Prediction of Ki-67 expression in hepatocellular carcinoma with machine learning models based on intratumoral and peritumoral radiomic features
A machine learning model based on the radiomic features of the intratumoral and peritumoral regions on superresolution computed tomography in conjunction with clinical factors can accurately evaluate Ki-67 expression. The model provides valuable assistance in selecting treatment strategies for HCC patients and contributes to research on neoadjuvant therapy for liver cancer.
Authors
- Qing Ren (ORCID: https://orcid.org/0000-0002-8802-4810)
- Dongning Li (ORCID: https://orcid.org/0000-0003-2893-1432)
- Zeyu Li (ORCID: https://orcid.org/0000-0002-3306-4348)
- Lei Han (ORCID: https://orcid.org/0000-0002-2411-880X)
- Jun Wu (ORCID: https://orcid.org/0000-0002-0678-3435)
- Yang Guo (ORCID: https://orcid.org/0009-0000-7581-7865)
- Ziwei Zhu (ORCID: https://orcid.org/0000-0002-6189-8796)
Institutions
- Dalian Medical University (CN)
- General Hospital of Shenyang Military Region (CN)
- China Medical University (CN)
Publication Details
- Journal
- World Journal of Gastrointestinal Oncology
- Published
- 2025-05-15
- DOI
- https://doi.org/10.4251/wjgo.v17.i5.104172
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72