Literature DB >> 29366598

Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application-Part 2: From Clinical Implementation to Enterprise.

Faiq Shaikh1, Benjamin Franc2, Erastus Allen3, Evis Sala4, Omer Awan5, Kenneth Hendrata6, Safwan Halabi7, Sohaib Mohiuddin8, Sana Malik9, Dexter Hadley10, Rasu Shrestha3.   

Abstract

Enterprise imaging has channeled various technological innovations to the field of clinical radiology, ranging from advanced imaging equipment and postacquisition iterative reconstruction tools to image analysis and computer-aided detection tools. More recently, the advancement in the field of quantitative image analysis coupled with machine learning-based data analytics, classification, and integration has ushered in the era of radiomics, a paradigm shift that holds tremendous potential in clinical decision support as well as drug discovery. However, there are important issues to consider to incorporate radiomics into a clinically applicable system and a commercially viable solution. In this two-part series, we offer insights into the development of the translational pipeline for radiomics from methodology to clinical implementation (Part 1) and from that point to enterprise development (Part 2). In Part 2 of this two-part series, we study the components of the strategy pipeline, from clinical implementation to building enterprise solutions.
Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Radiomics; enterprise; medicine; precision; radiology; translational

Mesh:

Year:  2018        PMID: 29366598      PMCID: PMC7440361          DOI: 10.1016/j.jacr.2017.12.006

Source DB:  PubMed          Journal:  J Am Coll Radiol        ISSN: 1546-1440            Impact factor:   5.532


  67 in total

1.  Quantitative trait Loci analysis using the false discovery rate.

Authors:  Yoav Benjamini; Daniel Yekutieli
Journal:  Genetics       Date:  2005-06-14       Impact factor: 4.562

2.  Automatic IVUS segmentation of atherosclerotic plaque with stop & go snake.

Authors:  Ellen Brunenberg; Oriol Pujol; Bart ter Haar Romeny; Petia Radeva
Journal:  Med Image Comput Comput Assist Interv       Date:  2006

3.  Texture analysis of multiple sclerosis: a comparative study.

Authors:  Jing Zhang; Longzheng Tong; Lei Wang; Ning Li
Journal:  Magn Reson Imaging       Date:  2008-05-29       Impact factor: 2.546

4.  The Yale cTAKES extensions for document classification: architecture and application.

Authors:  Vijay Garla; Vincent Lo Re; Zachariah Dorey-Stein; Farah Kidwai; Matthew Scotch; Julie Womack; Amy Justice; Cynthia Brandt
Journal:  J Am Med Inform Assoc       Date:  2011-05-27       Impact factor: 4.497

5.  Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival.

Authors:  Balaji Ganeshan; Elleny Panayiotou; Kate Burnand; Sabina Dizdarevic; Ken Miles
Journal:  Eur Radiol       Date:  2011-11-17       Impact factor: 5.315

6.  Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer.

Authors:  Manushka Vaidya; Kimberly M Creach; Jennifer Frye; Farrokh Dehdashti; Jeffrey D Bradley; Issam El Naqa
Journal:  Radiother Oncol       Date:  2011-11-16       Impact factor: 6.280

Review 7.  Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis.

Authors:  Sugama Chicklore; Vicky Goh; Musib Siddique; Arunabha Roy; Paul K Marsden; Gary J R Cook
Journal:  Eur J Nucl Med Mol Imaging       Date:  2012-10-13       Impact factor: 9.236

8.  Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy?

Authors:  Gary J R Cook; Connie Yip; Muhammad Siddique; Vicky Goh; Sugama Chicklore; Arunabha Roy; Paul Marsden; Shahreen Ahmad; David Landau
Journal:  J Nucl Med       Date:  2012-11-30       Impact factor: 10.057

9.  Texture analysis of aggressive and nonaggressive lung tumor CE CT images.

Authors:  Omar S Al-Kadi; D Watson
Journal:  IEEE Trans Biomed Eng       Date:  2008-07       Impact factor: 4.538

10.  Identification of Histological Correlates of Overall Survival in Lower Grade Gliomas Using a Bag-of-words Paradigm: A Preliminary Analysis Based on Hematoxylin & Eosin Stained Slides from the Lower Grade Glioma Cohort of The Cancer Genome Atlas.

Authors:  Reid Trenton Powell; Adriana Olar; Shivali Narang; Ganesh Rao; Erik Sulman; Gregory N Fuller; Arvind Rao
Journal:  J Pathol Inform       Date:  2017-03-10
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  2 in total

1.  Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers.

Authors:  Laure Fournier; Lena Costaridou; Luc Bidaut; Nicolas Michoux; Frederic E Lecouvet; Lioe-Fee de Geus-Oei; Ronald Boellaard; Daniela E Oprea-Lager; Nancy A Obuchowski; Anna Caroli; Wolfgang G Kunz; Edwin H Oei; James P B O'Connor; Marius E Mayerhoefer; Manuela Franca; Angel Alberich-Bayarri; Christophe M Deroose; Christian Loewe; Rashindra Manniesing; Caroline Caramella; Egesta Lopci; Nathalie Lassau; Anders Persson; Rik Achten; Karen Rosendahl; Olivier Clement; Elmar Kotter; Xavier Golay; Marion Smits; Marc Dewey; Daniel C Sullivan; Aad van der Lugt; Nandita M deSouza
Journal:  Eur Radiol       Date:  2021-01-25       Impact factor: 5.315

2.  A nomogram combined with radiomics features, albuminuria, and metabolic syndrome to predict the risk of myometrial invasion of bladder cancer.

Authors:  Qi Zhou; Zhiyu Zhang; Xiaojie Ang; Haoyang Zhang; Jun Ouyang
Journal:  Transl Cancer Res       Date:  2021-07       Impact factor: 1.241

  2 in total

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