Literature DB >> 33597610

Radiomics feature robustness as measured using an MRI phantom.

Joonsang Lee1,2, Angela Steinmann1, Yao Ding1, Hannah Lee1, Constance Owens1, Jihong Wang1, Jinzhong Yang1, David Followill1, Rachel Ger1, Dennis MacKin1, Laurence E Court3.   

Abstract

Radiomics involves high-throughput extraction of large numbers of quantitative features from medical images and analysis of these features to predict patients' outcome and support clinical decision-making. However, radiomics features are sensitive to several factors, including scanning protocols. The purpose of this study was to investigate the robustness of magnetic resonance imaging (MRI) radiomics features with various MRI scanning protocol parameters and scanners using an MRI radiomics phantom. The variability of the radiomics features with different scanning parameters and repeatability measured using a test-retest scheme were evaluated using the coefficient of variation and intraclass correlation coefficient (ICC) for both T1- and T2-weighted images. For variability measures, the features were categorized into three groups: large, intermediate, and small variation. For repeatability measures, the average T1- and T2-weighted image ICCs for the phantom (0.963 and 0.959, respectively) were higher than those for a healthy volunteer (0.856 and 0.849, respectively). Our results demonstrated that various radiomics features are dependent on different scanning parameters and scanners. The radiomics features with a low coefficient of variation and high ICC for both the phantom and volunteer can be considered good candidates for MRI radiomics studies. The results of this study will assist current and future MRI radiomics studies.

Entities:  

Year:  2021        PMID: 33597610     DOI: 10.1038/s41598-021-83593-3

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  34 in total

1.  Texture Feature Ratios from Relative CBV Maps of Perfusion MRI Are Associated with Patient Survival in Glioblastoma.

Authors:  J Lee; R Jain; K Khalil; B Griffith; R Bosca; G Rao; A Rao
Journal:  AJNR Am J Neuroradiol       Date:  2015-10-15       Impact factor: 3.825

2.  Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery.

Authors:  Margarita Kirienko; Luca Cozzi; Lidija Antunovic; Lisa Lozza; Antonella Fogliata; Emanuele Voulaz; Alexia Rossi; Arturo Chiti; Martina Sollini
Journal:  Eur J Nucl Med Mol Imaging       Date:  2017-09-24       Impact factor: 9.236

3.  MR-based radiomics signature in differentiating ocular adnexal lymphoma from idiopathic orbital inflammation.

Authors:  Jian Guo; Zhenyu Liu; Chen Shen; Zheng Li; Fei Yan; Jie Tian; Junfang Xian
Journal:  Eur Radiol       Date:  2018-04-09       Impact factor: 5.315

Review 4.  Radiomics: the process and the challenges.

Authors:  Virendra Kumar; Yuhua Gu; Satrajit Basu; Anders Berglund; Steven A Eschrich; Matthew B Schabath; Kenneth Forster; Hugo J W L Aerts; Andre Dekker; David Fenstermacher; Dmitry B Goldgof; Lawrence O Hall; Philippe Lambin; Yoganand Balagurunathan; Robert A Gatenby; Robert J Gillies
Journal:  Magn Reson Imaging       Date:  2012-08-13       Impact factor: 2.546

Review 5.  Radiomics: extracting more information from medical images using advanced feature analysis.

Authors:  Philippe Lambin; Emmanuel Rios-Velazquez; Ralph Leijenaar; Sara Carvalho; Ruud G P M van Stiphout; Patrick Granton; Catharina M L Zegers; Robert Gillies; Ronald Boellard; André Dekker; Hugo J W L Aerts
Journal:  Eur J Cancer       Date:  2012-01-16       Impact factor: 9.162

6.  Radiomics analysis at PET/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy.

Authors:  Anastasia Oikonomou; Farzad Khalvati; Pascal N Tyrrell; Masoom A Haider; Usman Tarique; Laura Jimenez-Juan; Michael C Tjong; Ian Poon; Armin Eilaghi; Lisa Ehrlich; Patrick Cheung
Journal:  Sci Rep       Date:  2018-03-05       Impact factor: 4.379

7.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.

Authors:  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 Rietveld; Frank Hoebers; Michelle M Rietbergen; C René Leemans; Andre Dekker; John Quackenbush; Robert J Gillies; Philippe Lambin
Journal:  Nat Commun       Date:  2014-06-03       Impact factor: 14.919

8.  Radiomics: Images Are More than Pictures, They Are Data.

Authors:  Robert J Gillies; Paul E Kinahan; Hedvig Hricak
Journal:  Radiology       Date:  2015-11-18       Impact factor: 11.105

9.  Prognostic Impact of the Findings on Thin-Section Computed Tomography in stage I lung adenocarcinoma with visceral pleural invasion.

Authors:  Mei Yuan; Jin-Yuan Liu; Teng Zhang; Yu-Dong Zhang; Hai Li; Tong-Fu Yu
Journal:  Sci Rep       Date:  2018-03-16       Impact factor: 4.379

10.  Lesion location implemented magnetic resonance imaging radiomics for predicting IDH and TERT promoter mutations in grade II/III gliomas.

Authors:  Hideyuki Arita; Manabu Kinoshita; Atsushi Kawaguchi; Masamichi Takahashi; Yoshitaka Narita; Yuzo Terakawa; Naohiro Tsuyuguchi; Yoshiko Okita; Masahiro Nonaka; Shusuke Moriuchi; Masatoshi Takagaki; Yasunori Fujimoto; Junya Fukai; Shuichi Izumoto; Kenichi Ishibashi; Yoshikazu Nakajima; Tomoko Shofuda; Daisuke Kanematsu; Ema Yoshioka; Yoshinori Kodama; Masayuki Mano; Kanji Mori; Koichi Ichimura; Yonehiro Kanemura
Journal:  Sci Rep       Date:  2018-08-06       Impact factor: 4.379

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  10 in total

1.  Repeatability and reproducibility of magnetic resonance imaging-based radiomic features in rectal cancer.

Authors:  Robba Rai; Michael B Barton; Phillip Chlap; Gary Liney; Carsten Brink; Shalini Vinod; Monique Heinke; Yuvnik Trada; Lois C Holloway
Journal:  J Med Imaging (Bellingham)       Date:  2022-08-18

2.  Local tuning of radiomics-based model for predicting pathological response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

Authors:  Bin Tang; Jacopo Lenkowicz; Qian Peng; Luca Boldrini; Qing Hou; Nicola Dinapoli; Vincenzo Valentini; Peng Diao; Gang Yin; Lucia Clara Orlandini
Journal:  BMC Med Imaging       Date:  2022-03-14       Impact factor: 1.930

Review 3.  Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review.

Authors:  Michael Roberts; Leonardo Rundo; Nikita Sushentsev; Nadia Moreira Da Silva; Michael Yeung; Tristan Barrett; Evis Sala
Journal:  Insights Imaging       Date:  2022-03-28

4.  Radiomics and deep learning for myocardial scar screening in hypertrophic cardiomyopathy.

Authors:  Ahmed S Fahmy; Ethan J Rowin; Arghavan Arafati; Talal Al-Otaibi; Martin S Maron; Reza Nezafat
Journal:  J Cardiovasc Magn Reson       Date:  2022-06-27       Impact factor: 6.903

Review 5.  The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization.

Authors:  Hishan Tharmaseelan; Alexander Hertel; Shereen Rennebaum; Dominik Nörenberg; Verena Haselmann; Stefan O Schoenberg; Matthias F Froelich
Journal:  Cancers (Basel)       Date:  2022-07-09       Impact factor: 6.575

6.  The CT delta-radiomics based machine learning approach in evaluating multiple primary lung adenocarcinoma.

Authors:  Yanqing Ma; Jie Li; Xiren Xu; Yang Zhang; Yi Lin
Journal:  BMC Cancer       Date:  2022-09-03       Impact factor: 4.638

7.  Minimising multi-centre radiomics variability through image normalisation: a pilot study.

Authors:  Víctor M Campello; Carlos Martín-Isla; Cristian Izquierdo; Andrea Guala; José F Rodríguez Palomares; David Viladés; Martín L Descalzo; Mahir Karakas; Ersin Çavuş; Zahra Raisi-Estabragh; Steffen E Petersen; Sergio Escalera; Santi Seguí; Karim Lekadir
Journal:  Sci Rep       Date:  2022-07-22       Impact factor: 4.996

Review 8.  Challenges in Glioblastoma Radiomics and the Path to Clinical Implementation.

Authors:  Philip Martin; Lois Holloway; Peter Metcalfe; Eng-Siew Koh; Caterina Brighi
Journal:  Cancers (Basel)       Date:  2022-08-12       Impact factor: 6.575

9.  Building reliable radiomic models using image perturbation.

Authors:  Xinzhi Teng; Jiang Zhang; Alex Zwanenburg; Jiachen Sun; Yuhua Huang; Saikit Lam; Yuanpeng Zhang; Bing Li; Ta Zhou; Haonan Xiao; Chenyang Liu; Wen Li; Xinyang Han; Zongrui Ma; Tian Li; Jing Cai
Journal:  Sci Rep       Date:  2022-06-16       Impact factor: 4.996

10.  Test-Retest Data for the Assessment of Breast MRI Radiomic Feature Repeatability.

Authors:  R W Y Granzier; A Ibrahim; S Primakov; S A Keek; I Halilaj; A Zwanenburg; S M E Engelen; M B I Lobbes; P Lambin; H C Woodruff; M L Smidt
Journal:  J Magn Reson Imaging       Date:  2021-12-22       Impact factor: 5.119

  10 in total

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