Journal
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
Volume 48, Issue 6, Pages 1785-1794Publisher
SPRINGER
DOI: 10.1007/s00259-020-05142-w
Keywords
Gastrointestinal cancer; Advanced analytics; Artificial intelligence; Radiomics; Diagnostic imaging; Treatment response
Funding
- Amsterdam UMC (Vrije Universiteit Amsterdam)
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Radiomics is increasingly utilized to predict treatment response in patients with gastrointestinal cancer, showing potential for improving patient selection and treatment strategy adjustment in a non-invasive manner. However, challenges such as external validation and implementation in clinical practice still exist.
Purpose Advanced medical image analytics is increasingly used to predict clinical outcome in patients diagnosed with gastrointestinal tumors. This review provides an overview on the value of radiomics in predicting response to treatment in patients with gastrointestinal tumors. Methods A systematic review was conducted, according to PRISMA guidelines. The protocol was prospectively registered (PROSPERO: CRD42019128408). PubMed, Embase, and Cochrane databases were searched. Original studies reporting on the value of radiomics in predicting response to treatment in patients with a gastrointestinal tumor were included. A narrative synthesis of results was conducted. Results were stratified by tumor type. Quality assessment of included studies was performed, according to the radiomics quality score. Results The comprehensive literature search identified 1360 unique studies, of which 60 articles were included for analysis. In 37 studies, radiomics models and individual radiomic features showed good predictive performance for response to treatment (area under the curve or accuracy > 0.75). Various strategies to construct predictive models were used. Internal validation of predictive models was often performed, while the majority of studies lacked external validation. None of the studies reported predictive models implemented in clinical practice. Conclusion Radiomics is increasingly used to predict response to treatment in patients suffering from gastrointestinal cancer. This review demonstrates its great potential to help predict response to treatment and improve patient selection and early adjustment of treatment strategy in a non-invasive manner.
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