Radiomics: extracting more information from medical images using advanced feature analysis.
Philippe Lambin,Emmanuel Rios-Velazquez,Ralph T.H. Leijenaar,Sara Carvalho,Ruud G.P.M. van Stiphout,Patrick V. Granton,Catharina M.L. Zegers,Robert J. Gillies,Ronald Boellard,Andre Dekker,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts +11 more
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TLDR
Radiomics--the high-throughput extraction of large amounts of image features from radiographic images--addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory.About:
This article is published in European Journal of Cancer.The article was published on 2012-03-01 and is currently open access. It has received 3411 citations till now. The article focuses on the topics: Medical imaging & Image processing.read more
Citations
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Journal ArticleDOI
Radiomics: Images Are More than Pictures, They Are Data.
TL;DR: This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
Journal ArticleDOI
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Hugo J.W.L. Aerts,Emmanuel Rios Velazquez,Ralph T.H. Leijenaar,Chintan Parmar,Patrick Grossmann,Sara Carvalho,Sara Cavalho,Johan Bussink,René Monshouwer,Benjamin Haibe-Kains,Derek H. F. Rietveld,Frank J. P. Hoebers,Michelle M. Rietbergen,C. René Leemans,Andre Dekker,John Quackenbush,Robert J. Gillies,Philippe Lambin +17 more
TL;DR: The data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer, which may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.
Journal ArticleDOI
Computational Radiomics System to Decode the Radiographic Phenotype
Joost J. M. van Griethuysen,Joost J. M. van Griethuysen,Joost J. M. van Griethuysen,Andriy Fedorov,Chintan Parmar,Ahmed Hosny,Nicole Aucoin,Vivek Narayan,Regina G. H. Beets-Tan,Regina G. H. Beets-Tan,Jean-Christophe Fillion-Robin,Steve Pieper,Hugo J.W.L. Aerts +12 more
TL;DR: PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images, is developed and its application in characterizing lung lesions is demonstrated.
Journal ArticleDOI
Radiomics: the bridge between medical imaging and personalized medicine
Philippe Lambin,Ralph T.H. Leijenaar,Timo M. Deist,Jurgen Peerlings,Evelyn E.C. de Jong,Janita E. van Timmeren,Sebastian Sanduleanu,Ruben T. H. M. Larue,Aniek J.G. Even,Arthur Jochems,Yvonka van Wijk,Henry C. Woodruff,Johan van Soest,Tim Lustberg,Erik Roelofs,Wouter van Elmpt,Andre Dekker,Felix M. Mottaghy,Felix M. Mottaghy,Joachim E. Wildberger,Sean Walsh +20 more
TL;DR: Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research as mentioned in this paper.
Journal ArticleDOI
Convolutional neural networks: an overview and application in radiology
TL;DR: A perspective on the basic concepts of convolutional neural network and its application to various radiological tasks is offered, and its challenges and future directions in the field of radiology are discussed.
References
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Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer
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TL;DR: Textural features of tumor metabolic distribution extracted from baseline 18F-FDG PET images allow for the best stratification of esophageal carcinoma patients in the context of therapy-response prediction.
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