Deformable image registration as a tool to improve survival prediction after neoadjuvant chemotherapy for breast cancer: results from the ACRIN 6657/I-SPY-1 trial

Nariman Jahani; Eric Cohen; Meng-Kang Hsieh; Susan P. Weinstein; Lauren Pantalone; Christos Davatzikos; Despina Kontos

doi:10.1117/12.2293720

27 February 2018 Deformable image registration as a tool to improve survival prediction after neoadjuvant chemotherapy for breast cancer: results from the ACRIN 6657/I-SPY-1 trial

Nariman Jahani, Eric Cohen, Meng-Kang Hsieh, Susan P. Weinstein, Lauren Pantalone, Christos Davatzikos, Despina Kontos

Author Affiliations +

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 105752S (2018) https://doi.org/10.1117/12.2293720
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

We examined the ability of DCE-MRI longitudinal features to give early prediction of recurrence-free survival (RFS) in women undergoing neoadjuvant chemotherapy for breast cancer, in a retrospective analysis of 106 women from the ISPY 1 cohort. These features were based on the voxel-wise changes seen in registered images taken before treatment and after the first round of chemotherapy. We computed the transformation field using a robust deformable image registration technique to match breast images from these two visits. Using the deformation field, parametric response maps (PRM) — a voxel-based feature analysis of longitudinal changes in images between visits — was computed for maps of four kinetic features (signal enhancement ratio, peak enhancement, and wash-in/wash-out slopes). A two-level discrete wavelet transform was applied to these PRMs to extract heterogeneity information about tumor change between visits. To estimate survival, a Cox proportional hazard model was applied with the C statistic as the measure of success in predicting RFS. The best PRM feature (as determined by C statistic in univariable analysis) was determined for each of the four kinetic features. The baseline model, incorporating functional tumor volume, age, race, and hormone response status, had a C statistic of 0.70 in predicting RFS. The model augmented with the four PRM features had a C statistic of 0.76. Thus, our results suggest that adding information on the texture of voxel-level changes in tumor kinetic response between registered images of first and second visits could improve early RFS prediction in breast cancer after neoadjuvant chemotherapy.

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Nariman Jahani, Eric Cohen, Meng-Kang Hsieh, Susan P. Weinstein, Lauren Pantalone, Christos Davatzikos, and Despina Kontos "Deformable image registration as a tool to improve survival prediction after neoadjuvant chemotherapy for breast cancer: results from the ACRIN 6657/I-SPY-1 trial", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105752S (27 February 2018); https://doi.org/10.1117/12.2293720

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