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Are we at a crossroads or a plateau? Radiomics and machine learning in abdominal oncology imaging

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Abstract

Advances in radiomics and machine learning have driven a technology boom in the automated analysis of radiology images. For the past several years, expectations have been nearly boundless for these new technologies to revolutionize radiology image analysis and interpretation. In this editorial, I compare the expectations with the realities with particular attention to applications in abdominal oncology imaging. I explore whether these technologies will leave us at a crossroads to an exciting future or to a sustained plateau and disillusionment.

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Fig. 1

Reproduced from Ref. [18]

Fig. 2

Reproduced from [28]

Fig. 3

Reproduced from [36]

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Authors and Affiliations

  1. Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences Department, National Institutes of Health Clinical Center, Bldg. 10 Room 1C224D, MSC 1182, Bethesda, MD, 20892-1182, USA

    Ronald M. Summers

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  1. Ronald M. Summers
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Correspondence to Ronald M. Summers.

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Funding

This study was funded by the Intramural Research Program of the National Institutes of Health, Clinical Center (Grant Number 1Z01 CL040004).

Conflicts of interest

The author has pending and/or awarded patents for automated image analyses, and received royalty income from iCAD, Zebra Medical, Imbio, ScanMed and Koninklijke Philips. His lab received research support from Ping An Technology Company Ltd. and NVIDIA.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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N/A.

Additional information

This work is dedicated to the memory of my former colleague, Andrew Dwyer, MD.

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Summers, R.M. Are we at a crossroads or a plateau? Radiomics and machine learning in abdominal oncology imaging. Abdom Radiol 44, 1985–1989 (2019). https://doi.org/10.1007/s00261-018-1613-1

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  • DOI: https://doi.org/10.1007/s00261-018-1613-1

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