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.
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 language | English |
|---|---|
| Journal | Healthcare (Switzerland) |
| Publication status | Published - 2022 |