Prediction of Ki-67 expression in hepatocellular carcinoma with machine learning models based on intratumoral and peritumoral radiomic features

doi:10.4251/wjgo.v17.i5.104172

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World Journal of Gastrointestinal Oncology

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1948-5204

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

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World J Gastrointest Oncol. May 15, 2025; 17(5): 104172
Published online May 15, 2025. doi: 10.4251/wjgo.v17.i5.104172

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Figure 2 Computed tomography images before and after superresolution reconstruction. A: Image from a normal resolution; B: Image after superresolution reconstruction.

Citation: Zhu ZW, Wu J, Guo Y, Ren QY, Li DN, Li ZY, Han L. Prediction of Ki-67 expression in hepatocellular carcinoma with machine learning models based on intratumoral and peritumoral radiomic features. World J Gastrointest Oncol 2025; 17(5): 104172
URL: https://www.wjgnet.com/1948-5204/full/v17/i5/104172.htm
DOI: https://dx.doi.org/10.4251/wjgo.v17.i5.104172

Zhu ZW, Wu J, Guo Y, Ren QY, Li DN, Li ZY, Han L. Prediction of Ki-67 expression in hepatocellular carcinoma with machine learning models based on intratumoral and peritumoral radiomic features. World J Gastrointest Oncol 2025; 17(5): 104172 [PMID: 40487953 DOI: 10.4251/wjgo.v17.i5.104172]

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