Literature DB >> 26344440

[Radiomics: Definition and clinical development].

C Bourgier1, J Colinge2, N Aillères3, P Fenoglietto3, M Brengues1, A Pèlegrin3, D Azria4.   

Abstract

The ultimate goal in radiation oncology is to offer a personalized treatment to all patients indicated for radiotherapy. Radiomics is a tool that reinforces a deep analysis of tumors at the molecular aspect taking into account intrinsic susceptibility in a long-term follow-up. Radiomics allow qualitative and quantitative performance analyses with high throughput extraction of numeric radiologic data to obtain predictive or prognostic information from patients treated for cancer. A second approach is to define biological or constitutional that could change the practice. This technique included normal tissue individual susceptibility but also potential response of tumors under ionizing radiation treatment. These "omics" are biological and technical techniques leading to simultaneous novel identification and exploration a set of genes, lipids, proteins.
Copyright © 2015 Société française de radiothérapie oncologique (SFRO). Published by Elsevier SAS. All rights reserved.

Entities:  

Keywords:  Genomic; Génomique; Proteomic; Protéomique; Radiomics

Mesh:

Year:  2015        PMID: 26344440     DOI: 10.1016/j.canrad.2015.06.008

Source DB:  PubMed          Journal:  Cancer Radiother        ISSN: 1278-3218            Impact factor:   1.018


  7 in total

1.  Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging.

Authors:  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
Journal:  Eur Radiol       Date:  2019-01-21       Impact factor: 5.315

2.  Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma.

Authors:  Zeju Li; Yuanyuan Wang; Jinhua Yu; Yi Guo; Wei Cao
Journal:  Sci Rep       Date:  2017-07-14       Impact factor: 4.379

3.  Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction.

Authors:  Qian Du; Michael Baine; Kyle Bavitz; Josiah McAllister; Xiaoying Liang; Hongfeng Yu; Jeffrey Ryckman; Lina Yu; Hengle Jiang; Sumin Zhou; Chi Zhang; Dandan Zheng
Journal:  PLoS One       Date:  2019-05-07       Impact factor: 3.240

4.  Quantitative PET Imaging and Clinical Parameters as Predictive Factors for Patients With Cervical Carcinoma: Implications of a Prediction Model Generated Using Multi-Objective Support Vector Machine Learning.

Authors:  Zhiguo Zhou; Genevieve M Maquilan; Kimberly Thomas; Jason Wachsmann; Jing Wang; Michael R Folkert; Kevin Albuquerque
Journal:  Technol Cancer Res Treat       Date:  2020 Jan-Dec

5.  Radiomics for the Prediction of Epilepsy in Patients With Frontal Glioma.

Authors:  Ankang Gao; Hongxi Yang; Yida Wang; Guohua Zhao; Chenglong Wang; Haijie Wang; Xiaonan Zhang; Yong Zhang; Jingliang Cheng; Guang Yang; Jie Bai
Journal:  Front Oncol       Date:  2021-11-22       Impact factor: 6.244

6.  Deep learning for prediction of isocitrate dehydrogenase mutation in gliomas: a critical approach, systematic review and meta-analysis of the diagnostic test performance using a Bayesian approach.

Authors:  Mert Karabacak; Burak Berksu Ozkara; Seren Mordag; Sotirios Bisdas
Journal:  Quant Imaging Med Surg       Date:  2022-08

7.  Stability analysis of CT radiomic features with respect to segmentation variation in oropharyngeal cancer.

Authors:  Rongjie Liu; Hesham Elhalawani; Abdallah Sherif Radwan Mohamed; Baher Elgohari; Laurence Court; Hongtu Zhu; Clifton David Fuller
Journal:  Clin Transl Radiat Oncol       Date:  2019-11-28
  7 in total

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