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1. Verfasser: Pavic Matea
Format: Artículo científico
Sprache:en
Veröffentlicht: Universität Bern 2021
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Online-Zugang:https://www.redalyc.org/articulo.oa?id=664472002002
https://www.redalyc.org/journal/6644/664472002002/
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author Pavic Matea
author_facet Pavic Matea
contents Radiomics in oncology -uncovering tumor phenotype from medical images: a short introduction Pavic Matea Janita E. van Timmeren Medicina Radiomics Image analysis Machine learning Prognostic modelling Clinical decision support system Radiomics is a promising method to quantify and describe the tumor phenotype on medical images. High numbers of image features are extracted from medical images and can be used within a clinical decision support system by integrating this data with clinical and pathological variables. Herein, we give a short introduction into this image analysis method and present an overview on the workflow. 2021 artículo científico 1663-618X https://www.redalyc.org/articulo.oa?id=664472002002 https://www.redalyc.org/journal/6644/664472002002/ https://www.redalyc.org/journal/6644/664472002002/html/ https://www.redalyc.org/journal/6644/664472002002/664472002002.epub https://www.redalyc.org/journal/6644/664472002002/movil en http://www.redalyc.org/revista.oa?id=6644 Journal of Radiation Oncology Informatics application/pdf Universität Bern Journal of Radiation Oncology Informatics (Suiza) Num.1 Vol.11
format Artículo científico
id redalyc_664472002002
language en
publishDate 2021
publisher Universität Bern
spellingShingle Radiomics in oncology -uncovering tumor phenotype from medical images: a short introduction
Pavic Matea
Medicina
Radiomics
Image analysis
Machine learning
Prognostic modelling
Clinical decision support system
Radiomics in oncology -uncovering tumor phenotype from medical images: a short introduction Pavic Matea Janita E. van Timmeren Medicina Radiomics Image analysis Machine learning Prognostic modelling Clinical decision support system Radiomics is a promising method to quantify and describe the tumor phenotype on medical images. High numbers of image features are extracted from medical images and can be used within a clinical decision support system by integrating this data with clinical and pathological variables. Herein, we give a short introduction into this image analysis method and present an overview on the workflow. 2021 artículo científico 1663-618X https://www.redalyc.org/articulo.oa?id=664472002002 https://www.redalyc.org/journal/6644/664472002002/ https://www.redalyc.org/journal/6644/664472002002/html/ https://www.redalyc.org/journal/6644/664472002002/664472002002.epub https://www.redalyc.org/journal/6644/664472002002/movil en http://www.redalyc.org/revista.oa?id=6644 Journal of Radiation Oncology Informatics application/pdf Universität Bern Journal of Radiation Oncology Informatics (Suiza) Num.1 Vol.11
title Radiomics in oncology -uncovering tumor phenotype from medical images: a short introduction
topic Medicina
Radiomics
Image analysis
Machine learning
Prognostic modelling
Clinical decision support system
url https://www.redalyc.org/articulo.oa?id=664472002002
https://www.redalyc.org/journal/6644/664472002002/
https://www.redalyc.org/journal/6644/664472002002/html/
https://www.redalyc.org/journal/6644/664472002002/664472002002.epub
https://www.redalyc.org/journal/6644/664472002002/movil