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Image-based biomarkers for solid tumor quantification

  • Oncology
  • Published:
European Radiology Aims and scope Submit manuscript

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.

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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

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Funding

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

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Authors and Affiliations

  1. Department of Diagnostic Radiology, McGill University, Montreal, QC, Canada

    Peter Savadjiev

  2. Department of Diagnostic Radiology, McGill University Health Centre, McGill University, 1001 Décarie Boulevard, Montreal, QC, H4A 3J1, Canada

    Jaron Chong, Anthony Dohan, Reza Forghani, Caroline Reinhold & Benoit Gallix

  3. Department of Body and Interventional Imaging, Hôpital Lariboisière-AP-HP, Université Diderot-Paris 7 and INSERM U965, 2 rue Ambroise Paré, 75475, Paris Cedex 10, France

    Anthony Dohan

  4. Institut de chirurgie guidée par l’image IHU Strasbourg, 1, place de l’Hôpital, 67091, Strasbourg Cedex, France

    Vincent Agnus & Benoit Gallix

  5. Department of Radiology, Jewish General Hospital, 3755 Chemin de la Côte-Sainte-Catherine, Montreal, QC, H3T 1E2, Canada

    Reza Forghani

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  1. Peter Savadjiev
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  2. Jaron Chong
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  4. Vincent Agnus
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  7. Benoit Gallix
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Correspondence to Benoit Gallix.

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Savadjiev, P., Chong, J., Dohan, A. et al. Image-based biomarkers for solid tumor quantification. Eur Radiol 29, 5431–5440 (2019). https://doi.org/10.1007/s00330-019-06169-w

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