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Updated: Nov 18, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting postoperative liver cancer death outcomes with machine learning.

Yong Wang1, Chaopeng Ji2,3, Ying Wang1

  • 1Department of Anesthesiology, Pain and Perioperative Medicine, The first Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Current Medical Research and Opinion
|February 4, 2021
PubMed
Summary

Machine learning models can predict hepatocellular carcinoma (HCC) postoperative death. Key predictors include preoperative liver function tests and AFP, with Random Forest showing the highest accuracy.

Keywords:
AUCMachine learninghepatocellular carcinomamortalitypostoperative

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Area of Science:

  • Hepatocellular Carcinoma Research
  • Machine Learning in Medicine
  • Prognostic Modeling

Background:

  • Hepatocellular carcinoma (HCC) poses significant challenges in postoperative mortality.
  • Accurate prediction of postoperative outcomes is crucial for patient management and treatment planning.
  • Existing prognostic models may not fully leverage the predictive power of machine learning.

Purpose of the Study:

  • To evaluate the efficacy of five distinct machine learning algorithms in predicting total postoperative death outcomes for HCC patients.
  • To identify the most influential preoperative factors contributing to HCC postoperative mortality.

Main Methods:

  • A secondary data analysis was performed.
  • Prognostic models were developed using machine learning algorithms implemented in Python.
  • Five algorithms were compared for their predictive performance.

Main Results:

  • The Gradient Boosting Machine (GBM) algorithm identified preoperative aspartate aminotransferase (GOT), AFP, alanine aminotransferase (GPT), total bilirubin, and LC3 as the top five predictors.
  • In the test group, the Random Forest model achieved the highest accuracy (0.739), followed by the GBM algorithm (0.714).
  • The area under the curve (AUC) values were highest for Random Forest (0.803), GradientBoosting (0.746), and GBM (0.724).

Conclusions:

  • Machine learning algorithms demonstrate significant potential in predicting postoperative death in hepatocellular carcinoma patients.
  • The study highlights the importance of preoperative clinical and laboratory data in prognostic modeling.
  • Further research can refine these models for improved clinical decision-making.