A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: From Traditional to Artificial Intelligence Techniques: From Traditional to Artificial Intelligence Techniques

Fatma Alshohoumi*, Abdullah Al-Hamdani, Rachid Hedjam, Abdul Rahman AlAbdulsalam, Adhari Al Zaabi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


An early evaluation of colorectal cancer liver metastasis (CRCLM) is crucial in determining
treatment options that ultimately affect patient survival rates and outcomes. Radiomics (quantitative
imaging features) have recently gained popularity in diagnostic and therapeutic strategies. Despite
this, radiomics faces many challenges and limitations. This study sheds light on these limitations
by reviewing the studies that used radiomics to predict therapeutic response in CRCLM. Despite
radiomics’ potential to enhance clinical decision-making, it lacks standardization. According to
the results of this study, the instability of radiomics quantification is caused by changes in CT
scan parameters used to obtain CT scans, lesion segmentation methods used for contouring liver
metastases, feature extraction methods, and dataset size used for experimentation and validation.
Accordingly, the study recommends combining radiomics with deep learning to improve prediction
Original languageEnglish
Article number2075
JournalHealthcare (Switzerland)
Issue number10
Publication statusPublished - Oct 19 2022


  • CT
  • colorectal cancer
  • liver metastases
  • radiomics
  • texture features

ASJC Scopus subject areas

  • Leadership and Management
  • Health Policy
  • Health Informatics
  • Health Information Management

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