Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting r
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REVIEW ARTICLE
Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment Nina J. Wesdorp 1 & Tessa Hellingman 1 & Elise P. Jansma 2 & Jan-Hein T. M. van Waesberghe 3 & Ronald Boellaard 4 & Cornelis J. A. Punt 5 & Joost Huiskens 6 & Geert Kazemier 1 Received: 13 July 2020 / Accepted: 29 November 2020 # The Author(s) 2020
Abstract 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. Keywords Gastrointestinal cancer . Advanced analytics . Artificial intelligence . Radiomics . Diagnostic imaging . Treatment response
Nina J. Wesdorp and Tessa Hellingman shared first authorship This article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence) * Nina J. Wesdorp [email protected] 1
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Department of Radiology and Molecular Imaging, Cancer Center Amsterdam, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, The Netherlands
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Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, The Netherlands
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Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
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SAS Institute B.V, Huizen, The Netherlands
Department of Surgery, Cancer Center Amsterdam, Amsterdam University Medical Centers, Vrije Universiteit, Amst
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