A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: 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 journalReview articlepeer-review

Abstract

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 accuracy.

Original languageEnglish
Article number2075
JournalHealthcare (Switzerland)
Volume10
Issue number10
DOIs
Publication statusPublished - Oct 2022
Externally publishedYes

Keywords

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

ASJC Scopus subject areas

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

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