Deep learning-based multimodal model for predicting on-treatment histological outcomes in chronic hepatitis B-associated advanced liver fibrosis

doi:10.3748/wjg.v32.i15.116679

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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. Apr 21, 2026; 32(15): 116679
Published online Apr 21, 2026. doi: 10.3748/wjg.v32.i15.116679

Table 4 Predictive performances for different model in the test set

Metrics	Clinical	HE	Masson	HE + Masson	Multimodal model
AUC (mean)	0.588	0.615	0.676	0.690	0.694
AUC (95%CI)	0.456-0.716	0.484-0.741	0.547-0.799	0.564-0.812	0.570-0.815
Sensitivity	0.412	0.882	0.706	0.647	0.647
Specificity	0.750	0.250	0.450	0.525	0.525
PPV	0.583	0.500	0.522	0.537	0.537
NPV	0.600	0.714	0.643	0.636	0.636

HE: Hematoxylin and eosin; AUC: Area under the receiver operating characteristic curve; CI: Confidence interval; PPV: Positive predictive value; NPV: Negative predictive value.

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Citation: Han W, Cheng DY, He QW, Wang SH, Gong SJ, Chen Y, Yang YP. Deep learning-based multimodal model for predicting on-treatment histological outcomes in chronic hepatitis B-associated advanced liver fibrosis. World J Gastroenterol 2026; 32(15): 116679
URL: https://www.wjgnet.com/1007-9327/full/v32/i15/116679.htm
DOI: https://dx.doi.org/10.3748/wjg.v32.i15.116679

Han W, Cheng DY, He QW, Wang SH, Gong SJ, Chen Y, Yang YP. Deep learning-based multimodal model for predicting on-treatment histological outcomes in chronic hepatitis B-associated advanced liver fibrosis. World J Gastroenterol 2026; 32(15): 116679 [DOI: 10.3748/wjg.v32.i15.116679]

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