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Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics.

Authors
  • Tran, William T1
  • Jerzak, Katarzyna2
  • Lu, Fang-I3
  • Klein, Jonathan4
  • Tabbarah, Sami5
  • Lagree, Andrew5
  • Wu, Tina5
  • Rosado-Mendez, Ivan6
  • Law, Ethan5
  • Saednia, Khadijeh7
  • Sadeghi-Naini, Ali8
  • 1 Odette Cancer Program, Sunnybrook Health Sciences Centre, Toronto, Canada; Faculty of Medicine, Department Radiation Oncology, University of Toronto, Toronto, Canada; Faculty of Health and Wellbeing, Sheffield Hallam University, Sheffield, United Kingdom; Radiogenomics Laboratory, Sunnybrook Health Sciences Centre, Toronto, Canada. Electronic address: [email protected] , (Canada)
  • 2 Odette Cancer Program, Sunnybrook Health Sciences Centre, Toronto, Canada; Radiogenomics Laboratory, Sunnybrook Health Sciences Centre, Toronto, Canada; Division of Medical Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada; Faculty of Medicine, Department of Medicine, University of Toronto, Toronto, Canada. , (Canada)
  • 3 Odette Cancer Program, Sunnybrook Health Sciences Centre, Toronto, Canada; Anatomic Pathology, Sunnybrook Health Sciences Centre, Toronto, Canada. , (Canada)
  • 4 Department of Radiation Oncology, Albert Einstein College of Medicine, Bronx, USA.
  • 5 Odette Cancer Program, Sunnybrook Health Sciences Centre, Toronto, Canada; Radiogenomics Laboratory, Sunnybrook Health Sciences Centre, Toronto, Canada. , (Canada)
  • 6 Instituto de Fisica, Universidad Nacional Autónoma de México, Mexico City, Mexico. , (Mexico)
  • 7 Radiogenomics Laboratory, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, Canada. , (Canada)
  • 8 Odette Cancer Program, Sunnybrook Health Sciences Centre, Toronto, Canada; Radiogenomics Laboratory, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, Canada. , (Canada)
Type
Published Article
Journal
Journal of medical imaging and radiation sciences
Publication Date
Dec 01, 2019
Volume
50
Issue
4S2
Identifiers
DOI: 10.1016/j.jmir.2019.07.010
PMID: 31447230
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Progress in computing power and advances in medical imaging over recent decades have culminated in new opportunities for artificial intelligence (AI), computer vision, and using radiomics to facilitate clinical decision-making. These opportunities are growing in medical specialties, such as radiology, pathology, and oncology. As medical imaging and pathology are becoming increasingly digitized, it is recently recognized that harnessing data from digital images can yield parameters that reflect the underlying biology and physiology of various malignancies. This greater understanding of the behaviour of cancer can potentially improve on therapeutic strategies. In addition, the use of AI is particularly appealing in oncology to facilitate the detection of malignancies, to predict the likelihood of tumor response to treatments, and to prognosticate the patients' risk of cancer-related mortality. AI will be critical for identifying candidate biomarkers from digital imaging and developing robust and reliable predictive models. These models will be used to personalize oncologic treatment strategies, and identify confounding variables that are related to the complex biology of tumors and diversity of patient-related factors (ie, mining "big data"). This commentary describes the growing body of work focussed on AI for precision oncology. Advances in AI-driven computer vision and machine learning are opening new pathways that can potentially impact patient outcomes through response-guided adaptive treatments and targeted therapies based on radiomic and pathomic analysis. Copyright © 2019. Published by Elsevier Inc.

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