Computed tomography radiomics-based machine learning nomogram for preoperative prediction of glypican-3 expression in hepatocellular carcinoma

doi:10.4329/wjr.121161

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World Journal of Radiology

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1949-8470

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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 Radiol. Jul 28, 2026; 18(7): 121161
Published online Jul 28, 2026. doi: 10.4329/wjr.121161

Table 6 Comparison of net reclassification improvement and integrated discrimination improvement among the clinical model, random forest radiomics model, and combined model

Comparisons between models	Training set		Testing set
Comparisons between models	NRI	IDI	NRI	IDI
Nomogram vs clinic	0.263	0.071	-0.034	-0.046
Nomogram vs ALL	0.032	0.048	0.397	0.166
ALL vs clinic	0.231	0.023	-0.431	-0.211

Clinic: Clinical model; ALL: Random forest radiomics model; Nomogram: Combined model; NRI: Net reclassification improvement; IDI: Integrated discrimination improvement.

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Citation: Zheng ZH, Wu CH, Hu JB, Xu JF, Zi XY, Chen JH, He Q, Dong WY. Computed tomography radiomics-based machine learning nomogram for preoperative prediction of glypican-3 expression in hepatocellular carcinoma. World J Radiol 2026; 18(7): 121161
URL: https://www.wjgnet.com/1949-8470/full/v18/i7/121161.htm
DOI: https://dx.doi.org/10.4329/wjr.121161

Zheng ZH, Wu CH, Hu JB, Xu JF, Zi XY, Chen JH, He Q, Dong WY. Computed tomography radiomics-based machine learning nomogram for preoperative prediction of glypican-3 expression in hepatocellular carcinoma. World J Radiol 2026; 18(7): 121161 [PMID: 42534435 DOI: 10.4329/wjr.121161]

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