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Updated: Jan 24, 2026

Transradial Access Chemoembolization for Hepatocellular Carcinoma Patients
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Influencing Factors for Postembolization Fever in Patients Undergoing Transarterial Chemoembolization Based on

Won-Du Chang1, Myoung Soo Kim2

  • 1Department of Artificial Intelligence and Convergence, Pukyong National University, Busan, South Korea.

Computers, Informatics, Nursing : CIN
|January 23, 2026
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Summary
This summary is machine-generated.

Machine learning models can predict postembolization fever after transarterial chemoembolization (TACE). Key factors include pre- and post-TACE lab values, tumor size, and treatment agents, aiding clinical monitoring.

Keywords:
fevermachine learningpredictive learning modelsrisk factorstherapeutic chemoembolization

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Area of Science:

  • Oncology
  • Medical Imaging
  • Machine Learning

Background:

  • Postembolization fever is a common complication of transarterial chemoembolization (TACE).
  • Currently, no established framework exists for predicting postembolization fever.
  • Predictive factors may encompass demographic, clinical, laboratory, and radiologic data.

Purpose of the Study:

  • To develop and validate machine learning-based prediction models for postembolization fever following TACE.
  • To identify key clinical, laboratory, and radiologic predictors of postembolization fever.

Main Methods:

  • Retrospective review of data from 1495 patients who underwent TACE.
  • Development of seven machine learning algorithms using SPSS and Python.
  • Validation of prediction models for postembolization fever occurrence.

Main Results:

  • An ensemble method demonstrated the best performance in predicting postembolization fever.
  • Positive predictors included post-TACE AST, CRP, ALT, bilirubin, INR, platelets, pre-TACE AST, alpha-fetoprotein, platelets, lipiodol/doxorubicin amounts, and tumor size >5 cm.
  • Negative predictors included post-TACE lymphocyte/monocyte counts, albumin, pre-TACE albumin/lymphocyte count, and likely hepatocellular carcinoma.

Conclusions:

  • Machine learning models can effectively predict postembolization fever after TACE.
  • Clinicians should monitor various patient data before and after TACE to assess fever risk.
  • Awareness of TACE side effects and management strategies is crucial for healthcare professionals.