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

Autor: Jarrett AM; Oden Institute for Computational Engineering and Sciences, Austin, TX, USA.; Livestrong Cancer Institutes, Austin, TX, USA., Kazerouni AS; Departments of Biomedical Engineering, Austin, TX, USA.; Department of Radiology, University of Washington, Seattle, WA, USA., Wu C; Oden Institute for Computational Engineering and Sciences, Austin, TX, USA., Virostko J; Livestrong Cancer Institutes, Austin, TX, USA.; Departments of Diagnostic Medicine, Austin, TX, USA.; Departments of Oncology, Austin, TX, USA., Sorace AG; Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA.; Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, USA.; O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA., DiCarlo JC; Oden Institute for Computational Engineering and Sciences, Austin, TX, USA.; Livestrong Cancer Institutes, Austin, TX, USA., Hormuth DA 2nd; Oden Institute for Computational Engineering and Sciences, Austin, TX, USA.; Livestrong Cancer Institutes, Austin, TX, USA., Ekrut DA; Oden Institute for Computational Engineering and Sciences, Austin, TX, USA., Patt D; Texas Oncology, Austin, TX, USA., Goodgame B; Departments of Oncology, Austin, TX, USA.; Departments of Internal Medicine, The University of Texas at Austin, Austin, Texas, USA.; Seton Hospital, Austin, TX, USA., Avery S; Austin Radiological Association, Austin, TX, USA., Yankeelov TE; Oden Institute for Computational Engineering and Sciences, Austin, TX, USA. thomas.yankeelov@utexas.edu.; Livestrong Cancer Institutes, Austin, TX, USA. thomas.yankeelov@utexas.edu.; Departments of Biomedical Engineering, Austin, TX, USA. thomas.yankeelov@utexas.edu.; Departments of Diagnostic Medicine, Austin, TX, USA. thomas.yankeelov@utexas.edu.; Departments of Oncology, Austin, TX, USA. thomas.yankeelov@utexas.edu.; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. thomas.yankeelov@utexas.edu.
Jazyk: angličtina
Zdroj: Nature protocols [Nat Protoc] 2021 Nov; Vol. 16 (11), pp. 5309-5338. Date of Electronic Publication: 2021 Sep 22.
DOI: 10.1038/s41596-021-00617-y
Abstrakt: 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.
(© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)
Databáze: MEDLINE
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