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Pancreas image mining: a systematic review of radiomics

  • Hepatobiliary-Pancreas
  • Published:
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Abstract

Objectives

To systematically review published studies on the use of radiomics of the pancreas.

Methods

The search was conducted in the MEDLINE database. Human studies that investigated the applications of radiomics in diseases of the pancreas were included. The radiomics quality score was calculated for each included study.

Results

A total of 72 studies encompassing 8863 participants were included. Of them, 66 investigated focal pancreatic lesions (pancreatic cancer, precancerous lesions, or benign lesions); 4, pancreatitis; and 2, diabetes mellitus. The principal applications of radiomics were differential diagnosis between various types of focal pancreatic lesions (n = 19), classification of pancreatic diseases (n = 23), and prediction of prognosis or treatment response (n = 30). Second-order texture features were most useful for the purpose of differential diagnosis of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature), whereas filtered image features were most useful for the purpose of classification of diseases of the pancreas and prediction of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature). The median radiomics quality score of the included studies was 28%, with the interquartile range of 22% to 36%. The radiomics quality score was significantly correlated with the number of extracted radiomics features (r = 0.52, p < 0.001) and the study sample size (r = 0.34, p = 0.003).

Conclusions

Radiomics of the pancreas holds promise as a quantitative imaging biomarker of both focal pancreatic lesions and diffuse changes of the pancreas. The usefulness of radiomics features may vary depending on the purpose of their application. Standardisation of image acquisition protocols and image pre-processing is warranted prior to considering the use of radiomics of the pancreas in routine clinical practice.

Key Points

• Methodologically sound studies on radiomics of the pancreas are characterised by a large sample size and a large number of extracted features.

• Optimisation of the radiomics pipeline will increase the clinical utility of mineable pancreas imaging data.

• Radiomics of the pancreas is a promising personalised medicine tool in diseases of the pancreas.

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Abbreviations

RQS:

Radiomics quality score

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Acknowledgements

This study was part of the COSMOS program.

Funding

COSMOS is supported, in part, by the Royal Society of New Zealand (Rutherford Discovery Fellowship to Associate Professor Max Petrov).

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Authors and Affiliations

  1. School of Medicine, University of Auckland, Auckland, New Zealand

    Bassam M. Abunahel & Maxim S. Petrov

  2. School of Medical Sciences, University of Auckland, Auckland, New Zealand

    Beau Pontre

  3. Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand

    Haribalan Kumar

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  1. Bassam M. Abunahel
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  2. Beau Pontre
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  3. Haribalan Kumar
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  4. Maxim S. Petrov
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Correspondence to Maxim S. Petrov.

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The scientific guarantor of this publication is Associate Professor Max Petrov, MD, MPH, PhD.

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Abunahel, B.M., Pontre, B., Kumar, H. et al. Pancreas image mining: a systematic review of radiomics. Eur Radiol 31, 3447–3467 (2021). https://doi.org/10.1007/s00330-020-07376-6

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