CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma
Thibaud P. Coroller,Thibaud P. Coroller,Patrick Grossmann,Patrick Grossmann,Ying Hou,Emmanuel Rios Velazquez,Ralph T.H. Leijenaar,Gretchen Hermann,Philippe Lambin,Benjamin Haibe-Kains,Benjamin Haibe-Kains,Raymond H. Mak,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts +13 more
TLDR
In this article, the authors evaluated computed-tomography (CT) radiomic features for their capability to predict distant metastasis (DM) for lung adenocarcinoma patients.About:
This article is published in Radiotherapy and Oncology.The article was published on 2015-03-01 and is currently open access. It has received 524 citations till now.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
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
Artificial intelligence in radiology
Ahmed Hosny,Chintan Parmar,John Quackenbush,Lawrence H. Schwartz,Lawrence H. Schwartz,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts +6 more
TL;DR: A general understanding of AI methods, particularly those pertaining to image-based tasks, is established and how these methods could impact multiple facets of radiology is explored, with a general focus on applications in oncology.
Journal ArticleDOI
Applications and limitations of radiomics
TL;DR: This technical review will review radiomic application areas and technical issues, as well as proper practices for the designs of radiomic studies.
Journal ArticleDOI
Machine Learning methods for Quantitative Radiomic Biomarkers
Chintan Parmar,Chintan Parmar,Patrick Grossmann,Johan Bussink,Philippe Lambin,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts +6 more
TL;DR: Identification of optimal machine-learning methods for radiomic applications is a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor-phenotypic characteristics in clinical practice.
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