Stephanie Nougaret1,2, Hichem Tibermacine3,4, Marion Tardieu3,4, Evis Sala5. 1. Montpellier Cancer Research Institute (IRCM), 208 Ave des Apothicaires, 34295, Montpellier, France. Stephanie.Nougaret@icm.unicancer.fr. 2. Department of Radiology, Montpellier Cancer institute, INSERM, U1194, University of Montpellier, 208 Ave des Apothicaires, 34295, Montpellier, France. Stephanie.Nougaret@icm.unicancer.fr. 3. Montpellier Cancer Research Institute (IRCM), 208 Ave des Apothicaires, 34295, Montpellier, France. 4. Department of Radiology, Montpellier Cancer institute, INSERM, U1194, University of Montpellier, 208 Ave des Apothicaires, 34295, Montpellier, France. 5. Department of Radiology, Box 218 and Cancer Research UK Cambridge Centre, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Abstract
PURPOSE OF REVIEW: To briefly review the radiomics concept, its applications, and challenges in oncology in the era of precision medicine. RECENT FINDINGS: Over the last 5 years, more than 500 studies have evaluated the role of radiomics to predict tumor diagnosis, genetic pattern, tumor response to therapy, and survival in multiple cancers. This new post-processing method is aimed at extracting multiple quantitative features from the image and converting them into mineable data. Radiomics models developed have shown promising results and may play a role in the near future in the daily patient management especially to assess tumor heterogeneity acting as a whole tumor virtual biopsy. For now, radiomics is limited by its lack of standardization; future challenges will be to provide robust and reproducible metrics extracted from large multicenter databases.
PURPOSE OF REVIEW: To briefly review the radiomics concept, its applications, and challenges in oncology in the era of precision medicine. RECENT FINDINGS: Over the last 5 years, more than 500 studies have evaluated the role of radiomics to predict tumor diagnosis, genetic pattern, tumor response to therapy, and survival in multiple cancers. This new post-processing method is aimed at extracting multiple quantitative features from the image and converting them into mineable data. Radiomics models developed have shown promising results and may play a role in the near future in the daily patient management especially to assess tumor heterogeneity acting as a whole tumor virtual biopsy. For now, radiomics is limited by its lack of standardization; future challenges will be to provide robust and reproducible metrics extracted from large multicenter databases.
Authors: J S Ross; S M Ali; K Wang; G Palmer; R Yelensky; D Lipson; V A Miller; D Zajchowski; L K Shawver; P J Stephens Journal: Gynecol Oncol Date: 2013-06-20 Impact factor: 5.482
Authors: Kylie L Gorringe; Joshy George; Michael S Anglesio; Manasa Ramakrishna; Dariush Etemadmoghadam; Prue Cowin; Anita Sridhar; Louise H Williams; Samantha E Boyle; Nozomu Yanaihara; Aikou Okamoto; Mitsuyoshi Urashima; Gordon K Smyth; Ian G Campbell; David D L Bowtell Journal: PLoS One Date: 2010-09-10 Impact factor: 3.240
Authors: Kenneth A Miles; Balaji Ganeshan; Matthew R Griffiths; Rupert C D Young; Christopher R Chatwin Journal: Radiology Date: 2009-01-22 Impact factor: 11.105
Authors: Fergus Davnall; Connie S P Yip; Gunnar Ljungqvist; Mariyah Selmi; Francesca Ng; Bal Sanghera; Balaji Ganeshan; Kenneth A Miles; Gary J Cook; Vicky Goh Journal: Insights Imaging Date: 2012-10-24
Authors: Ali Bashashati; Gavin Ha; Alicia Tone; Jiarui Ding; Leah M Prentice; Andrew Roth; Jamie Rosner; Karey Shumansky; Steve Kalloger; Janine Senz; Winnie Yang; Melissa McConechy; Nataliya Melnyk; Michael Anglesio; Margaret T Y Luk; Kane Tse; Thomas Zeng; Richard Moore; Yongjun Zhao; Marco A Marra; Blake Gilks; Stephen Yip; David G Huntsman; Jessica N McAlpine; Sohrab P Shah Journal: J Pathol Date: 2013-09 Impact factor: 7.996