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Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting

Nature Protocols volume 16, pages 5309–5338 (2021)Cite this article

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Abstract

This protocol describes a complete data acquisition, analysis and computational forecasting pipeline for employing quantitative MRI data to predict the response of locally advanced breast cancer to neoadjuvant therapy in a community-based care setting. The methodology has previously been successfully applied to a heterogeneous patient population. The protocol details how to acquire the necessary images followed by registration, segmentation, quantitative perfusion and diffusion analysis, model calibration, and prediction. The data collection portion of the protocol requires ~25 min of scanning, postprocessing requires 2–3 h, and the model calibration and prediction components require ~10 h per patient depending on tumor size. The response of individual breast cancer patients to neoadjuvant therapy is forecast by application of a biophysical, reaction–diffusion mathematical model to these data. Successful application of the protocol results in coregistered MRI data from at least two scan visits that quantifies an individual tumor’s size, cellularity and vascular properties. This enables a spatially resolved prediction of how a particular patient’s tumor will respond to therapy. Expertise in image acquisition and analysis, as well as the numerical solution of partial differential equations, is required to carry out this protocol.

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Fig. 1: Overview of the protocol.
Fig. 2: Timeline of MRI acquisition with an example standard-of-care NAT regimen for triple-negative breast cancer consisting of two therapeutic regimens.
Fig. 3: FCM clustering to generate a tumor ROI.
Fig. 4: Comparison of intervisit registration results with and without tumor ROI penalties incorporated into the registration scheme.
Fig. 5: Flowchart of the data analysis steps of the protocol.
Fig. 6: Example image acquisition results.
Fig. 7: Example results from the data analysis.
Fig. 8: Converting imaging data to physical quantities for the mathematical model.
Fig. 9: Results of the 3D model predictions (over three central slices, left column) compared with the observed results at the third scan time (right column) for one example patient.

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Data availability

We have made available one patient dataset that has been fully preprocessed (i.e., Steps 1–36) and ready for the calibration and prediction components (i.e., Steps 37–40). This will enable the interested investigator to verify that the calibration and prediction code is working on the individual investigator’s platform. The dataset is available at https://github.com/ChengyueWu/Quantitative-MRI-of-breast-cancer-patients-to-forecast-response-to-therapy.

Code availability

We have made the code for the calibration and prediction components (i.e., Steps 37–40) available without charge to anyone for academic, research, experimental or personal use. This code and license may be found at https://github.com/ChengyueWu/Quantitative-MRI-of-breast-cancer-patients-to-forecast-response-to-therapy. To distribute or make other use of the software, including commercial use, a license must be obtained from The University of Texas at Austin (by contacting licensing@otc.utexas.edu).

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Acknowledgements

We offer a sincere thank you to all the women who volunteer to participate in our studies; your strength and courage are examples for all of us. We thank N. Atuegwu, S. Eldridge, X. Li, L. Arlinghaus and J. Weis for many significant contributions to developing early versions of the techniques employed in the protocol. We thank the National Cancer Institute for support via U01 CA174706 (T.E.Y.), R01CA186193 (T.E.Y.), U24CA226110 (T.E.Y.), U01CA154602 (T.E.Y.) and R01CA240589 (A.G.S.). We thank the Cancer Prevention and Research Institute of Texas (CPRIT) for funding through RR160005. T.E.Y. is a CPRIT Scholar of Cancer Research. We thank the American Cancer Society for funding through RSG-18-006-01-CCE (A.G.S). We thank the American Association of Physicists in Medicine for funding through the 2018 Research Seed Grant (D.A.H). We thank the National Institute of Biomedical Imaging and Bioengineering for supporting A.S.K. through T32 EB007507.

Author information

Author notes

  1. Anum S. Kazerouni

    Present address: Department of Radiology, University of Washington, Seattle, WA, USA

Authors and Affiliations

  1. Oden Institute for Computational Engineering and Sciences, Austin, TX, USA

    Angela M. Jarrett, Chengyue Wu, Julie C. DiCarlo, David A. Hormuth II, David A. Ekrut & Thomas E. Yankeelov

  2. Livestrong Cancer Institutes, Austin, TX, USA

    Angela M. Jarrett, John Virostko, Julie C. DiCarlo, David A. Hormuth II & Thomas E. Yankeelov

  3. Departments of Biomedical Engineering, Austin, TX, USA

    Anum S. Kazerouni & Thomas E. Yankeelov

  4. Departments of Diagnostic Medicine, Austin, TX, USA

    John Virostko & Thomas E. Yankeelov

  5. Departments of Oncology, Austin, TX, USA

    John Virostko, Boone Goodgame & Thomas E. Yankeelov

  6. Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA

    Anna G. Sorace

  7. Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, USA

    Anna G. Sorace

  8. O’Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA

    Anna G. Sorace

  9. Texas Oncology, Austin, TX, USA

    Debra Patt

  10. Departments of Internal Medicine, The University of Texas at Austin, Austin, Texas, USA

    Boone Goodgame

  11. Seton Hospital, Austin, TX, USA

    Boone Goodgame

  12. Austin Radiological Association, Austin, TX, USA

    Sarah Avery

  13. Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Thomas E. Yankeelov

Authors
  1. Angela M. Jarrett
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  2. Anum S. Kazerouni
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  3. Chengyue Wu
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  4. John Virostko
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  5. Anna G. Sorace
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  6. Julie C. DiCarlo
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  7. David A. Hormuth II
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  8. David A. Ekrut
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  9. Debra Patt
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  10. Boone Goodgame
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  11. Sarah Avery
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  12. Thomas E. Yankeelov
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Contributions

T.E.Y. conceived the project and obtained funding. J.C.D., J.V., A.G.S. and T.E.Y developed the procedures for the MRI acquisition with input from D.P., B.G. and S.A. A.S.K., C.W., D.A.H. and D.A.E. developed procedures for the data processing with input from A.M.J., J.V., A.G.S. and T.E.Y. A.M.J., C.W. and D.A.H. developed the procedures for generating modeling quantities with input from A.S.K. and T.E.Y. A.M.J., D.A.H. and T.E.Y. developed the procedures for tumor forecasting. A.M.J., J.C.D., J.V. and A.G.S. collected the data. A.M.J., A.S.K. and T.E.Y. organized the manuscript. All authors reviewed and edited the manuscript and approved the final draft.

Corresponding author

Correspondence to Thomas E. Yankeelov.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Lihua Li and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key references using this protocol

Weis, J. et al. A. Cancer Res. 74, 4697–707 (2015): https://doi.org/10.1158/0008-5472.CAN-14-2945

Jarrett, A. et al. Neoplasia. 22, 820–830 (2020): https://doi.org/10.1016/j.neo.2020.10.011

Supplementary Information

Supplementary Information

Supplementary Data 1.

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Jarrett, A.M., Kazerouni, A.S., Wu, C. et al. Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting. Nat Protoc 16, 5309–5338 (2021). https://doi.org/10.1038/s41596-021-00617-y

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  • DOI: https://doi.org/10.1038/s41596-021-00617-y

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