Annals of Hepato-Biliary-Pancreatic Surgery (한국간담췌외과학회지)

The Korean Association of Hepato-Biliary-Pancreatic Surgery (한국간담췌외과학회)
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Predictive modeling algorithms for liver metastasis in colorectal cancer: A systematic review of the current literature

  • Isaac Seow-En (Department of Colorectal Surgery, Singapore General Hospital and National Cancer Centre Singapore) ;
  • Ye Xin Koh (Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Centre Singapore) ;
  • Yun Zhao (Department of Colorectal Surgery, Singapore General Hospital and National Cancer Centre Singapore) ;
  • Boon Hwee Ang (Group Finance Analytics, Singapore Health Services) ;
  • Ivan En-Howe Tan (Group Finance Analytics, Singapore Health Services) ;
  • Aik Yong Chok (Department of Colorectal Surgery, Singapore General Hospital and National Cancer Centre Singapore) ;
  • Emile John Kwong Wei Tan (Department of Colorectal Surgery, Singapore General Hospital and National Cancer Centre Singapore) ;
  • Marianne Kit Har Au (Group Finance Analytics, Singapore Health Services)
  • Received : 2023.06.26
  • Accepted : 2023.08.16
  • Published : 2024.02.29
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Abstract

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

Keywords

References

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