Kumar S, Singh MP, Goyal LD. Machine learning model-based approach using cellular proliferation marker expression for preoperative clinical decision-making in patients with hepatocellular carcinoma. World J Gastrointest Oncol 2025; 17(10): 106844 [PMID: 41114116 DOI: 10.4251/wjgo.v17.i10.106844]
Corresponding Author of This Article
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
Research Domain of This Article
Oncology
Article-Type of This Article
Letter to the Editor
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastrointest Oncol. Oct 15, 2025; 17(10): 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
Abstract
The investigation by Zhu et al on the assessment of cellular proliferation markers to assist clinical decision-making in patients with hepatocellular carcinoma (HCC) using a machine learning model-based approach is a scientific approach. This study looked into the possibilities of using a Ki-67 (a marker for cell proliferation) expression-based machine learning model to help doctors make decisions about treatment options for patients with HCC before surgery. The study used reconstructed tomography images of 164 patients with confirmed HCC from the intratumoral and peritumoral regions. The features were chosen using various statistical methods, including least absolute shrinkage and selection operator regression. Also, a nomogram was made using Radscore and clinical risk factors. It was tested for its ability to predict receiver operating characteristic curves and calibration curves, and its clinical benefits were found using decision curve analysis. The calibration curve demonstrated excellent consistency between predicted and actual probability, and the decision curve confirmed its clinical benefit. The proposed model is helpful for treating patients with HCC because the predicted and actual probabilities are very close to each other, as shown by the decision curve analysis. Further prospective studies are required, incorporating a multicenter and large sample size design, additional relevant exclusion criteria, information on tumors (size, number, and grade), and cancer stage to strengthen the clinical benefit in patients with HCC.
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.
