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

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 ChurchPatrick 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 P. Crane, Charlotte Dillis, Pulin Sheth, Linda Hovanessian-Larsen, Dae Hee Bang, Bruce Porter, Karen Y. Oh, Neda Jafarian, Alina Tudorica, Bethany L. Niell, Jennifer Drukteinis, Mary S. Newell, Michael A. Cohen, Marina Giurescu, Elise Berman, Constance Lehman, Savannah C. Partridge, Kimberly A. 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

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype.

Original languageEnglish (US)
Article number63
Journalnpj Breast Cancer
Issue number1
StatePublished - Dec 2020

Bibliographical note

Funding Information:
The authors would like to thank all patients who participated in the I-SPY 2 Trial, working group chairs, investigators, and study coordinators from all participant sites for their contributions to the project. This research is supported by NIH grants U01 CA225427, R01 CA132870, and P01 CA210961. The I-SPY 2 Trial is supported by Quantum Leap Healthcare Collaborative (2013 to present).

Publisher Copyright:
© 2020, The Author(s).


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