Technical Considerations for Implementing Multi-Center and Multi-Platform Quantitative DCE-MRI to Predict Breast Cancer Therapy Response

Motivation: Determine best-practice quantitative DCE-MRI for predicting breast cancer (BC) response to neoadjuvant chemotherapy (NAC) in a multi-center (MC) and multi-vendor platform (MP) setting. Goal(s): Evaluate effects of different pharmacokinetic analysis approaches on Ktrans and its predictive performance. Approach: 15 BC patients treated with NAC underwent longitudinal DCE-MRI at 3 sites using 3T systems from 3 vendors. Variations in analysis included Tofts model vs. Shutter-Speed model (SSM), ROI- vs. voxel-based analysis, and using fixed vs. measured R10. Results: Different analysis approaches resulted in significantly different Ktrans, with SSM Ktrans from voxel-based analysis using fixed R10 showing highest predictive accuracy for response. Impact: Voxel-based SSM analysis using fixed R10 takes advantage of greater range of SSM Ktrans changes in response to therapy, mitigates R10 measurement errors, and may be the best-practice quantitative DCE-MRI for predicting NAC response in a MC and MP setting.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2024-11-26
DOI
https://doi.org/10.58530/2024/1167
Primary Topic
MRI in cancer diagnosis
Type
article
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article

Technical Considerations for Implementing Multi-Center and Multi-Platform Quantitative DCE-MRI to Predict Breast Cancer Therapy Response

Evthokia Hobbs, Debosmita Biswas, Savannah C. Partridge, Su Kim et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
MRI in cancer diagnosis
article

Technical Considerations for Implementing Multi-Center and Multi-Platform Quantitative DCE-MRI to Predict Breast Cancer Therapy Response

Evthokia Hobbs, Debosmita Biswas, Savannah C. Partridge, Su Kim, Jennifer M. Specht, Suzanne M. Dintzis, Anum S. Kazerouni, Michael Hirano, Habib Rahbar, Christian Wille, Mary Lynn Bryant, James H. Holmes, Travis Rice‐Stitt, Chelsea Pyle, Isabella Li, Sneha Phadke, Alexandra S. Zimmer, Nicole Margo Grogan Fleege, Amani Bashir, Alina Tudorica, Assim Saad Eddin, Xin Li, Wei Huang, Brendan Moloney, Jeong Youn Lim
article en

Abstract

Motivation: Determine best-practice quantitative DCE-MRI for predicting breast cancer (BC) response to neoadjuvant chemotherapy (NAC) in a multi-center (MC) and multi-vendor platform (MP) setting. Goal(s): Evaluate effects of different pharmacokinetic analysis approaches on Ktrans and its predictive performance. Approach: 15 BC patients treated with NAC underwent longitudinal DCE-MRI at 3 sites using 3T systems from 3 vendors. Variations in analysis included Tofts model vs. Shutter-Speed model (SSM), ROI- vs. voxel-based analysis, and using fixed vs. measured R10. Results: Different analysis approaches resulted in significantly different Ktrans, with SSM Ktrans from voxel-based analysis using fixed R10 showing highest predictive accuracy for response. Impact: Voxel-based SSM analysis using fixed R10 takes advantage of greater range of SSM Ktrans changes in response to therapy, mitigates R10 measurement errors, and may be the best-practice quantitative DCE-MRI for predicting NAC response in a MC and MP setting.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Good health and well-being
Openalex Percentile: Top 64%
MRI in cancer diagnosis
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