Literature DB >> 31818385

Breast Cancer Radiogenomics: Current Status and Future Directions.

Lars J Grimm1, Maciej A Mazurowski2.   

Abstract

Radiogenomics is an area of research that aims to identify associations between imaging phenotypes ("radio-") and tumor genome ("-genomics"). Breast cancer radiogenomics research in particular has been an especially prolific area of investigation in recent years as evidenced by the wide number and variety of publications and conferences presentations. To date, research has primarily been focused on dynamic contrast enhanced pre-operative breast MRI and breast cancer molecular subtypes, but investigations have extended to all breast imaging modalities as well as multiple additional genetic markers including those that are commercially available. Furthermore, both human and computer-extracted features as well as deep learning techniques have been explored. This review will summarize the specific imaging modalities used in radiogenomics analysis, describe the methods of extracting imaging features, and present the types of genomics, molecular, and related information used for analysis. Finally, the limitations and future directions of breast cancer radiogenomics research will be discussed.
Copyright © 2019 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  MRI; breast cancer; deep learning; radiogenomics

Year:  2020        PMID: 31818385     DOI: 10.1016/j.acra.2019.09.012

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  7 in total

1.  Mutation Profiles of Urothelial Cancer: Will Genomics Change Radiology or Vice Versa?

Authors:  Peter L Choyke
Journal:  Radiology       Date:  2020-03-31       Impact factor: 11.105

2.  Prediction of pathological complete response after neoadjuvant chemotherapy in breast cancer: comparison of diagnostic performances of dedicated breast PET, whole-body PET, and dynamic contrast-enhanced MRI.

Authors:  Yukiko Tokuda; Masahiro Yanagawa; Yuka Fujita; Keiichiro Honma; Tomonori Tanei; Masafumi Shimoda; Tomohiro Miyake; Yasuto Naoi; Seung Jin Kim; Kenzo Shimazu; Seiki Hamada; Noriyuki Tomiyama
Journal:  Breast Cancer Res Treat       Date:  2021-03-17       Impact factor: 4.872

3.  Prediction of the clinicopathological subtypes of breast cancer using a fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI.

Authors:  Ming Ni; Xiaoming Zhou; Jingwei Liu; Haiyang Yu; Yuanxiang Gao; Xuexi Zhang; Zhiming Li
Journal:  BMC Cancer       Date:  2020-11-09       Impact factor: 4.430

4.  Tomographic Ultrasound Imaging in the Diagnosis of Breast Tumors under the Guidance of Deep Learning Algorithms.

Authors:  Xuehua Xiao; Fengping Gan; Haixia Yu
Journal:  Comput Intell Neurosci       Date:  2022-02-28

5.  Radiogenomics analysis reveals the associations of dynamic contrast-enhanced-MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer.

Authors:  Wenlong Ming; Yanhui Zhu; Yunfei Bai; Wanjun Gu; Fuyu Li; Zixi Hu; Tiansong Xia; Zuolei Dai; Xiafei Yu; Huamei Li; Yu Gu; Shaoxun Yuan; Rongxin Zhang; Haitao Li; Wenyong Zhu; Jianing Ding; Xiao Sun; Yun Liu; Hongde Liu; Xiaoan Liu
Journal:  Front Oncol       Date:  2022-07-28       Impact factor: 5.738

Review 6.  Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms.

Authors:  Jesus A Basurto-Hurtado; Irving A Cruz-Albarran; Manuel Toledano-Ayala; Mario Alberto Ibarra-Manzano; Luis A Morales-Hernandez; Carlos A Perez-Ramirez
Journal:  Cancers (Basel)       Date:  2022-07-15       Impact factor: 6.575

7.  A Novel Gene Prognostic Signature Based on Differential DNA Methylation in Breast Cancer.

Authors:  Chunmei Zhu; Shuyuan Zhang; Di Liu; Qingqing Wang; Ningning Yang; Zhewen Zheng; Qiuji Wu; Yunfeng Zhou
Journal:  Front Genet       Date:  2021-12-08       Impact factor: 4.599

  7 in total

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