Imaging Genomics and Multiomics: A Guide for Beginners Starting Radiomics-Based Research

被引:3
|
作者
Singh, Shiva [1 ]
Mohajer, Bahram [2 ]
Wells, Shane A. [3 ]
Garg, Tushar [2 ]
Hanneman, Kate [4 ]
Takahashi, Takashi [5 ]
Aldandan, Omran [6 ]
McBee, Morgan P. [7 ]
Jawahar, Anugayathri [8 ]
机构
[1] NIH, Radiol & Imaging Sci, Bethesda, MD USA
[2] Johns Hopkins Med, Radiol & Radiol Sci, Baltimore, MD USA
[3] Univ Michigan, Radiol, Ann Arbor, MI USA
[4] Univ Toronto, Med Imaging, Toronto, ON, Canada
[5] Univ Minnesota, Radiol, Minneapolis, MN USA
[6] Imam Abdulrahman Bin Faisal Univ, Coll Med Dammam, Dept Radiol, Eastern, Saudi Arabia
[7] Med Univ South Carolina, Radiol & Radiol Sci, Charleston, SC USA
[8] Northwestern Univ, Radiol, Feinberg Sch Med, 800 Arkes Pavil,676 N St Clair St, Chicago, IL 60611 USA
关键词
Radiomics; Imaging; Genomics; Multiomics; Texture analysis; PERFORMANCE; BIOMARKERS; FRAMEWORK; PHENOTYPE; IMAGES;
D O I
10.1016/j.acra.2024.01.024
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Radiomics uses advanced mathematical analysis of pixel-level information from radiologic images to extract existing information in traditional imaging algorithms. It is intended to find imaging biomarkers related to the genomics of tumors or disease patterns that improve medical care by advanced detection of tumor response patterns in tumors and to assess prognosis. Radiomics expands the paradigm of medical imaging to help with diagnosis, management of diseases and prognostication, leveraging image features by extracting information that can be used as imaging biomarkers to predict prognosis and response to treatment. Radiogenomics is an emerging area in radiomics that investigates the association between imaging characteristics and gene expression profiles. There are an increasing number of research publications using different radiomics approaches without a clear consensus on which method works best. We aim to describe the workflow of radiomics along with a guide of what to expect when starting a radiomics-based research project.
引用
收藏
页码:2281 / 2291
页数:11
相关论文
共 50 条
  • [21] Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging
    Shuling Chen
    Shiting Feng
    Jingwei Wei
    Fei Liu
    Bin Li
    Xin Li
    Yang Hou
    Dongsheng Gu
    Mimi Tang
    Han Xiao
    Yingmei Jia
    Sui Peng
    Jie Tian
    Ming Kuang
    European Radiology, 2019, 29 : 4177 - 4187
  • [22] A radiomics-based comparative study on arterial spin labeling and dynamic susceptibility contrast perfusion-weighted imaging in gliomas
    Hashido, Takashi
    Saito, Shigeyoshi
    Ishida, Takayuki
    SCIENTIFIC REPORTS, 2020, 10 (01)
  • [23] Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas
    Hui Feng
    Gaofeng Shi
    Qian Xu
    Jialiang Ren
    Lijia Wang
    Xiaojia Cai
    Insights into Imaging, 14
  • [24] Letter to the Editor: radiomics-based distinction of small (≤ 2 cm) hepatocellular carcinoma and precancerous lesions based on unenhanced magnetic imaging resonance
    Hu, Zhe
    Tian, Zhikang
    Wei, Xi
    Chen, Yueqin
    CLINICAL RADIOLOGY, 2024, 79 (07)
  • [25] Pretreatment prediction of Immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging
    Shuling Chen
    Zhenwei Peng
    Han Xiao
    Mimi Tang
    Sui Peng
    Ming Kuang
    Cancer Biology & Medicine, 2018, 15(S1) (S1) : 9 - 9
  • [26] The progress of multimodal imaging combination and subregion based radiomics research of cancers
    Zhang, Luyuan
    Wang, Yumin
    Peng, Zhouying
    Weng, Yuxiang
    Fang, Zebin
    Xiao, Feng
    Zhang, Chao
    Fan, Zuoxu
    Huang, Kaiyuan
    Zhu, Yu
    Jiang, Weihong
    Shen, Jian
    Zhan, Renya
    INTERNATIONAL JOURNAL OF BIOLOGICAL SCIENCES, 2022, 18 (08): : 3458 - 3469
  • [27] Posterior circulation ischemic stroke: radiomics-based machine learning approach to identify onset time from magnetic resonance imaging
    Liu, Zhenhao
    Zhang, Shiyu
    Wang, Yuxin
    Xu, Hui
    Gao, Yongqiang
    Jin, Hong
    Zhang, Yufeng
    Wu, Hongyang
    Lu, Jun
    Chen, Peipei
    Qiao, Peng-Gang
    Yang, Zhenghan
    NEURORADIOLOGY, 2024, 66 (07) : 1141 - 1152
  • [28] Prediction of CD3 T-cell infiltration status in colorectal liver metastases: a radiomics-based imaging biomarker
    Saber, Ralph
    Henault, David
    Vorontsov, Eugene
    Montagnon, Emmanuel
    Tang, An
    Turcotte, Simon
    Kadoury, Samuel
    MEDICAL IMAGING 2022: COMPUTER-AIDED DIAGNOSIS, 2022, 12033
  • [29] The Diagnostic Value of Radiomics-Based Machine Learning in Predicting the Grade of Meningiomas Using Conventional Magnetic Resonance Imaging: A Preliminary Study
    Chen, Chaoyue
    Guo, Xinyi
    Wang, Jian
    Guo, Wen
    Ma, Xuelei
    Xu, Jianguo
    FRONTIERS IN ONCOLOGY, 2019, 9
  • [30] Radiomics-Based Machine Learning Classification for Glioma Grading Using Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging
    Hashido, Takashi
    Saito, Shigeyoshi
    Ishida, Takayuki
    JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY, 2021, 45 (04) : 606 - 613