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A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models

Abstract

Purpose

To determine whether multivariate machine learning models of algorithmically assessed magnetic resonance imaging (MRI) features from breast cancer patients are associated with Oncotype DX (ODX) test recurrence scores.

Methods

A set of 261 female patients with invasive breast cancer, pre-operative dynamic contrast enhanced magnetic resonance (DCE-MR) images and available ODX score at our institution was identified. A computer algorithm extracted a comprehensive set of 529 features from the DCE-MR images of these patients. The set of patients was divided into a training set and a test set. Using the training set we developed two machine learning-based models to discriminate (1) high ODX scores from intermediate and low ODX scores, and (2) high and intermediate ODX scores from low ODX scores. The performance of these models was evaluated on the independent test set.

Results

High against low and intermediate ODX scores were predicted by the multivariate model with AUC 0.77 (95% CI 0.56–0.98, p < 0.003). Low against intermediate and high ODX score was predicted with AUC 0.51 (95% CI 0.41–0.61, p = 0.75).

Conclusion

A moderate association between imaging and ODX score was identified. The evaluated models currently do not warrant replacement of ODX with imaging alone.

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Abbreviations

Oncotype DX:

ODX

MRI:

Magnetic resonance imaging

DCE-MR:

Dynamic contrast enhanced magnetic resonance

IHC:

Immunohistochemical

NFS:

Non-fat-saturated

FGT:

Fibroglandular tissue

ROC:

Receiver operating characteristics

AUC:

Area under receiver operating characteristics

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Funding

This study received funding from North Carolina Biotechnology Center (2016-BIG-6520) and National Institutes of Health (R01EB021360).

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Correspondence to Ashirbani Saha.

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Authors have no conflicts of interest to declare.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

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The requirement for informed consent was waived by the institutional review board.

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Saha, A., Harowicz, M.R., Wang, W. et al. A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models. J Cancer Res Clin Oncol 144, 799–807 (2018). https://doi.org/10.1007/s00432-018-2595-7

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  • DOI: https://doi.org/10.1007/s00432-018-2595-7

Keywords

  • Breast cancer MRI
  • Oncotype DX
  • Imaging features
  • Radiomics
  • Feature selection
  • Logistic regression