J-E Bibault1, L Xing2, P Giraud7, R El Ayachy3, N Giraud4, P Decazes5, A Burgun6, P Giraud7. 1. Radiation Oncology Department, hôpital européen Georges-Pompidou, Assistance publique-Hôpitaux de Paris, 20, rue Leblanc, 75015 Paris, France; Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France; Inserm, UMR 1138, Team 22: Information Sciences to support Personalized Medicine, 15, rue de l'École-de-Médecine, 75006 Paris, France. Electronic address: jean-emmanuel.bibault@aphp.fr. 2. Laboratory of Artificial Intelligence in Medicine and Biomedical Physics, Stanford University School of Medicine, 875 Blake Wilbur Drive, 94305-5847 Stanford, CA, USA. 3. Inserm, UMR 1138, Team 22: Information Sciences to support Personalized Medicine, 15, rue de l'École-de-Médecine, 75006 Paris, France. 4. Radiation Oncology Department, CHU de Bordeaux, hôpital Haut-Lévêque, avenue Magellan, 33600 Pessac, France. 5. Nuclear Medicine Department, centre Henri-Becquerel, 1, rue d'Amiens, 76038 Rouen, France; Quantif, EA 4108, université de Rouen, avenue de l'Université, 76801 Saint-Étienne-du-Rouvray, France. 6. Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France; Inserm, UMR 1138, Team 22: Information Sciences to support Personalized Medicine, 15, rue de l'École-de-Médecine, 75006 Paris, France; Biomedical Informatics and Public Health Department, hôpital européen Georges-Pompidou, Assistance publique-hôpitaux de Paris, 20, rue Leblanc, 75015 Paris, France. 7. Radiation Oncology Department, hôpital européen Georges-Pompidou, Assistance publique-Hôpitaux de Paris, 20, rue Leblanc, 75015 Paris, France; Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France.
Abstract
PURPOSE: Radiomics are a set of methods used to leverage medical imaging and extract quantitative features that can characterize a patient's phenotype. All modalities can be used with several different software packages. Specific informatics methods can then be used to create meaningful predictive models. In this review, we will explain the major steps of a radiomics analysis pipeline and then present the studies published in the context of radiation therapy. METHODS: A literature review was performed on Medline using the search engine PubMed. The search strategy included the search terms "radiotherapy", "radiation oncology" and "radiomics". The search was conducted in July 2019 and reference lists of selected articles were hand searched for relevance to this review. RESULTS: A typical radiomics workflow always includes five steps: imaging and segmenting, data curation and preparation, feature extraction, exploration and selection and finally modeling. In radiation oncology, radiomics studies have been published to explore different clinical outcome in lung (n=5), head and neck (n=5), esophageal (n=3), rectal (n=3), pancreatic (n=2) cancer and brain metastases (n=2). The quality of these retrospective studies is heterogeneous and their results have not been translated to the clinic. CONCLUSION: Radiomics has a great potential to predict clinical outcome and better personalize treatment. But the field is still young and constantly evolving. Improvement in bias reduction techniques and multicenter studies will hopefully allow more robust and generalizable models.
PURPOSE: Radiomics are a set of methods used to leverage medical imaging and extract quantitative features that can characterize a patient's phenotype. All modalities can be used with several different software packages. Specific informatics methods can then be used to create meaningful predictive models. In this review, we will explain the major steps of a radiomics analysis pipeline and then present the studies published in the context of radiation therapy. METHODS: A literature review was performed on Medline using the search engine PubMed. The search strategy included the search terms "radiotherapy", "radiation oncology" and "radiomics". The search was conducted in July 2019 and reference lists of selected articles were hand searched for relevance to this review. RESULTS: A typical radiomics workflow always includes five steps: imaging and segmenting, data curation and preparation, feature extraction, exploration and selection and finally modeling. In radiation oncology, radiomics studies have been published to explore different clinical outcome in lung (n=5), head and neck (n=5), esophageal (n=3), rectal (n=3), pancreatic (n=2) cancer and brain metastases (n=2). The quality of these retrospective studies is heterogeneous and their results have not been translated to the clinic. CONCLUSION: Radiomics has a great potential to predict clinical outcome and better personalize treatment. But the field is still young and constantly evolving. Improvement in bias reduction techniques and multicenter studies will hopefully allow more robust and generalizable models.
Authors: Radouane El Ayachy; Nicolas Giraud; Paul Giraud; Catherine Durdux; Philippe Giraud; Anita Burgun; Jean Emmanuel Bibault Journal: Front Oncol Date: 2021-05-05 Impact factor: 6.244