Literature DB >> 26663695

Breast MRI radiogenomics: Current status and research implications.

Lars J Grimm1.   

Abstract

Breast magnetic resonance imaging (MRI) radiogenomics is an emerging area of research that has the potential to directly influence clinical practice. Clinical MRI scanners today are capable of providing excellent temporal and spatial resolution, which allows extraction of numerous imaging features via human extraction approaches or complex computer vision algorithms. Meanwhile, advances in breast cancer genetics research has resulted in the identification of promising genes associated with cancer outcomes. In addition, validated genomic signatures have been developed that allow categorization of breast cancers into distinct molecular subtypes as well as predict the risk of cancer recurrence and response to therapy. Current radiogenomics research has been directed towards exploratory analysis of individual genes, understanding tumor biology, and developing imaging surrogates to genetic analysis with the long-term goal of developing a meaningful tool for clinical care. The background of breast MRI radiogenomics research, image feature extraction techniques, approaches to radiogenomics research, and promising areas of investigation are reviewed. J. Magn. Reson. Imaging 2016;43:1269-1278.
© 2015 Wiley Periodicals, Inc.

Entities:  

Keywords:  MRI; breast cancer; computer vision algorithms; genetics; radiogenomics

Mesh:

Substances:

Year:  2015        PMID: 26663695     DOI: 10.1002/jmri.25116

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  19 in total

1.  Contrast-Enhanced Mammography and Radiomics Analysis for Noninvasive Breast Cancer Characterization: Initial Results.

Authors:  Maria Adele Marino; Katja Pinker; Doris Leithner; Janice Sung; Daly Avendano; Elizabeth A Morris; Maxine Jochelson
Journal:  Mol Imaging Biol       Date:  2020-06       Impact factor: 3.488

2.  Radiogenomics of rectal adenocarcinoma in the era of precision medicine: A pilot study of associations between qualitative and quantitative MRI imaging features and genetic mutations.

Authors:  Natally Horvat; Harini Veeraraghavan; Raphael A Pelossof; Maria Clara Fernandes; Arshi Arora; Monika Khan; Michael Marco; Chin-Tung Cheng; Mithat Gonen; Jennifer S Golia Pernicka; Marc J Gollub; Julio Garcia-Aguillar; Iva Petkovska
Journal:  Eur J Radiol       Date:  2019-02-18       Impact factor: 3.528

Review 3.  Background, current role, and potential applications of radiogenomics.

Authors:  Katja Pinker; Fuki Shitano; Evis Sala; Richard K Do; Robert J Young; Andreas G Wibmer; Hedvig Hricak; Elizabeth J Sutton; Elizabeth A Morris
Journal:  J Magn Reson Imaging       Date:  2017-11-02       Impact factor: 4.813

4.  MR Imaging-Based Radiomic Signatures of Distinct Molecular Subgroups of Medulloblastoma.

Authors:  M Iv; M Zhou; K Shpanskaya; S Perreault; Z Wang; E Tranvinh; B Lanzman; S Vajapeyam; N A Vitanza; P G Fisher; Y J Cho; S Laughlin; V Ramaswamy; M D Taylor; S H Cheshier; G A Grant; T Young Poussaint; O Gevaert; K W Yeom
Journal:  AJNR Am J Neuroradiol       Date:  2018-12-06       Impact factor: 3.825

5.  Correlation between NF1 genotype and imaging phenotype on whole-body MRI: NF1 radiogenomics.

Authors:  Yunpeng Liu; Justin T Jordan; Miriam A Bredella; Serkan Erdin; James A Walker; Mark Vangel; Gordon J Harris; Scott R Plotkin; Wenli Cai
Journal:  Neurology       Date:  2020-04-28       Impact factor: 9.910

Review 6.  Challenges and opportunities for artificial intelligence in oncological imaging.

Authors:  H M C Cheung; D Rubin
Journal:  Clin Radiol       Date:  2021-04-24       Impact factor: 3.389

7.  Interim heterogeneity changes measured using entropy texture features on T2-weighted MRI at 3.0 T are associated with pathological response to neoadjuvant chemotherapy in primary breast cancer.

Authors:  Shelley Henderson; Colin Purdie; Caroline Michie; Andrew Evans; Richard Lerski; Marilyn Johnston; Sarah Vinnicombe; Alastair M Thompson
Journal:  Eur Radiol       Date:  2017-05-18       Impact factor: 5.315

8.  Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer.

Authors:  Ming Fan; Hui Li; Shijian Wang; Bin Zheng; Juan Zhang; Lihua Li
Journal:  PLoS One       Date:  2017-02-06       Impact factor: 3.240

9.  Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes.

Authors:  Elizabeth J Sutton; Erich P Huang; Karen Drukker; Elizabeth S Burnside; Hui Li; Jose M Net; Arvind Rao; Gary J Whitman; Margarita Zuley; Marie Ganott; Ermelinda Bonaccio; Maryellen L Giger; Elizabeth A Morris
Journal:  Eur Radiol Exp       Date:  2017-11-21

10.  Evaluation of human epidermal growth factor receptor 2 status of breast cancer using preoperative multidetector computed tomography with deep learning and handcrafted radiomics features.

Authors:  Xiaojun Yang; Lei Wu; Ke Zhao; Weitao Ye; Weixiao Liu; Yingyi Wang; Jiao Li; Hanxiao Li; Xiaomei Huang; Wen Zhang; Yanqi Huang; Xin Chen; Su Yao; Zaiyi Liu; Changhong Liang
Journal:  Chin J Cancer Res       Date:  2020-04       Impact factor: 5.087

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