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 2â3 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, reactionâdiffusion 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 tumorâs size, cellularity and vascular properties. This enables a spatially resolved prediction of how a particular patientâs 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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Data availability
We have made available one patient dataset that has been fully preprocessed (i.e., Steps 1â36) and ready for the calibration and prediction components (i.e., Steps 37â40). This will enable the interested investigator to verify that the calibration and prediction code is working on the individual investigatorâs 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 37â40) 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.
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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.
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Weis, J. et al. A. Cancer Res. 74, 4697â707 (2015): https://doi.org/10.1158/0008-5472.CAN-14-2945
Jarrett, A. et al. Neoplasia. 22, 820â830 (2020): https://doi.org/10.1016/j.neo.2020.10.011
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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, 5309â5338 (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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