Computed tomography-based transformer model for non-invasive prediction of portal venous pressure in chronic liver diseases

doi:10.3748/wjg.121435

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World Journal of Gastroenterology

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1007-9327

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

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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 Gastroenterol. Dec 21, 2026; 32(47): 121435
Published online Dec 21, 2026. doi: 10.3748/wjg.121435

Table 3 Diagnostic performance of the model for different hepatic venous pressure gradient thresholds

HVPG	Group	Sensitivity (95%CI)	Specificity (95%CI)	PPV (95%CI)	NPV (95%CI)	AUC (95%CI)
> 5 mmHg	Training set	0.999 (0.998, 1.000)	0.817 (0.798, 0.834)	0.941 (0.935, 0.947)	0.998 (0.994, 0.999)	0.999 (0.999, 1.000)
	Internal testing set	0.990 (0.968, 0.997)	0.389 (0.183, 0.639)	0.964 (0.935, 0.981)	0.700 (0.354, 0.919)	0.956 (0.922, 0.991)
	Temporally independent validation set	0.967 (0.901, 0.992)	0.500 (0.223, 0.777)	0.937 (0.862, 0.974)	0.667 (0.309, 0.910)	0.951 (0.911, 0.991)
≥ 10 mmHg	Training set	0.986 (0.982, 0.989)	0.970 (0.963, 0.976)	0.983 (0.979, 0.987)	0.974 (0.968, 0.980)	0.998 (0.998, 0.999)
	Internal testing set	0.992 (0.970, 0.999)	0.519 (0.380, 0.655)	0.909 (0.868, 0.938)	0.933 (0.765, 0.988)	0.934 (0.905, 0.962)
	Temporally independent validation set	0.940 (0.859, 0.978)	0.857 (0.626, 0.962)	0.963 (0.888, 0.990)	0.783 (0.558, 0.917)	0.962 (0.927, 0.996)
≥ 12 mmHg	Training set	0.986 (0.982, 0.989)	0.990 (0.985, 0.993)	0.993 (0.991, 0.995)	0.978 (0.972, 0.983)	0.999 (0.999, 1.000)
	Internal testing set	0.996 (0.972, 1.000)	0.640 (0.531, 0.737)	0.875 (0.827, 0.912)	0.983 (0.895, 0.999)	0.978 (0.965, 0.991)
	Temporally independent validation set	0.912 (0.823, 0.961)	0.958 (0.769, 0.998)	0.986 (0.917, 0.999)	0.767 (0.573, 0.894)	0.972 (0.943, 1.000)
≥ 16 mmHg	Training set	0.938 (0.929, 0.946)	0.967 (0.961, 0.972)	0.958 (0.951, 0.965)	0.951 (0.944, 0.957)	0.993 (0.992, 0.994)
	Internal testing set	0.941 (0.899, 0.967)	0.853 (0.762, 0.914)	0.937 (0.894, 0.964)	0.862 (0.772, 0.921)	0.955 (0.922, 0.988)
	Temporally independent validation set	0.905 (0.809, 0.958)	0.833 (0.645, 0.937)	0.931 (0.839, 0.974)	0.781 (0.596, 0.901)	0.932 (0.877, 0.987)
≥ 20 mmHg	Training set	0.906 (0.891, 0.919)	0.969 (0.964, 0.973)	0.899 (0.883, 0.912)	0.971 (0.967, 0.975)	0.992 (0.991, 0.993)
	Internal testing set	0.793 (0.730, 0.845)	0.875 (0.796, 0.927)	0.920 (0.867, 0.954)	0.700 (0.616, 0.773)	0.895 (0.849, 0.940)
	Temporally independent validation set	0.750 (0.575, 0.873)	0.912 (0.811, 0.964)	0.818 (0.639, 0.924)	0.873 (0.768, 0.937)	0.904 (0.846, 0.961)

HVPG: Hepatic venous pressure gradient; PPV: Positive predictive value; NPV: Negative predictive value; AUC: Area under the receiver operating characteristic curve; CI: Confidence interval.

Full Size Table

Citation: Guo X, Tong H, Cai WQ, He JY, Han TR, Qian SJ, Quan X, Li Y, Wei B, Wang ZD, Tai Y, Guo DQ, Wu H. Computed tomography-based transformer model for non-invasive prediction of portal venous pressure in chronic liver diseases. World J Gastroenterol 2026; 32(47): 121435
URL: https://www.wjgnet.com/1007-9327/full/v32/i47/121435.htm
DOI: https://dx.doi.org/10.3748/wjg.121435

Guo X, Tong H, Cai WQ, He JY, Han TR, Qian SJ, Quan X, Li Y, Wei B, Wang ZD, Tai Y, Guo DQ, Wu H. Computed tomography-based transformer model for non-invasive prediction of portal venous pressure in chronic liver diseases. World J Gastroenterol 2026; 32(47): 121435 [DOI: 10.3748/wjg.121435]

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