Literature DB >> 27269645

Applications and limitations of radiomics.

Stephen S F Yip1, Hugo J W L Aerts.   

Abstract

Radiomics is an emerging field in quantitative imaging that uses advanced imaging features to objectively and quantitatively describe tumour phenotypes. Radiomic features have recently drawn considerable interest due to its potential predictive power for treatment outcomes and cancer genetics, which may have important applications in personalized medicine. In this technical review, we describe applications and challenges of the radiomic field. We will review radiomic application areas and technical issues, as well as proper practices for the designs of radiomic studies.

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Year:  2016        PMID： 27269645      PMCID： PMC4927328          DOI： 10.1088/0031-9155/61/13/R150

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN： 0031-9155            Impact factor:   3.609


  111 in total

1.  A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: preliminary results.

Authors:  M F McNitt-Gray; E M Hart; N Wyckoff; J W Sayre; J G Goldin; D R Aberle
Journal:  Med Phys       Date:  1999-06       Impact factor: 4.071

2.  Impact of Image Reconstruction Settings on Texture Features in 18F-FDG PET.

Authors:  Jianhua Yan; Jason Lim Chu-Shern; Hoi Yin Loi; Lih Kin Khor; Arvind K Sinha; Swee Tian Quek; Ivan W K Tham; David Townsend
Journal:  J Nucl Med       Date:  2015-07-30       Impact factor: 10.057

Review 3.  Intra-tumour heterogeneity: a looking glass for cancer?

Authors:  Andriy Marusyk; Vanessa Almendro; Kornelia Polyak
Journal:  Nat Rev Cancer       Date:  2012-04-19       Impact factor: 60.716

4.  Staging of cervical cancer based on tumor heterogeneity characterized by texture features on (18)F-FDG PET images.

Authors:  Wei Mu; Zhe Chen; Ying Liang; Wei Shen; Feng Yang; Ruwei Dai; Ning Wu; Jie Tian
Journal:  Phys Med Biol       Date:  2015-06-17       Impact factor: 3.609

5.  18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort.

Authors:  Mathieu Hatt; Mohamed Majdoub; Martin Vallières; Florent Tixier; Catherine Cheze Le Rest; David Groheux; Elif Hindié; Antoine Martineau; Olivier Pradier; Roland Hustinx; Remy Perdrisot; Remy Guillevin; Issam El Naqa; Dimitris Visvikis
Journal:  J Nucl Med       Date:  2014-12-11       Impact factor: 10.057

6.  Identification of noninvasive imaging surrogates for brain tumor gene-expression modules.

Authors:  Maximilian Diehn; Christine Nardini; David S Wang; Susan McGovern; Mahesh Jayaraman; Yu Liang; Kenneth Aldape; Soonmee Cha; Michael D Kuo
Journal:  Proc Natl Acad Sci U S A       Date:  2008-03-24       Impact factor: 11.205

7.  The promise and limits of PET texture analysis.

Authors:  Nai-Ming Cheng; Yu-Hua Dean Fang; Tzu-Chen Yen
Journal:  Ann Nucl Med       Date:  2013-08-13       Impact factor: 2.668

8.  Modeling pathologic response of esophageal cancer to chemoradiation therapy using spatial-temporal 18F-FDG PET features, clinical parameters, and demographics.

Authors:  Hao Zhang; Shan Tan; Wengen Chen; Seth Kligerman; Grace Kim; Warren D D'Souza; Mohan Suntharalingam; Wei Lu
Journal:  Int J Radiat Oncol Biol Phys       Date:  2013-11-01       Impact factor: 7.038

9.  Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI.

Authors:  Ke Nie; Jeon-Hor Chen; Hon J Yu; Yong Chu; Orhan Nalcioglu; Min-Ying Su
Journal:  Acad Radiol       Date:  2008-12       Impact factor: 3.173

10.  The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis.

Authors:  Ralph T H Leijenaar; Georgi Nalbantov; Sara Carvalho; Wouter J C van Elmpt; Esther G C Troost; Ronald Boellaard; Hugo J W L Aerts; Robert J Gillies; Philippe Lambin
Journal:  Sci Rep       Date:  2015-08-05       Impact factor: 4.379
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  270 in total

1.  Perspectives in Radiomics for Personalized Medicine and Theranostics.

Authors:  Seunggyun Ha
Journal:  Nucl Med Mol Imaging       Date:  2019-01-23

2.  18F-FDG PET/CT radiomic predictors of pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer patients.

Authors:  Panli Li; Xiuying Wang; Chongrui Xu; Cheng Liu; Chaojie Zheng; Michael J Fulham; Dagan Feng; Lisheng Wang; Shaoli Song; Gang Huang
Journal:  Eur J Nucl Med Mol Imaging       Date:  2020-01-25       Impact factor: 9.236

3.  External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy.

Authors:  François Lucia; Dimitris Visvikis; Martin Vallières; Marie-Charlotte Desseroit; Omar Miranda; Philippe Robin; Pietro Andrea Bonaffini; Joanne Alfieri; Ingrid Masson; Augustin Mervoyer; Caroline Reinhold; Olivier Pradier; Mathieu Hatt; Ulrike Schick
Journal:  Eur J Nucl Med Mol Imaging       Date:  2018-12-07       Impact factor: 9.236

Review 4.  Towards precision medicine: from quantitative imaging to radiomics.

Authors:  U Rajendra Acharya; Yuki Hagiwara; Vidya K Sudarshan; Wai Yee Chan; Kwan Hoong Ng
Journal:  J Zhejiang Univ Sci B       Date:  2018 Jan.       Impact factor: 3.066

Review 5.  "Radio-oncomics" : The potential of radiomics in radiation oncology.

Authors:  Jan Caspar Peeken; Fridtjof Nüsslin; Stephanie E Combs
Journal:  Strahlenther Onkol       Date:  2017-07-07       Impact factor: 3.621

Review 6.  Texture analysis of medical images for radiotherapy applications.

Authors:  Elisa Scalco; Giovanna Rizzo
Journal:  Br J Radiol       Date:  2016-11-25       Impact factor: 3.039

7.  Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer.

Authors:  Emmanuel Rios Velazquez; Chintan Parmar; Ying Liu; Thibaud P Coroller; Gisele Cruz; Olya Stringfield; Zhaoxiang Ye; Mike Makrigiorgos; Fiona Fennessy; Raymond H Mak; Robert Gillies; John Quackenbush; Hugo J W L Aerts
Journal:  Cancer Res       Date:  2017-05-31       Impact factor: 12.701

8.  Comparison of Breast MRI Tumor Classification Using Human-Engineered Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Methods.

Authors:  Heather M Whitney; Hui Li; Yu Ji; Peifang Liu; Maryellen L Giger
Journal:  Proc IEEE Inst Electr Electron Eng       Date:  2019-11-21       Impact factor: 10.961

9.  Radiation Therapy Outcomes Models in the Era of Radiomics and Radiogenomics: Uncertainties and Validation.

Authors:  Issam El Naqa; Gaurav Pandey; Hugo Aerts; Jen-Tzung Chien; Christian Nicolaj Andreassen; Andrzej Niemierko; Randall K Ten Haken
Journal:  Int J Radiat Oncol Biol Phys       Date:  2018-10-18       Impact factor: 7.038

10.  Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features.

Authors:  Ping Yin; Ning Mao; Chao Zhao; Jiangfen Wu; Chao Sun; Lei Chen; Nan Hong
Journal:  Eur Radiol       Date:  2018-10-02       Impact factor: 5.315
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