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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Oct 15, 2025; 17(10): 106844
Published online Oct 15, 2025. doi: 10.4251/wjgo.v17.i10.106844
Published online Oct 15, 2025. doi: 10.4251/wjgo.v17.i10.106844
Machine learning model-based approach using cellular proliferation marker expression for preoperative clinical decision-making in patients with hepatocellular carcinoma
Shashank Kumar, Department of Biochemistry, Central University of Punjab, Bathinda 151401, Punjab, India
Mahendra Pratap Singh, Department of General Surgery, All India Institute of Medical Sciences, Bathinda 151001, India
Lajya Devi Goyal, Department of Obstetrics and Gynaecology, All India Institute of Medical Sciences Bathinda, Batala 151001, Punjab, India
Author contributions: Kumar S wrote the original draft and contributed to conceptualization; Singh MP and Goyal LD contributed to writing, reviewing, and editing.
Conflict-of-interest statement: The authors report no relevant conflicts of interest for this article.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Shashank Kumar, PhD, Professor, Department of Biochemistry, Central University of Punjab, VPO Ghudda Central University of Punjab Lab No. 520, Bathinda 151401, Punjab, India. shashankbiochemau@gmail.com
Received: March 9, 2025
Revised: April 2, 2025
Accepted: April 23, 2025
Published online: October 15, 2025
Processing time: 219 Days and 23.7 Hours
Revised: April 2, 2025
Accepted: April 23, 2025
Published online: October 15, 2025
Processing time: 219 Days and 23.7 Hours
Core Tip
Core Tip: The retrospective study by Zhu et al employed a machine learning model to evaluate cellular proliferation markers in patients with hepatocellular carcinomas, demonstrating its predictive ability and clinical benefits in presurgery treatment decisions. Retrospective cancer prognostic biomarker studies face limitations such as selection bias, data quality, factors affecting biomarker-patient outcomes, and poor generalizability to different populations. The study was based on a small population with no geographical information in the report. The study lacks information on tumor histology (size, number of tumors, grade, and primary/secondary nature), which is highly associated with the marker signature in the samples.