Abstract #1982

Functional Tumor Volume by Subtype Improves Prediction of Pathological Complete Response in Breast Cancer

Matthew Gibbons¹, Wen Li¹, Nu N Le¹, Jessica E Gibbs¹, Teffany Joy Bareng¹, Natsuko Onishi¹, Lisa J Wilmes¹, Elissa R Price¹, Bonnie N Joe¹, John Kornak², Christina Yau³, Denise M Wolf⁴, Mark Jesus M Magbanua⁴, Barbara LeStage⁵, Jane Perlmutter⁶, Douglas Yee⁷, W Fraiser Symmans⁸, Hope S Rugo⁹, Rebecca Shatsky¹⁰, Claudine Issacs¹¹, I-SPY2 Investigator Network¹², I-SPY2 Imaging Working Group¹, Laura van 't Veer⁴, Angela DeMichele¹³, Laura J Esserman³, and Nola M Hylton¹

¹Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, ²Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States, ³Surgery, University of California, San Francisco, San Francisco, CA, United States, ⁴Laboratory Medicine, University of California, San Francisco, San Francisco, CA, United States, ⁵I-SPY2 Advocacy Group, Quantum Leap Healthcare Collaborative, San Franciso, CA, United States, ⁶Gemini Group, Ann Arbor, MI, United States, ⁷Masonic Cancer Center, University of Minnesota, Minneapolis, MN, United States, ⁸Anatomical Pathology, University of Texas, Houston, TX, United States, ⁹Hematology and Oncology, University of California, San Francisco, San Francisco, CA, United States, ¹⁰Univeresity of California, San Diego, San Diego, CA, United States, ¹¹Georgetown University, Washington, DC, United States, ¹²Quantum Leap Healthcare Collaborative, San Francisco, CA, United States, ¹³Hematology and Oncology, University of Pennsylvania, Philadelphia, PA, United States

Synopsis

Keywords: Breast, Treatment Response

Motivation: In the breast cancer I-SPY2 clinical trial, improvement of predictive capability for pathological complete response (pCR) would lead to improved drug candidate identification and treatment length assignment.

Goal(s): Improve functional tumor volume (FTV) predictive performance by optimizing parameter thresholds by receptor subtype.

Approach: This retrospective study used DCE-MRI to calculate FTV at different enhancement curve parameter thresholds. Resulting FTV metrics were used in prediction models for pCR. Optimal thresholds were selected to maximize receiver operating characteristic area under the curve.

Results: Each factor category (subtype, enhancement curve thresholds, and measurement timepoint) were important for improving pCR predictive performance. The maximum AUC was 0.77.

Impact: In the breast cancer I-SPY2 clinical trial, better pathological complete response (pCR) prediction would lead to improved treatment redirection and treatment sparing, improving patient outcomes. In this study, we optimized parameters for MRI functional tumor volume calculation to improve predictions.

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