Literature DB >> 28168275

Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology.

E J Limkin1,2, R Sun1,2,3, L Dercle4, E I Zacharaki5, C Robert1,2,3, S Reuzé1,2,3, A Schernberg1,2,3, N Paragios5,6, E Deutsch1,2, C Ferté1,7.   

Abstract

Medical image processing and analysis (also known as Radiomics) is a rapidly growing discipline that maps digital medical images into quantitative data, with the end goal of generating imaging biomarkers as decision support tools for clinical practice. The use of imaging data from routine clinical work-up has tremendous potential in improving cancer care by heightening understanding of tumor biology and aiding in the implementation of precision medicine. As a noninvasive method of assessing the tumor and its microenvironment in their entirety, radiomics allows the evaluation and monitoring of tumor characteristics such as temporal and spatial heterogeneity. One can observe a rapid increase in the number of computational medical imaging publications-milestones that have highlighted the utility of imaging biomarkers in oncology. Nevertheless, the use of radiomics as clinical biomarkers still necessitates amelioration and standardization in order to achieve routine clinical adoption. This Review addresses the critical issues to ensure the proper development of radiomics as a biomarker and facilitate its implementation in clinical practice.
© The Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  computational medical imaging; precision medicine; quantitative imaging; radiomics; tumor biology

Mesh:

Year:  2017        PMID: 28168275     DOI: 10.1093/annonc/mdx034

Source DB:  PubMed          Journal:  Ann Oncol        ISSN: 0923-7534            Impact factor:   32.976


  190 in total

1.  [Differential diagnosis of hepatocellular carcinoma and hepatic hemangiomas based on radiomic features of gadoxetate disodium-enhanced magnetic resonance imaging].

Authors:  Mao-Dong Chen; Jing Zhang; Gui-Xiang Yang; Jie-Min Lin; Yan-Qiu Feng
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2018-04-20

2.  Radiomics analysis of contrast-enhanced CT for classification of hepatic focal lesions in colorectal cancer patients: its limitations compared to radiologists.

Authors:  Heejin Bae; Hansang Lee; Sungwon Kim; Kyunghwa Han; Hyungjin Rhee; Dong-Kyu Kim; Hyuk Kwon; Helen Hong; Joon Seok Lim
Journal:  Eur Radiol       Date:  2021-05-10       Impact factor: 5.315

3.  An MRI-based radiomics signature as a pretreatment noninvasive predictor of overall survival and chemotherapeutic benefits in lower-grade gliomas.

Authors:  Jingtao Wang; Xuejun Zheng; Jinling Zhang; Hao Xue; Lijie Wang; Rui Jing; Shuo Chen; Fengyuan Che; Xueyuan Heng; Gang Li; Fuzhong Xue
Journal:  Eur Radiol       Date:  2021-01-06       Impact factor: 5.315

Review 4.  NCTN Assessment on Current Applications of Radiomics in Oncology.

Authors:  Ke Nie; Hania Al-Hallaq; X Allen Li; Stanley H Benedict; Jason W Sohn; Jean M Moran; Yong Fan; Mi Huang; Michael V Knopp; Jeff M Michalski; James Monroe; Ceferino Obcemea; Christina I Tsien; Timothy Solberg; Jackie Wu; Ping Xia; Ying Xiao; Issam El Naqa
Journal:  Int J Radiat Oncol Biol Phys       Date:  2019-01-31       Impact factor: 7.038

5.  Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis.

Authors:  Yong Chen; Tian-Wu Chen; Chang-Qiang Wu; Qiao Lin; Ran Hu; Chao-Lian Xie; Hou-Dong Zuo; Jia-Long Wu; Qi-Wen Mu; Quan-Shui Fu; Guo-Qing Yang; Xiao Ming Zhang
Journal:  Eur Radiol       Date:  2018-11-09       Impact factor: 5.315

6.  Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma.

Authors:  Hang-Tong Hu; Zhu Wang; Xiao-Wen Huang; Shu-Ling Chen; Xin Zheng; Si-Min Ruan; Xiao-Yan Xie; Ming-de Lu; Jie Yu; Jie Tian; Ping Liang; Wei Wang; Ming Kuang
Journal:  Eur Radiol       Date:  2018-11-12       Impact factor: 5.315

7.  Feasibility of MRI Radiomics for Predicting KRAS Mutation in Rectal Cancer.

Authors:  Xiao-Fang Quo; Wen-Qian Yang; Qian Yang; Zi-Long Yuan; Yu-Lin Liu; Xiao-Hui Niu; Hai-Bo Xu
Journal:  Curr Med Sci       Date:  2021-01-11

8.  Breast Cancer Molecular Subtype Prediction by Mammographic Radiomic Features.

Authors:  Wenjuan Ma; Yumei Zhao; Yu Ji; Xinpeng Guo; Xiqi Jian; Peifang Liu; Shandong Wu
Journal:  Acad Radiol       Date:  2018-03-08       Impact factor: 3.173

9.  Impact of Variability in Portal Venous Phase Acquisition Timing in Tumor Density Measurement and Treatment Response Assessment: Metastatic Colorectal Cancer as a Paradigm.

Authors:  Laurent Dercle; Lin Lu; Philip Lichtenstein; Hao Yang; Deling Wang; Jianguo Zhu; Feiyun Wu; Hubert Piessevaux; Lawrence H Schwartz; Binsheng Zhao
Journal:  JCO Clin Cancer Inform       Date:  2017-11

10.  Radiomics Response Signature for Identification of Metastatic Colorectal Cancer Sensitive to Therapies Targeting EGFR Pathway.

Authors:  Laurent Dercle; Lin Lu; Lawrence H Schwartz; Min Qian; Sabine Tejpar; Peter Eggleton; Binsheng Zhao; Hubert Piessevaux
Journal:  J Natl Cancer Inst       Date:  2020-09-01       Impact factor: 13.506

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