Radiomics : A primer for the radiation oncologist
| Autor: | Jean-Emmanuel Bibault, R. El Ayachy, L. Xing, Anita Burgun, Pierre Decazes, Paul Giraud, Nicolas Giraud |
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| Přispěvatelé: | Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Université Paris Cité (UPCité), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Stanford University School of Medicine [CA, USA], CHU Bordeaux [Bordeaux], Hôpital Haut-Lévêque [CHU Bordeaux], Imagerie médicale - Médecine nucléaire [Rouen], CHU Rouen, Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Centre de Lutte Contre le Cancer Henri Becquerel Normandie Rouen (CLCC Henri Becquerel), Equipe Quantification en Imagerie Fonctionnelle (QuantIF-LITIS), Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), CCSD, Accord Elsevier, Université de Paris (UP), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Data Analysis
Lung Neoplasms Esophageal Neoplasms medicine.medical_treatment [SDV]Life Sciences [q-bio] Oncologie Clinical oncology Radiation oncology 0302 clinical medicine Neoplasms Medicine Radiation oncologist Data Curation Brain Neoplasms Radiation Oncologists 3. Good health [SDV] Life Sciences [q-bio] Phenotype Oncology Head and Neck Neoplasms 030220 oncology & carcinogenesis Diagnostic Imaging medicine.medical_specialty Apprentissage profond Clinique MEDLINE 03 medical and health sciences Machine learning Medical imaging Humans Radiology Nuclear Medicine and imaging Medical physics Radiomique Retrospective Studies Radiothérapie Modalities Radiomics Data curation Radiotherapy business.industry Rectal Neoplasms Radiotherapy Planning Computer-Assisted Modeling Reproducibility of Results Retrospective cohort study Deep learning Radiation therapy Pancreatic Neoplasms Modélisation Informatics business |
| Zdroj: | Cancer/Radiothérapie Cancer/Radiothérapie, 2020, 24, pp.403-410. ⟨10.1016/j.canrad.2020.01.011⟩ Cancer Radiothérapie Cancer Radiothérapie, Elsevier Masson, 2020, 24, pp.403-410. ⟨10.1016/j.canrad.2020.01.011⟩ |
| ISSN: | 1278-3218 1769-6658 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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