Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review |
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| Authors: | Femke C.R. Staal Denise J. van der Reijd Marjaneh Taghavi Doenja M.J. Lambregts Regina G.H. Beets-Tan Monique Maas |
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| Affiliation: | 1. Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands;2. GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands;3. Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark;1. GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD, The Netherlands;2. Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands;3. Department of Radiology, Maastricht University Medical Center, P.O. Box 6200, 6202 AZ Maastricht, The Netherlands;4. Department of Surgery, Maastricht University Medical Center, P.O. Box 6200, 6202 AZ Maastricht, The Netherlands;5. Department of Radiology, Zhongshan Hospital, Fudan University,180 Fenglin Road Shangai 200032, China;6. Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University, P.O. Box 6200, 6202 AZ Maastricht, , The Netherlands;7. Department of Surgery, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands;8. NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University, P.O. Box 616, 6200 MD, The Netherlands;9. Department of Surgery, RWTH Universitätsklinikum Aachen, Pauwelsstraße 30, 52074 Aachen, Germany;10. Department of Surgical Oncology, Erasmus MC Cancer Institute, Groene Hilledijk 301, 3075 EA, Rotterdam, The Netherlands;1. Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands;2. Department of Radiation Oncology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands;3. Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands;4. Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA;5. Department of Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands;6. Department of Radiology, Moffitt Cancer Center, Tampa, FL, USA;1. Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA;2. Departments of Radiology and Physics & Astronomy, University of British Columbia, Vancouver, BC, Canada;3. Department of Nuclear Medicine and PET Center, Aarhus University Hospital, Aarhus, Denmark;4. Department of Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD, USA;5. School of Biomedical Engineering, Southern Medical University, Guangzhou, China;6. Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA;7. Department of Radiology, University of Texas Southwestern Medical Center, TX, USA;1. Department of Radiation Oncology, Institut Jean Godinot, Reims, France;2. Radiology, Centre Hospitalier Universitaire de Reims, France;3. Biostatistics Unit, Centre Oscar Lambret, Lille, France;4. CESP, INSERM, Paris-Sud Paris-Orsay University, Villejuif, France;5. Department of Surgery, Institut Jean Godinot, Reims, France;6. Department of Radiation Oncology, Centre Oscar Lambret, Lille, France;7. CRESTIC, University of Reims, France;1. Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjyo, Chuo-ku, Kumamoto, 860-8556, Japan;2. Department of Radiology, Kumamoto General Hospital, 10-10 Tori-cho, Yatsushiro, Kumamoto, 866-8660, Japan;3. Kumamoto General Health Center, 4-11-1 Higashimachi, Higashi-ku, Kumamoto, 862-0901, Japan;4. Department of Medical Physics, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjyo, Chuo-ku, Kumamoto, 860-8556, Japan;5. Department of Molecular Laboratory Medicine, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjyo, Chuo-ku, Kumamoto, 860-8556, Japan;6. Department of Gastroenterological Surgery, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjyo, Chuo-ku, Kumamoto, 860-8556, Japan;1. Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia;2. Sir Peter MacCallum Department of Oncology, University of Melbourne, Peter MacCallum Cancer Centre, Melbourne, Australia;3. Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia;4. Department of Biomedical Engineering, School of Engineering, University of Melbourne, Melbourne, Australia;5. Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, USA;6. Department of Radiation Oncology, Penn State Cancer Institute, Hershey, USA;7. Department of Public Health Sciences, Penn State College of Medicine, Hershey, USA;8. Health Sciences Library, Peter MacCallum Cancer Centre, Parkville, Australia;9. Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia |
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| Abstract: | Prediction of outcome in patients with colorectal cancer (CRC) is challenging as a result of lack of a robust biomarker and heterogeneity between and within tumors. The aim of this review was to assess the current possibilities and limitations of radiomics (on computed tomography [CT], magnetic resonance imaging [MRI], and positron emission tomography [PET]) for the prediction of treatment outcome and long-term outcome in CRC. Medline/PubMed was searched up to August 2020 for studies that used radiomics for the prediction of response to treatment and survival in patients with CRC (based on pretreatment imaging). The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool and Radiomics Quality Score (RQS) were used for quality assessment. A total of 76 studies met the inclusion criteria and were included for further analysis. Radiomics analyses were performed on MRI in 41 studies, on CT in 30 studies, and on 18F-FDG-PET/CT in 10 studies. Heterogeneous results were reported regarding radiomics methods and included features. High-quality studies (n = 13), consisting mainly of MRI-based radiomics to predict response in rectal cancer, were able to predict response with good performance. Radiomics literature in CRC is highly heterogeneous, but it nonetheless holds promise for the prediction of outcome. The most evidence is available for MRI-based radiomics in rectal cancer. Future radiomics research in CRC should focus on independent validation of existing models rather than on developing new models. |
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| Keywords: | Artificial intelligence Metastasis Neoadjuvant chemotherapy Quantitative imaging analysis Response |
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