Interpretable model based on multisequence magnetic resonance imaging radiomics for predicting the pathological grades of hepatocellular carcinomas

doi:10.4329/wjr.v17.i12.112911

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World Journal of Radiology

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1949-8470

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

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World J Radiol. Dec 28, 2025; 17(12): 112911
Published online Dec 28, 2025. doi: 10.4329/wjr.v17.i12.112911

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Figure 8 Dynamic nomogram based on the combined radiomics-clinical model via the web interface. A dynamic nomogram was developed for the training group by combining the following four parameters: Rad score, tumor size, hepatitis B virus status, and monocyte count; and an example of predicting the probability of poor differentiation in hepatocellular carcinoma patients is shown. HBV: Hepatitis B virus; MONO: Monocyte count.

Citation: Shi Y, Zhang P, Li L, Yang HM, Li ZM, Zheng J, Yang L. Interpretable model based on multisequence magnetic resonance imaging radiomics for predicting the pathological grades of hepatocellular carcinomas. World J Radiol 2025; 17(12): 112911
URL: https://www.wjgnet.com/1949-8470/full/v17/i12/112911.htm
DOI: https://dx.doi.org/10.4329/wjr.v17.i12.112911

Shi Y, Zhang P, Li L, Yang HM, Li ZM, Zheng J, Yang L. Interpretable model based on multisequence magnetic resonance imaging radiomics for predicting the pathological grades of hepatocellular carcinomas. World J Radiol 2025; 17(12): 112911 [PMID: 41480292 DOI: 10.4329/wjr.v17.i12.112911]

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