Literature DB >> 29750429

Computer-aided diagnosis with radiogenomics: analysis of the relationship between genotype and morphological changes of the brain magnetic resonance images.

Chiharu Kai1, Yoshikazu Uchiyama2, Junji Shiraishi3, Hiroshi Fujita4, Kunio Doi5,6.   

Abstract

In the post-genome era, a novel research field, 'radiomics' has been developed to offer a new viewpoint for the use of genotypes in radiology and medicine research which have traditionally focused on the analysis of imaging phenotypes. The present study analyzed brain morphological changes related to the individual's genotype. Our data consisted of magnetic resonance (MR) images of patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD), as well as their apolipoprotein E (APOE) genotypes. First, statistical parametric mapping (SPM) 12 was used for three-dimensional anatomical standardization of the brain MR images. A total of 30 normal images were used to create a standard normal brain image. Z-score maps were generated to identify the differences between an abnormal image and the standard normal brain. Our experimental results revealed that cerebral atrophies, depending on genotypes, can occur in different locations and that morphological changes may differ between MCI and AD. Using a classifier to characterize cerebral atrophies related to an individual's genotype, we developed a computer-aided diagnosis (CAD) scheme to identify the disease. For the early detection of cerebral diseases, a screening system using MR images, called Brain Check-up, is widely performed in Japan. Therefore, our proposed CAD scheme would be used in Brain Check-up.

Entities:  

Keywords:  Alzheimer’s disease; Computer-aided diagnosis; Mild cognitive impairment; Radiogenomics; Radiomics

Mesh:

Substances:

Year:  2018        PMID: 29750429     DOI: 10.1007/s12194-018-0462-5

Source DB:  PubMed          Journal:  Radiol Phys Technol        ISSN: 1865-0333


  34 in total

1.  Apolipoprotein E epsilon4 allele, temporal lobe atrophy, and white matter lesions in late-life dementias.

Authors:  R Barber; A Gholkar; P Scheltens; C Ballard; I G McKeith; C M Morris; J T O'Brien
Journal:  Arch Neurol       Date:  1999-08

2.  Voxel-based morphometry to discriminate early Alzheimer's disease from controls.

Authors:  Yoko Hirata; Hiroshi Matsuda; Kiyotaka Nemoto; Takashi Ohnishi; Kentaro Hirao; Fumio Yamashita; Takashi Asada; Satoshi Iwabuchi; Hirotsugu Samejima
Journal:  Neurosci Lett       Date:  2005-04-14       Impact factor: 3.046

3.  A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities.

Authors:  M Vallières; C R Freeman; S R Skamene; I El Naqa
Journal:  Phys Med Biol       Date:  2015-06-29       Impact factor: 3.609

Review 4.  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

5.  Automated method for identification of patients with Alzheimer's disease based on three-dimensional MR images.

Authors:  Hidetaka Arimura; Takashi Yoshiura; Seiji Kumazawa; Kazuhiro Tanaka; Hiroshi Koga; Futoshi Mihara; Hiroshi Honda; Shuji Sakai; Fukai Toyofuku; Yoshiharu Higashida
Journal:  Acad Radiol       Date:  2008-03       Impact factor: 3.173

6.  Radiogenomics: what it is and why it is important.

Authors:  Maciej A Mazurowski
Journal:  J Am Coll Radiol       Date:  2015-08       Impact factor: 5.532

7.  Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data.

Authors:  Wentian Guo; Hui Li; Yitan Zhu; Li Lan; Shengjie Yang; Karen Drukker; Elizabeth Morris; Elizabeth Burnside; Gary Whitman; Maryellen L Giger; Yuan Ji
Journal:  J Med Imaging (Bellingham)       Date:  2015-09-23

8.  Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set.

Authors:  Hui Li; Yitan Zhu; Elizabeth S Burnside; Erich Huang; Karen Drukker; Katherine A Hoadley; Cheng Fan; Suzanne D Conzen; Margarita Zuley; Jose M Net; Elizabeth Sutton; Gary J Whitman; Elizabeth Morris; Charles M Perou; Yuan Ji; Maryellen L Giger
Journal:  NPJ Breast Cancer       Date:  2016-05-11

9.  Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study.

Authors:  Jeff Wang; Fumi Kato; Noriko Oyama-Manabe; Ruijiang Li; Yi Cui; Khin Khin Tha; Hiroko Yamashita; Kohsuke Kudo; Hiroki Shirato
Journal:  PLoS One       Date:  2015-11-24       Impact factor: 3.240

10.  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

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  6 in total

Review 1.  Potentials of radiomics for cancer diagnosis and treatment in comparison with computer-aided diagnosis.

Authors:  Hidetaka Arimura; Mazen Soufi; Kenta Ninomiya; Hidemi Kamezawa; Masahiro Yamada
Journal:  Radiol Phys Technol       Date:  2018-10-29

Review 2.  AI-based computer-aided diagnosis (AI-CAD): the latest review to read first.

Authors:  Hiroshi Fujita
Journal:  Radiol Phys Technol       Date:  2020-01-02

Review 3.  Harnessing non-destructive 3D pathology.

Authors:  Jonathan T C Liu; Adam K Glaser; Kaustav Bera; Lawrence D True; Nicholas P Reder; Kevin W Eliceiri; Anant Madabhushi
Journal:  Nat Biomed Eng       Date:  2021-02-15       Impact factor: 25.671

4.  Radiogenomics correlation between MR imaging features and mRNA-based subtypes in lower-grade glioma.

Authors:  Zhenyin Liu; Jing Zhang
Journal:  BMC Neurol       Date:  2020-06-29       Impact factor: 2.474

5.  Development and assessment of an individualized nomogram to predict colorectal cancer liver metastases.

Authors:  Mingyang Li; Xueyan Li; Yu Guo; Zheng Miao; Xiaoming Liu; Shuxu Guo; Huimao Zhang
Journal:  Quant Imaging Med Surg       Date:  2020-02

6.  Building the Precision Medicine for Mental Disorders via Radiomics/Machine Learning and Neuroimaging.

Authors:  Long-Biao Cui; Xian Xu; Feng Cao
Journal:  Front Neurosci       Date:  2021-06-15       Impact factor: 4.677

  6 in total

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