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Li W, Newitt DC, Gibbs J, et al. Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL. NPJ Breast Cancer. 2020;6(1):63doi: 10.1038/s41523-020-00203-7.
Li, W., Newitt, D. C., Gibbs, J., Wilmes, L. J., Jones, E. F., Arasu, V. A., Strand, F., Onishi, N., Nguyen, A. A., Kornak, J., Joe, B. N., Price, E. R., Ojeda-Fournier, H., Eghtedari, M., Zamora, K. W., Woodard, S. A., Umphrey, H., Bernreuter, W., Nelson, M., Church, A. L., Bolan, P., Kuritza, T., Ward, K., Morley, K., Wolverton, D., Fountain, K., Lopez-Paniagua, D., Hardesty, L., Brandt, K., McDonald, E. S., Rosen, M., Kontos, D., Abe, H., Sheth, D., Crane, E. P., Dillis, C., Sheth, P., Hovanessian-Larsen, L., Bang, D. H., Porter, B., Oh, K. Y., Jafarian, N., Tudorica, A., Niell, B. L., Drukteinis, J., Newell, M. S., Cohen, M. A., Giurescu, M., Berman, E., Lehman, C., Partridge, S. C., Fitzpatrick, K. A., Borders, M. H., Yang, W. T., Dogan, B., Goudreau, S., Chenevert, T., Yau, C., DeMichele, A., Berry, D., Esserman, L. J., & Hylton, N. M. (2020). Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL. NPJ breast cancer, 6(1), 63. https://doi.org/10.1038/s41523-020-00203-7
Li, Wen, et al. "Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL." NPJ breast cancer vol. 6,1 (2020): 63. doi: https://doi.org/10.1038/s41523-020-00203-7
Li W, Newitt DC, Gibbs J, Wilmes LJ, Jones EF, Arasu VA, Strand F, Onishi N, Nguyen AA, Kornak J, Joe BN, Price ER, Ojeda-Fournier H, Eghtedari M, Zamora KW, Woodard SA, Umphrey H, Bernreuter W, Nelson M, Church AL, Bolan P, Kuritza T, Ward K, Morley K, Wolverton D, Fountain K, Lopez-Paniagua D, Hardesty L, Brandt K, McDonald ES, Rosen M, Kontos D, Abe H, Sheth D, Crane EP, Dillis C, Sheth P, Hovanessian-Larsen L, Bang DH, Porter B, Oh KY, Jafarian N, Tudorica A, Niell BL, Drukteinis J, Newell MS, Cohen MA, Giurescu M, Berman E, Lehman C, Partridge SC, Fitzpatrick KA, Borders MH, Yang WT, Dogan B, Goudreau S, Chenevert T, Yau C, DeMichele A, Berry D, Esserman LJ, Hylton NM. Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL. NPJ Breast Cancer. 2020 Nov 27;6(1):63. doi: 10.1038/s41523-020-00203-7. PMID: 33298938; PMCID: PMC7695723.
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NPJ Breast Cancer. 2020 Nov 27;6(1):63. doi: 10.1038/s41523-020-00203-7.

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

NPJ breast cancer

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 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

Affiliations

  1. University of California, San Francisco, CA, USA.
  2. Karolinska Institute, Stockholm, Sweden.
  3. University of California, San Diego, CA, USA.
  4. University of Alabama, Birmingham, AL, USA.
  5. University of Minnesota, Minneapolis, MN, USA.
  6. Loyola University, Maywood, IL, USA.
  7. University of Colorado, Denver, CO, USA.
  8. Mayo Clinic, Rochester, NY, USA.
  9. University of Pennsylvania, Philadelphia, PA, USA.
  10. University of Chicago, Chicago, IL, USA.
  11. Georgetown University, Georgetown, DC, USA.
  12. University of Southern California, Los Angeles, CA, USA.
  13. Swedish Cancer Institute, Seattle, WA, USA.
  14. Oregon Health & Science University, Portland, OR, USA.
  15. Moffitt Cancer Center, Tampa, FL, USA.
  16. Emory University, Atlanta, GA, USA.
  17. Mayo Clinic, Scottsdale, AZ, USA.
  18. Inova Health System, Falls Church, VA, USA.
  19. University of Washington, Seattle, WA, USA.
  20. University of Arizona, Tucson, AZ, USA.
  21. University of Texas, M.D. Anderson Cancer Center, Houston, TX, USA.
  22. University of Texas Southwestern, Dallas, TX, USA.
  23. University of Michigan, Ann Arbor, MI, USA.
  24. Berry Consultants, LLC, Austin, TX, USA.
  25. University of California, San Francisco, CA, USA. [email protected].

PMID: 33298938 PMCID: PMC7695723 DOI: 10.1038/s41523-020-00203-7

Abstract

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.

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