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©The Author(s) 2025.
World J Hepatol. Aug 27, 2025; 17(8): 109530
Published online Aug 27, 2025. doi: 10.4254/wjh.v17.i8.109530
Published online Aug 27, 2025. doi: 10.4254/wjh.v17.i8.109530
Figure 5 Recurrence-free survival curves obtained from Kaplan-Meier analysis were scaled for high Ki-67 risk stratification.
A: High Ki-67 expression was confirmed by histopathology; B: High Ki-67 expression was predicted by the nomogram model; C: Ki-67 risk stratification was further diagnosed by histopathology; D: Ki-67 risk stratification was further predicted by the nomogram model.
- 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