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Artificial Intelligence and Machine Learning Predicting Transarterial Chemoembolization Outcomes: A Systematic Review

  • 期刊论文

  • 发表日期：

    2024年12月21日

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  • Elina En Li Cho,
  • Michelle Law,
  • Zhenning Yu,
  • Jie Ning Yong,
  • Claire Shiying Tan,
  • En Ying Tan,
  • Hirokazu Takahashi,
  • Pojsakorn Danpanichkul,
  • Benjamin Nah,
  • Gwyneth Shook Ting Soon,
  • Cheng Han Ng,
  • Darren Jun Hao Tan,
  • Yuya Seko,
  • Toru Nakamura,
  • Asahiro Morishita,
  • Sakkarin Chirapongsathorn,
  • Rahul Kumar,
  • Alfred Wei Chieh Kow,
  • Daniel Q. Huang,
  • Mei Chin Lim,
  • Jia Hao Law

摘要

Background

Major society guidelines recommend transarterial chemoembolization (TACE) as the standard of care for intermediate-stage hepatocellular carcinoma (HCC) patients. However, predicting treatment response remains challenging.

Aims

As artificial intelligence (AI) may predict therapeutic responses, this systematic review aims to assess the performance and effectiveness of radiomics and AI-based models in predicting TACE outcomes in patients with HCC.

Methods

A systemic search was conducted on Medline and Embase databases from inception to 7th April 2024. Included studies generated a predictive model for TACE response and evaluated its performance by area under the curve (AUC), specificity, or sensitivity analysis. Systematic reviews, meta-analyses, case series and reports, pediatric, and animal studies were excluded. Secondary search of references of included articles ensured comprehensiveness.

Results

64 articles, with 13,412 TACE-treated patients, were included. AI in pre-treatment CT scans provided value in predicting the efficacy of TACE in HCC treatment. A positive association was observed for AI in pre-treatment MRI scans. Models incorporating radiomics had numerically better performance than those incorporating manual measured radiological variables. 39 studies demonstrated that combined predictive models had numerically better performance than models based solely on imaging or non-imaging features.

Conclusion

A combined predictive model incorporating clinical features, laboratory investigations, and radiological characteristics may effectively predict response to TACE treatment for HCC.