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

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 17, Issue 5

Peer-Review Report of This Article

CrossCheck and Google Search of This Article

Academic Rules and Norms of This Article

Supplementary Materials of This Article

Citation of this article

Corresponding Author of This Article

Research Domain of This Article

Article-Type of This Article

Open-Access Policy of This Article

Times Cited Counts in Google of This Article

Journal Information of This Article

Publication Name

World Journal of Gastrointestinal Oncology

ISSN

1948-5204

Publisher of This Article

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

Retrospective Study

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

Full Size Figure Download Figure

Figure 1 Flowchart for the inclusion and exclusion of hepatocellular carcinoma patients in this study. HCC: Hepatocellular carcinoma.

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]

All content on this site: Copyright © 1993-2026 Baishideng Publishing Group Inc, its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply.