Literature DB >> 28975929

Radiomics: the bridge between medical imaging and personalized medicine.

Philippe Lambin1, Ralph T H Leijenaar1, Timo M Deist1, Jurgen Peerlings1,2, Evelyn E C de Jong1, Janita van Timmeren1, Sebastian Sanduleanu1, Ruben T H M Larue1, Aniek J G Even1, Arthur Jochems1, Yvonka van Wijk1, Henry Woodruff1, Johan van Soest3, Tim Lustberg3, Erik Roelofs1,3, Wouter van Elmpt3, Andre Dekker3, Felix M Mottaghy2,4, Joachim E Wildberger2, Sean Walsh1.   

Abstract

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. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.

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Year:  2017        PMID: 28975929     DOI: 10.1038/nrclinonc.2017.141

Source DB:  PubMed          Journal:  Nat Rev Clin Oncol        ISSN: 1759-4774            Impact factor:   66.675


  116 in total

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3.  Statistics: P values are just the tip of the iceberg.

Authors:  Jeffrey T Leek; Roger D Peng
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4.  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

5.  Benefits of a clinical data warehouse with data mining tools to collect data for a radiotherapy trial.

Authors:  Erik Roelofs; Lucas Persoon; Sebastiaan Nijsten; Wolfgang Wiessler; André Dekker; Philippe Lambin
Journal:  Radiother Oncol       Date:  2013-02-05       Impact factor: 6.280

Review 6.  Applications and limitations of radiomics.

Authors:  Stephen S F Yip; Hugo J W L Aerts
Journal:  Phys Med Biol       Date:  2016-06-08       Impact factor: 3.609

7.  Building a rapid learning health care system for oncology: the regulatory framework of CancerLinQ.

Authors:  Richard L Schilsky; Dina L Michels; Amy H Kearbey; Peter Paul Yu; Clifford A Hudis
Journal:  J Clin Oncol       Date:  2014-06-09       Impact factor: 44.544

Review 8.  Creating a data exchange strategy for radiotherapy research: towards federated databases and anonymised public datasets.

Authors:  Tomas Skripcak; Claus Belka; Walter Bosch; Carsten Brink; Thomas Brunner; Volker Budach; Daniel Büttner; Jürgen Debus; Andre Dekker; Cai Grau; Sarah Gulliford; Coen Hurkmans; Uwe Just; Mechthild Krause; Philippe Lambin; Johannes A Langendijk; Rolf Lewensohn; Armin Lühr; Philippe Maingon; Michele Masucci; Maximilian Niyazi; Philip Poortmans; Monique Simon; Heinz Schmidberger; Emiliano Spezi; Martin Stuschke; Vincenzo Valentini; Marcel Verheij; Gillian Whitfield; Björn Zackrisson; Daniel Zips; Michael Baumann
Journal:  Radiother Oncol       Date:  2014-10-28       Impact factor: 6.280

9.  Pharmaceutical treatments to prevent recurrence of endometriosis following surgery: a model-based economic evaluation.

Authors:  Sabina Sanghera; Pelham Barton; Siladitya Bhattacharya; Andrew W Horne; Tracy Elizabeth Roberts
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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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  971 in total

1.  Extracting and Selecting Robust Radiomic Features from PET/MR Images in Nasopharyngeal Carcinoma.

Authors:  Pengfei Yang; Lei Xu; Zuozhen Cao; Yidong Wan; Yi Xue; Yangkang Jiang; Eric Yen; Chen Luo; Jing Wang; Yi Rong; Tianye Niu
Journal:  Mol Imaging Biol       Date:  2020-12       Impact factor: 3.488

2.  Magnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study.

Authors:  He Zhang; Yunfei Mao; Xiaojun Chen; Guoqing Wu; Xuefen Liu; Peng Zhang; Yu Bai; Pengcong Lu; Weigen Yao; Yuanyuan Wang; Jinhua Yu; Guofu Zhang
Journal:  Eur Radiol       Date:  2019-04-08       Impact factor: 5.315

3.  Computed Tomography-Based Radiomics Signature: A Potential Indicator of Epidermal Growth Factor Receptor Mutation in Pulmonary Adenocarcinoma Appearing as a Subsolid Nodule.

Authors:  Xinguan Yang; Xiao Dong; Jiao Wang; Weiwei Li; Zhuoran Gu; Dashan Gao; Nanshan Zhong; Yubao Guan
Journal:  Oncologist       Date:  2019-04-01

4.  CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study.

Authors:  Dongsheng Gu; Yabin Hu; Hui Ding; Jingwei Wei; Ke Chen; Hao Liu; Mengsu Zeng; Jie Tian
Journal:  Eur Radiol       Date:  2019-06-21       Impact factor: 5.315

Review 5.  The role of radiology in the evaluation of the immunotherapy efficacy.

Authors:  Marco Calandri; Federica Solitro; Valeria Angelino; Federica Moretti; Andrea Veltri
Journal:  J Thorac Dis       Date:  2018-05       Impact factor: 2.895

6.  The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer.

Authors:  Jinrong Qu; Chen Shen; Jianjun Qin; Zhaoqi Wang; Zhenyu Liu; Jia Guo; Hongkai Zhang; Pengrui Gao; Tianxia Bei; Yingshu Wang; Hui Liu; Ihab R Kamel; Jie Tian; Hailiang Li
Journal:  Eur Radiol       Date:  2018-07-23       Impact factor: 5.315

7.  Integrative nomogram of CT imaging, clinical, and hematological features for survival prediction of patients with locally advanced non-small cell lung cancer.

Authors:  Linlin Wang; Taotao Dong; Bowen Xin; Chongrui Xu; Meiying Guo; Huaqi Zhang; Dagan Feng; Xiuying Wang; Jinming Yu
Journal:  Eur Radiol       Date:  2019-01-14       Impact factor: 5.315

8.  Radiomic profiles in diffuse glioma reveal distinct subtypes with prognostic value.

Authors:  Peng Lin; Yu-Ting Peng; Rui-Zhi Gao; Yan Wei; Xiao-Jiao Li; Su-Ning Huang; Ye-Ying Fang; Zhu-Xin Wei; Zhi-Guang Huang; Hong Yang; Gang Chen
Journal:  J Cancer Res Clin Oncol       Date:  2020-02-17       Impact factor: 4.553

9.  Diagnostic accuracy of MRI texture analysis for grading gliomas.

Authors:  Austin Ditmer; Bin Zhang; Taimur Shujaat; Andrew Pavlina; Nicholas Luibrand; Mary Gaskill-Shipley; Achala Vagal
Journal:  J Neurooncol       Date:  2018-08-25       Impact factor: 4.130

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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