Literature DB >> 29252899

Lung Cancer Radiogenomics: The Increasing Value of Imaging in Personalized Management of Lung Cancer Patients.

Varut Vardhanabhuti1, Michael D Kuo2,3.   

Abstract

Radiogenomics provide a large-scale data analytical framework that aims to understand the broad multiscale relationships between the complex information encoded in medical images (including computational, quantitative, and semantic image features) and their underlying clinical, therapeutic, and biological associations. As such it is a powerful and increasingly important tool for both clinicians and researchers involved in the imaging, evaluation, understanding, and management of lung cancers. Herein we provide an overview of the growing field of lung cancer radiogenomics and its applications.

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Year:  2018        PMID: 29252899     DOI: 10.1097/RTI.0000000000000312

Source DB:  PubMed          Journal:  J Thorac Imaging        ISSN: 0883-5993            Impact factor:   3.000


  1 in total

Review 1.  Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging.

Authors:  Tara A Retson; Alexandra H Besser; Sean Sall; Daniel Golden; Albert Hsiao
Journal:  J Thorac Imaging       Date:  2019-05       Impact factor: 3.000

  1 in total

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