Biparametric magnetic resonance imaging-based radiomic and deep learning models for predicting Ki-67 risk stratification in hepatocellular carcinoma

doi:10.4254/wjh.v17.i8.109530

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Aug 27, 2025 (publication date) through Aug 30, 2025

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World Journal of Hepatology

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1948-5182

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

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World J Hepatol. Aug 27, 2025; 17(8): 109530
Published online Aug 27, 2025. doi: 10.4254/wjh.v17.i8.109530

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Figure 1 Detailed process from biparametric magnetic resonance imaging acquisition to recurrence-free survival assessment. AP: Arterial phase; T2WI: T2-weighted imaging; ROI: Region of interest; DTL: Deep transfer learning; CAM: Class activation mapping; ROC: Receiver operating characteristic; DCA: Decision curve analyses; RFS: Recurrence-free survival.

Citation: Zuo XY, Liu HF. Biparametric magnetic resonance imaging-based radiomic and deep learning models for predicting Ki-67 risk stratification in hepatocellular carcinoma. World J Hepatol 2025; 17(8): 109530
URL: https://www.wjgnet.com/1948-5182/full/v17/i8/109530.htm
DOI: https://dx.doi.org/10.4254/wjh.v17.i8.109530