Literature DB >> 29907338

Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?

Reid F Thompson1, Gilmer Valdes2, Clifton D Fuller3, Colin M Carpenter4, Olivier Morin2, Sanjay Aneja5, William D Lindsay6, Hugo J W L Aerts7, Barbara Agrimson8, Curtiland Deville9, Seth A Rosenthal10, James B Yu5, Charles R Thomas8.   

Abstract

Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the "fourth" industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine. Published by Elsevier B.V.

Keywords:  Artificial intelligence; Deep learning; Machine learning

Mesh:

Year:  2018        PMID: 29907338     DOI: 10.1016/j.radonc.2018.05.030

Source DB:  PubMed          Journal:  Radiother Oncol        ISSN: 0167-8140            Impact factor:   6.280


  44 in total

1.  Future directions on the merge of quantitative imaging and artificial intelligence in radiation oncology.

Authors:  Kathrine Røe Redalen; Daniela Thorwarth
Journal:  Phys Imaging Radiat Oncol       Date:  2020-07-15

Review 2.  Why should radiation oncology do translational research? [corrected].

Authors:  Rut Cañas; Isabel Linares; Ferran Guedea; Miguel Ángel Berenguer
Journal:  Rep Pract Oncol Radiother       Date:  2018-11-12

Review 3.  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

Review 4.  Online daily adaptive proton therapy.

Authors:  Francesca Albertini; Michael Matter; Lena Nenoff; Ye Zhang; Antony Lomax
Journal:  Br J Radiol       Date:  2019-11-11       Impact factor: 3.039

Review 5.  An introduction to deep learning in medical physics: advantages, potential, and challenges.

Authors:  Chenyang Shen; Dan Nguyen; Zhiguo Zhou; Steve B Jiang; Bin Dong; Xun Jia
Journal:  Phys Med Biol       Date:  2020-03-03       Impact factor: 3.609

6.  Introduction to machine and deep learning for medical physicists.

Authors:  Sunan Cui; Huan-Hsin Tseng; Julia Pakela; Randall K Ten Haken; Issam El Naqa
Journal:  Med Phys       Date:  2020-06       Impact factor: 4.071

Review 7.  Genomics models in radiotherapy: From mechanistic to machine learning.

Authors:  John Kang; James T Coates; Robert L Strawderman; Barry S Rosenstein; Sarah L Kerns
Journal:  Med Phys       Date:  2020-06       Impact factor: 4.071

Review 8.  Artificial intelligence and machine learning for medical imaging: A technology review.

Authors:  Ana Barragán-Montero; Umair Javaid; Gilmer Valdés; Dan Nguyen; Paul Desbordes; Benoit Macq; Siri Willems; Liesbeth Vandewinckele; Mats Holmström; Fredrik Löfman; Steven Michiels; Kevin Souris; Edmond Sterpin; John A Lee
Journal:  Phys Med       Date:  2021-05-09       Impact factor: 2.685

9.  Clinical use, challenges, and barriers to implementation of deformable image registration in radiotherapy - the need for guidance and QA tools.

Authors:  Mohammad Hussein; Adeyemi Akintonde; Jamie McClelland; Richard Speight; Catharine H Clark
Journal:  Br J Radiol       Date:  2021-04-29       Impact factor: 3.039

Review 10.  Requirements and reliability of AI in the medical context.

Authors:  Yoganand Balagurunathan; Ross Mitchell; Issam El Naqa
Journal:  Phys Med       Date:  2021-03-13       Impact factor: 2.685

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