wavelet-HLH_glrlm_RunVariance_qcut
11	a_wavelet-LLL_firstorder_RootMeanSquared_cut
12	p_log-sigma-5-0-mm-3D_glcm_Imc1_qcut
13	a_wavelet-LLH_glszm_GrayLevelVariance_qcut
14	a_wavelet-LHH_glszm_LowGrayLevelZoneEmphasis_cut

The “a” indicates peripancreatic necrotic collections; “p” indicates pancreatic parenchyma; “_cut” indicates equal-width binning; “_qcut” indicates equal-frequency binning.

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Table 9 Final hyperparameter values

Model	Hyperparameter	Value
SVM	Kernel, C, gamma	Poly, 0.2
RF	n_estimators, max_depth, min_samples_split, max_features	50, 5, 15, 10
KNN	n_neighbors, wights, p	30, distance, 1
GBDT	n_estimators, learning_rate, max_depth, subsample	20, 0.3, 3, 0.8
XGBoost	n_estimators, learning_rate, max_depth, subsample	20, 0.1, 5, 0.5
LightGBM	n_estimators, learning_rate, num_leaves, colsample_bytree, subsample, max_depth	4000, 0.08, 32 (2⁵), 0.65, 0.9, 5

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.

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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.

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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)

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Table 12 Comparison of clinical outcomes and treatment modalities between acute necrotizing moderately severe pancreatitis and acute necrotizing severe pancreatitis, median (interquartile rage)/n (%)

Characteristics		MSAP (n = 112)	SAP (n = 72)	Z/χ²/t/Fisher’s exact test	P value
Length of stay		11.00 (8.00-16.00)	16.00 (10.00-20.75)	-3.562	< 0.001^b
ICU admission	No	112 (100.00)	60 (83.33)	17.329	< 0.001^b
ICU admission	Yes	0 (0.00)	12 (16.67)	17.329	< 0.001^b
Mortality	No	112 (100.00)	70 (97.22)		0.152
Mortality	Yes	0 (0.00)	2 (2.78)		0.152
Surgery/intervention	No	100 (89.29)	54 (75.00)	6.554	0.010^a
Surgery/intervention	Yes	12 (10.71)	18 (25.00)	6.554	0.010^a

^aP < 0.05.

^bP < 0.001.

ICU: Intensive care unit; MSAP: Moderately severe acute pancreatitis; SAP: Severe acute pancreatitis.

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Citation: Feng Y, Hu XH, Xiao B. Machine learning and radiomics for differentiating severe from moderately severe acute necrotizing pancreatitis on contrast-enhanced computed tomography. World J Gastrointest Surg 2026; 18(5): 115903
URL: https://www.wjgnet.com/1948-9366/full/v18/i5/115903.htm
DOI: https://dx.doi.org/10.4240/wjgs.v18.i5.115903

Feng Y, Hu XH, Xiao B. Machine learning and radiomics for differentiating severe from moderately severe acute necrotizing pancreatitis on contrast-enhanced computed tomography. World J Gastrointest Surg 2026; 18(5): 115903 [DOI: 10.4240/wjgs.v18.i5.115903]

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