Computed tomography radiomics combined with clinical parameters for hepatocellular carcinoma differentiation: a machine learning investigation

Aug 3, 2026

Purpose: To evaluate the performance of a combined clinical-radiomics model using multiple machine learning approaches for predicting pathological differentiation in hepatocellular carcinoma (HCC). Material and methods: A retrospective cohort of 196 pathologically confirmed HCC patients with preoperative CT was split into training (n=156) and validation (n=40). Models included a logistic regression clinical model, a radiomics model comparing six classifiers, and an integrated combined model using optimal features. Performance was assessed by AUC, calibration curves, and decision curve analysis, and a nomogram was built for clinical use. Results: BMI and CA153 independently predicted differentiated HCC, with the combined model achieving the highest AUC (training 0.878; validation 0.747). Conclusions: CT radiomics plus clinical parameters with machine learning supports a promising non-invasive approach for preoperative HCC pathological differentiation and treatment planning.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

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Abstract

Purpose

Material and methods

Results

Conclusions

Introduction

Clinical need for differentiation assessment

Limits of conventional imaging

Role of radiomics and study gap

Study objective

© Pol J Radiol 2025; 90 : e140-e150 DOI: [https://doi.org/10.5114/pjr/200631](https://doi.org/10.5114/pjr/200631) [Received: 13.11.2024](Received: 13.11.2024) [Accepted: 29.01.2025](Accepted: 29.01.2025) [Published: 24.03.2025](Published: 24.03.2025) [http://www.polradiol.com](http://www.polradiol.com) Original paper Computed tomography radiomics combined with clinical parameters for hepatocellular carcinoma differentiation: a machine learning investigation Shijing Ma1,2, Yingying Zhu3, Changhong Pu2, Jin Li1,4, Bin Zhong1,5 1School of Basic Medical Sciences, Youjiang Medical University for Nationalities, Baise City, China 2Department of Radiology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise City, China 3Baise City People’s Hospital, Baise City, China 4Department of Biochemistry, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand 5Modern Industrial College of Biomedicine and Great Health, Youjiang Medical University for Nationalities, Baise City, China Abstract Purpose: To evaluate the performance of a combined clinical-radiomics model using multiple machine learning approaches for predicting pathological differentiation in hepatocellular carcinoma (HCC) . Material and methods: A total of 196 patients with pathologically confirmed HCC, who underwent preoperative computed tomography (CT) were retrospectively enrolled (training: n = 156; validation: n = 40). The modelling process included the folowing: (1) clinical model construction through logistic regression analysis of risk factors; (2) radiomics model development by comparing 6 machine learning classifiers; and (3) integration of optimal clinical and radiomic features into a combined model. Model performance was assessed using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA) . A nomogram was constructed for clinical implementation. Results: Two clinical risk factors (BMI and CA153) were identified as independent predictors of differentiated HCC. The clinical model showed moderate performance (AUC: training = 0.705, validation = 0.658) . The radiomics model demonstrated improved prediction capability (AUC: training = 0.840, validation = 0.716) . The combined model achieved the best performance in differentiating HCC pathological grades (AUC: training = 0.878, validation = 0.747) . Conclusions: The integration of CT radiomics features with clinical parameters through machine learning provides a promising non-invasive approach for predicting HCC pathological differentiation. This combined model could serve as a valuable tool for preoperative treatment planning. Keywords: hepatocellular carcinoma, computed tomography, radiomics, machine learning, pathological grading. Introduction The incidence of hepatocellular carcinoma (HCC), the predominant form of primary liver cancer, continues to rise globally, making it a significant cause of cancerrelated mortality [1,2] . Accurate preoperative assessment of HCC pathological differentiation is crucial because it directly influences treatment planning and patient outcomes [3,4] . Evidence suggests that patients with well to moderately differentiated HCC demonstrate superior overall survival rates and lower recurrence risks compared to those with poorly differentiated tumours [5,6] . Although postoperative pathological examination remains the gold standard for determining HCC differentia- Correspondence address: Prof. Bin Zhong & Jin Li, School of Basic Medical Sciences, Youjiang Medical University for Nationalities, China, e-mails: [zb@ymun.edu.cn](zb@ymun.edu.cn); [jin_lijin@cmu.ac.th](jin_lijin@cmu.ac.th) Authors’ contribution: A Study design ∙ B Data collection ∙ C Statistical analysis ∙ D Data interpretation ∙ E Manuscript preparation ∙ F Literature search ∙ G Funds collection e140 This is an Open Access journal, all articles are distributed under the terms of the Creative Commons Attribution-Noncomm

FAQs

What does the study evaluate in hepatocellular carcinoma (HCC)?

The study evaluates how well a combined clinical-radiomics model predicts HCC pathological differentiation using multiple machine learning approaches.

How were the prediction models constructed and validated?

Patients with preoperative CT were retrospectively enrolled (training n=156, validation n=40). The workflow included a logistic regression clinical model, a radiomics model comparing six classifiers, and an integrated combined model using optimal clinical and radiomic features.

Which factors and model performed best for predicting differentiated HCC?

BMI and CA153 were identified as independent clinical predictors. The combined clinical-radiomics model achieved the best performance, with AUC values of 0.878 (training) and 0.747 (validation).

Aug 3, 2026

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