The Image Biomarker Standardization Initiative: standardized quantitative radiomics for highthroughput image-based phenotyping

Autor: Olivier Morin, Mahmoud A. Abdalah, Steffen Löck, Sandy Napel, Lisanne V. van Dijk, Arvind Rao, Muhammad Siddique, Jacopo Lenkowicz, Ronald Boellaard, Issam El Naqa, Jairo Socarras Fernandez, Marta Bogowicz, Spyridon Bakas, Jonas Scherer, Are Losnegård, Marie-Charlotte Desseroit, Robert J. Gillies, Gary Cook, Martin Vallières, Vincenzo Valentini, Alex Zwanenburg, Philip Whybra, Sarthak Pati, Sebastian Echegaray, Matthias Guckenberger, Christos Davatzikos, Emiliano Spezi, Roberto Gatta, Nicola Dinapoli, Daniela Thorwarth, Luca Boldrini, Roel J H M Steenbakkers, Esther G.C. Troost, Joost J. M. van Griethuysen, Irène Buvat, Sung Min Ha, Fiona Lippert, Hugo J.W.L. Aerts, Klaus H. Maier-Hein, Fanny Orlhac, Cuong V. Dinh, Stefan Leger, Philippe Lambin, Ralph T.H. Leijenaar, Fabian Isensee, Mathieu Hatt, Vicky Goh, Christian Richter, Roelof J. Beukinga, Henning Müller, Elisabeth Pfaehler, Nanna M. Sijtsema, Floris H. P. van Velden, Aditya Apte, Michael Götz, Arman Rahmim, Vincent Andrearczyk, Stephanie Tanadini-Lang, Adrien Depeursinge, Christophe Nioche, Andriy Fedorov, Saeed Ashrafinia, Taman Upadhaya

Publikováno v: Radiology 295(2020)2, 328-338
Radiology
Radiology, Radiological Society of North America, 2020, 295 (2), pp.328-338. ⟨10.1148/radiol.2020191145⟩
Radiology, 295(2), 328-338. RADIOLOGICAL SOC NORTH AMERICA
Zwanenburg, A, Vallières, M, Abdalah, M A, Aerts, H J W L, Andrearczyk, V, Apte, A, Ashrafinia, S, Bakas, S, Beukinga, R J, Boellaard, R, Bogowicz, M, Boldrini, L, Buvat, I, Cook, G J R, Davatzikos, C, Depeursinge, A, Desseroit, M C, Dinapoli, N, Dinh, C V & Echegaray, S 2020, ' The image biomarker standardization initiative : Standardized quantitative radiomics for high-throughput image-based phenotyping ', Radiology, vol. 295, no. 2, pp. 328-338 . https://doi.org/10.1148/radiol.2020191145
Radiology, vol 295, iss 2
Radiology, 295(2), 328-338. Radiological Society of North America, Inc.
Radiology, 2020, 295 (2), pp.328-338. ⟨10.1148/radiol.2020191145⟩
Radiology, 295(2), 328-338. Radiological Society of North America Inc.

International audience; Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a07c005740a02571d8d74ebfc9433efe
https://www.hzdr.de/publications/Publ-30321-1

Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images

Autor: Sebastian Echegaray, Sandy Napel, Daniel L. Rubin, Shaimaa Bakr

Publikováno v: Journal of Digital Imaging. 31:403-414

The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and buildi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cef50bbba31f2fe5953f6a10925b1fd8
https://doi.org/10.1007/s10278-017-0019-x

[18F] FDG Positron Emission Tomography (PET) Tumor and Penumbra Imaging Features Predict Recurrence in Non–Small Cell Lung Cancer

Autor: Sandy Napel, Ann N. Leung, George Horng, Sebastian Echegaray, Sarah A. Mattonen, Shaimaa Bakr, Guido Davidzon, Minal Vasanawala, Viswam S. Nair

Publikováno v: Tomography
Volume 5
Issue 1
Pages 145-153
Tomography; Volume 5; Issue 1; Pages: 145-153

We identified computational imaging features on 18F-fluorodeoxyglucose positron emission tomography (PET) that predict recurrence/progression in non–small cell lung cancer (NSCLC). We retrospectively identified 291 patients with NSCLC from 2 prospe

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c85a5cd7d9c6e1e21dbacee2c639cdfd

Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features

Autor: Jayashree Kalpathy-Cramer, Lubomir M. Hadjiiski, Jessica C. Sieren, Sandy Napel, Johanna Uthoff, Brandon Driscoll, Dmitry B. Goldgof, Sebastian Echegaray, Yoganand Balagurunathan, Pechin Lo, Artem Mamomov, Robert J. Gillies, Samantha K. N. Dilger, Binsheng Zhao, Dmitry Cherezov, Michael F. McNitt-Gray, Kenny H. Cha, Lin Lu, Ivan Yeung, Daniel L. Rubin

Publikováno v: Tomography : a journal for imaging research
Tomography
Volume 2
Issue 4
Pages 430-437
Tomography (Ann Arbor, Mich.), vol 2, iss 4
Tomography; Volume 2; Issue 4; Pages: 430-437

Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic “feature” sets to characterize tumo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c09fe2ac136ba0c643a4c8cd914e6eff
http://europepmc.org/articles/PMC5279995

A radiogenomic dataset of non-small cell lung cancer

Autor: Shaimaa Bakr, Hong Zheng, Sylvia K. Plevritis, Ann N. Leung, Sandy Napel, Sebastian Echegaray, Olivier Gevaert, Andrew Quon, Mu Zhou, Joseph B. Shrager, Michael A. Kadoch, Jalen Benson, Kelsey Ayers, Majid Shafiq, Weiruo Zhang, Chuong D. Hoang, Daniel L. Rubin

Publikováno v: Scientific Data

Medical image biomarkers of cancer promise improvements in patient care through advances in precision medicine. Compared to genomic biomarkers, image biomarkers provide the advantages of being non-invasive, and characterizing a heterogeneous tumor in

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf54a2bd198d2b83aef377913b1f2932