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Image-based biomarkers for solid tumor quantification

  • Peter Savadjiev
  • Jaron Chong
  • Anthony Dohan
  • Vincent Agnus
  • Reza Forghani
  • Caroline Reinhold
  • Benoit GallixEmail author
Oncology

Abstract

The last few decades have witnessed tremendous technological developments in image-based biomarkers for tumor quantification and characterization. Initially limited to manual one- and two-dimensional size measurements, image biomarkers have evolved to harness developments not only in image acquisition technology but also in image processing and analysis algorithms. At the same time, clinical validation remains a major challenge for the vast majority of these novel techniques, and there is still a major gap between the latest technological developments and image biomarkers used in everyday clinical practice. Currently, the imaging biomarker field is attracting increasing attention not only because of the tremendous interest in cutting-edge therapeutic developments and personalized medicine but also because of the recent progress in the application of artificial intelligence (AI) algorithms to large-scale datasets. Thus, the goal of the present article is to review the current state of the art for image biomarkers and their use for characterization and predictive quantification of solid tumors. Beginning with an overview of validated imaging biomarkers in current clinical practice, we proceed to a review of AI-based methods for tumor characterization, such as radiomics-based approaches and deep learning.

Key Points

Recent years have seen tremendous technological developments in image-based biomarkers for tumor quantification and characterization.

Image-based biomarkers can be used on an ongoing basis, in a non-invasive (or mildly invasive) way, to monitor the development and progression of the disease or its response to therapy.

We review the current state of the art for image biomarkers, as well as the recent developments in artificial intelligence (AI) algorithms for image processing and analysis.

Keywords

Diagnostic imaging Biomarkers Artificial intelligence (AI) Computer-assisted image processing Computer-assisted image interpretation 

Abbreviations

18F-FDG PET

18F-fluorodeoxyglucose positron emission tomography

AI

Artificial intelligence

CNN

Convolutional neural network

EASL

European Association for the Study of the Liver

mRECIST

Modified Response Evaluation Criteria in Solid Tumors

PERCIST

Positron Emission Tomography Response Criteria in Solid Tumors

RECIST

Response Evaluation Criteria in Solid Tumors

WHO

World Health Organization

Notes

Funding

The authors state that this work has not received any funding.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Dr. Benoit Gallix.

Conflict of interest

The authors declare that they have no competing interests.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was not required for this study because this is a review article, and no study was performed.

Ethical approval

Institutional review board approval was not required because this is a review article, and no study was performed.

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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of Diagnostic RadiologyMcGill UniversityMontrealCanada
  2. 2.Department of Diagnostic Radiology, McGill University Health CentreMcGill UniversityMontrealCanada
  3. 3.Department of Body and Interventional Imaging, Hôpital Lariboisière-AP-HPUniversité Diderot-Paris 7 and INSERM U965Paris Cedex 10France
  4. 4.Institut de chirurgie guidée par l’image IHU StrasbourgStrasbourg CedexFrance
  5. 5.Department of RadiologyJewish General HospitalMontrealCanada

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