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Computed tomography radiomics-based machine learning nomogram for preoperative prediction of glypican-3 expression in hepatocellular carcinoma
Zi-Han Zheng, Department of Radiology, The Second People’s Hospital of Yuxi City, Yuxi 653100, Yunnan Province, China
Chun-Hua Wu, Jin-Bo Hu, Jun-Feng Xu, Xin-Yue Zi, Jin-Hui Chen, Qin He, Department of Radi
Wen-Ying Dong, Department of Medical Nursing, College of Nursing of Dali University, Dali 671003, Yunnan Province, China
Co-first authors: Zi-Han Zheng and Chun-Hua Wu.
Author contributions: Zheng ZH and Wu CH were responsible for investigation, writing – original draft, formal analysis, methodology, software, writing – original draft as co-first authors; Hu JB was responsible for formal analysis, methodology, software, writing – review and editing; Xu JF was responsible for funding acquisition, investigation, supervision, validation, writing – review and editing; Zi XY, Chen JH, and He Q were responsible for data curation, investigation, writing – review and editing; Dong WY was responsible for conceptualization, investigation, project administration, resources, writing – review and editing.
AI contribution statement: No AI tools, including ChatGPT, Grammarly, DeepL, or any other AI software, were used at any stage. The entirety of the main text and the response to reviewers was written by the authors without the assistance of AI. No AI tools were used for language polishing, translation, data analysis, or writing assistance of the manuscript or response letter. No AI tools participated in the design of the study or interpretation of its results. No images in the manuscript were generated by AI. In summary, no generative AI was used in the preparation of the manuscript and the response letter.
Supported by the Key Research and Development Program in the Field of Biomedicine of the Dali Science and Technology Bureau, No. 20252903B030003.
Institutional review board statement: This study was approved by the Institutional Review Board of the First Affiliated Hospital of Dali University and Dali First People’s Hospital (IRB approval No. DFY20250120001) and conducted in accordance with the Declaration of Helsinki.
Informed consent statement: The requirement for written informed consent was waived due to the study’s retrospective design.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
Data sharing statement: The datasets generated and analyzed in the present study are available from the corresponding author upon reasonable request.
Corresponding author: Wen-Ying Dong, Director, Department of Medical Nursing, College of Nursing of Dali University, No. 22 Wanhua Road, Dali 671003, Yunnan Province, China.
Received: March 23, 2026
Revised: April 16, 2026
Accepted: June 8, 2026
Published online: July 28, 2026
Processing time: 127 Days and 4.5 Hours
Revised: April 16, 2026
Accepted: June 8, 2026
Published online: July 28, 2026
Processing time: 127 Days and 4.5 Hours
Core Tip
Core Tip: Glypican-3 is a clinically relevant biomarker in hepatocellular carcinoma, but its expression is usually confirmed only after biopsy or surgical resection. This study developed a computed tomography radiomics-based machine learning nomogram for the preoperative, noninvasive prediction of glypican-3 expression. The combined model integrated a random forest-derived radiomics signature with alpha-fetoprotein and total bilirubin and showed strong predictive performance, with evidence of clinical utility. This imaging-based approach may support preoperative risk stratification and individualized management in patients with hepatocellular carcinoma.