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Metadata record for the manuscript: Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL

dataset

posted on 2020-10-28, 14:38 authored by Wen Li, David C. Newitt, Jessica Gibbs, Lisa J. Wilmes, Ella F. Jones, Vignesh A. Arasu, Fredrik Strand, Natsuko Onishi, Alex Anh-Tu Nguyen, John Kornak, Bonnie N. Joe, Elissa R. Price, Haydee Ojeda-Fournier, Mohammad Eghtedari, Kathryn W. Zamora, Stefanie A. Woodard, Heidi Umphrey, Wanda Bernreuter, Michael Nelson, An Ly Church, Patrick Bolan, Theresa Kuritza, Kathleen Ward, Kevin Morley, Dulcy Wolverton, Kelly Fountain, Dan Lopez Paniagua, Lara Hardesty, Kathy Brandt, Elizabeth S. McDonald, Mark Rosen, Despina Kontos, Hiroyuki Abe, Deepa Sheth, Erin Crane, Charlotte Dillis, Pulin Sheth, Linda Hovanessian-Larsen, Dae Hee Bang, Bruce Porter, Karen Y. Oh, Neda Jafarian, Alina Tudorica, Bethany Niell, Jennifer Drukteinis, Mary S. Newell, Michael A. Cohen, Marina Gjurescu, Elise Berman, Connie Lehman, Savannah Partridge, Kim Fitzpatrick, Marisa H. Borders, Wei T. Yang, Basak Dogan, Sally Goudreau, Thomas Chenevert, Christina Yau, Angela DeMichele, Don Berry, Laura J. Esserman, Nola M. Hylton

Summary

This metadata record provides details of the data supporting the claims of the related manuscript: “Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL” The data consist of original acquired and derived MRI DICOM data and an Excel spreadsheet. The related study investigated whether the predictive performance of MRI can be improved over functional tumor volume (FTV) or any single feature alone by using a combination of features measured on dynamic contrast-enhanced MRI (DCE-MRI).

Data access

The datasets generated during and/or analysed during the current study are as follows: the original acquired and derived MRI DICOM data, under the title "I-SPY2 MRI Collection", and an Excel file called “Multi-feature MRI NACT DATA.xlsx”. These will be deposited and be publicly available in NCI The Cancer Imaging Archive (TCIA): https://www.cancerimagingarchive.net/. However, due to technical limitations with the deposition and curation of the data, their release date is anticipated to be late 2020. Please contact the corresponding author with data queries.

Corresponding author

Professor Nola Hylton, nola.hylton@ucsf.edu

Name of Institutional Review Board or ethics committee that approved the study

All participating sites received approval from their institutional review board.

Funding

U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) - R01 CA132870

U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) - U01 CA225427

U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) - P01 CA210961

History

Research Data Support

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