Multi-Center and Multi-Vendor Platform DCE-MRI Prediction of Breast Cancer Therapy Response: A Preliminary Comparison of Imaging Biomarkers

Motivation: Validate Shutter-Speed model (SSM) DCE-MRI as a robust predictor of breast cancer (BC) response to neoadjuvant chemotherapy (NAC) in a multi-center and multi-vendor platform setting. Goal(s): Compare tumor size, semi-quantitative, and quantitative DCE-MRI for early prediction of NAC response. Approach: BC patients treated with NAC underwent longitudinal high spatiotemporal resolution DCE-MRI at three sites using a 3T Siemens, GE, or Philips system. Semi-quantitative signal-enhancement-ratio (SER) and quantitative Tofts model (TM) and SSM pharmacokinetic (PK) parameters were derived from DCE time-course data. Results: PK parameters outperformed size and SER while SSM was superior to TM in early prediction of pathologic response. Impact: It is feasible to implement quantitative high spatiotemporal resolution SSM DCE-MRI in trials with multi-center and multi-vendor platform settings for robust assessment of BC response to NAC.

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/10.58530/2024/4824
Primary Topic
MRI in cancer diagnosis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multi-Center and Multi-Vendor Platform DCE-MRI Prediction of Breast Cancer Therapy Response: A Preliminary Comparison of Imaging Biomarkers

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

Multi-Center and Multi-Vendor Platform DCE-MRI Prediction of Breast Cancer Therapy Response: A Preliminary Comparison of Imaging Biomarkers

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: Validate Shutter-Speed model (SSM) DCE-MRI as a robust predictor of breast cancer (BC) response to neoadjuvant chemotherapy (NAC) in a multi-center and multi-vendor platform setting. Goal(s): Compare tumor size, semi-quantitative, and quantitative DCE-MRI for early prediction of NAC response. Approach: BC patients treated with NAC underwent longitudinal high spatiotemporal resolution DCE-MRI at three sites using a 3T Siemens, GE, or Philips system. Semi-quantitative signal-enhancement-ratio (SER) and quantitative Tofts model (TM) and SSM pharmacokinetic (PK) parameters were derived from DCE time-course data. Results: PK parameters outperformed size and SER while SSM was superior to TM in early prediction of pathologic response. Impact: It is feasible to implement quantitative high spatiotemporal resolution SSM DCE-MRI in trials with multi-center and multi-vendor platform settings for robust assessment of BC response to NAC.

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 63%
MRI in cancer diagnosis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.