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Proceedings Paper

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
Author(s): Nariman Jahani; Eric Cohen; Meng-Kang Hsieh; Susan P. Weinstein; Lauren Pantalone; Christos Davatzikos; Despina Kontos
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Paper 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.

Paper Details

Date Published: 27 February 2018
PDF: 7 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105752S (27 February 2018); doi: 10.1117/12.2293720
Show Author Affiliations
Nariman Jahani, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Eric Cohen, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Meng-Kang Hsieh, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Susan P. Weinstein, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Lauren Pantalone, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Christos Davatzikos, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Despina Kontos, Perelman School of Medicine, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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