Literature DB >> 30657374

Technical Challenges in the Clinical Application of Radiomics.

Faiq A Shaikh1, Brian J Kolowitz1, Omer Awan1, Hugo J Aerts1, Anna von Reden1, Safwan Halabi1, Sohaib A Mohiuddin1, Sana Malik1, Rasu B Shrestha1, Christopher Deible1.   

Abstract

Radiomics is a quantitative approach to medical image analysis targeted at deciphering the morphologic and functional features of a lesion. Radiomic methods can be applied across various malignant conditions to identify tumor phenotype characteristics in the images that correlate with their likelihood of survival, as well as their association with the underlying biology. Identifying this set of characteristic features, called tumor signature, holds tremendous value in predicting the behavior and progression of cancer, which in turn has the potential to predict its response to various therapeutic options. We discuss the technical challenges encountered in the application of radiomics, in terms of methodology, workflow integration, and user experience, that need to be addressed to harness its true potential.

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Year:  2017        PMID: 30657374     DOI: 10.1200/CCI.17.00004

Source DB:  PubMed          Journal:  JCO Clin Cancer Inform        ISSN: 2473-4276


  5 in total

1.  Radiomics analysis using stability selection supervised component analysis for right-censored survival data.

Authors:  Kang K Yan; Xiaofei Wang; Wendy W T Lam; Varut Vardhanabhuti; Anne W M Lee; Herbert H Pang
Journal:  Comput Biol Med       Date:  2020-08-06       Impact factor: 4.589

Review 2.  Radiomics in immuno-oncology.

Authors:  Z Bodalal; I Wamelink; S Trebeschi; R G H Beets-Tan
Journal:  Immunooncol Technol       Date:  2021-04-16

Review 3.  The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges.

Authors:  Zhenyu Liu; Shuo Wang; Di Dong; Jingwei Wei; Cheng Fang; Xuezhi Zhou; Kai Sun; Longfei Li; Bo Li; Meiyun Wang; Jie Tian
Journal:  Theranostics       Date:  2019-02-12       Impact factor: 11.556

Review 4.  Decision Support Systems in Oncology.

Authors:  Seán Walsh; Evelyn E C de Jong; Janna E van Timmeren; Abdalla Ibrahim; Inge Compter; Jurgen Peerlings; Sebastian Sanduleanu; Turkey Refaee; Simon Keek; Ruben T H M Larue; Yvonka van Wijk; Aniek J G Even; Arthur Jochems; Mohamed S Barakat; Ralph T H Leijenaar; Philippe Lambin
Journal:  JCO Clin Cancer Inform       Date:  2019-02

5.  Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung Adenocarcinoma.

Authors:  Baihua Zhang; Shouliang Qi; Xiaohuan Pan; Chen Li; Yudong Yao; Wei Qian; Yubao Guan
Journal:  Front Oncol       Date:  2021-02-12       Impact factor: 6.244

  5 in total

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