Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL
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Published:2020-11-27
Issue:1
Volume:6
Page:
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ISSN:2374-4677
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Container-title:npj Breast Cancer
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language:en
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Short-container-title:npj Breast Cancer
Author:
Li WenORCID, Newitt David C.ORCID, Gibbs Jessica, Wilmes Lisa J., Jones Ella F.ORCID, Arasu Vignesh A., Strand Fredrik, Onishi NatsukoORCID, Nguyen Alex Anh-Tu, Kornak John, Joe Bonnie N., Price Elissa R., Ojeda-Fournier Haydee, Eghtedari MohammadORCID, Zamora Kathryn W., Woodard Stefanie A., Umphrey Heidi, Bernreuter Wanda, Nelson Michael, Church An LyORCID, Bolan Patrick, Kuritza Theresa, Ward Kathleen, Morley Kevin, Wolverton DulcyORCID, Fountain Kelly, Lopez-Paniagua Dan, Hardesty Lara, Brandt Kathy, McDonald Elizabeth S., Rosen Mark, Kontos Despina, Abe Hiroyuki, Sheth DeepaORCID, Crane Erin P., Dillis Charlotte, Sheth Pulin, Hovanessian-Larsen Linda, Bang Dae Hee, Porter Bruce, Oh Karen Y., Jafarian Neda, Tudorica Alina, Niell Bethany L.ORCID, Drukteinis Jennifer, Newell Mary S., Cohen Michael A., Giurescu Marina, Berman Elise, Lehman Constance, Partridge Savannah C., Fitzpatrick Kimberly A., Borders Marisa H., Yang Wei T., Dogan Basak, Goudreau SallyORCID, Chenevert Thomas, Yau Christina, DeMichele Angela, Berry Don, Esserman Laura J.ORCID, Hylton Nola M.ORCID
Abstract
AbstractDynamic 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.
Funder
U.S. Department of Health & Human Services | NIH | National Cancer Institute
Publisher
Springer Science and Business Media LLC
Subject
Pharmacology (medical),Radiology, Nuclear Medicine and imaging,Oncology
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