29 December 2017 Combining multiparametric MRI with receptor information to optimize prediction of pathologic response to neoadjuvant therapy in breast cancer: preliminary results
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Abstract
Pathologic complete response following neoadjuvant therapy (NAT) is used as a short-term surrogate marker of eventual outcome in patients with breast cancer. Analyzing voxel-level heterogeneity in MRI-derived parametric maps, obtained before and after the first cycle of NAT (n=33), in conjunction with receptor status, may improve the predictive accuracy of tumor response to NAT. Toward that end, we incorporated two MRI-derived parameters, the apparent diffusion coefficient and efflux rate constant, with receptor status in a logistic ridge-regression model. The area under the curve (AUC) and Brier score of the model computed via 10-fold cross validation were 0.94 (95% CI: 0.85, 0.99) and 0.11 (95% CI: 0.06, 0.16), respectively. These two statistics strongly support the hypothesis that our proposed model outperforms the other models that we investigated (namely, models without either receptor information or voxel-level information). The contribution of the receptor information was manifested by an 8% to 15% increase in AUC and a 14% to 21% decrease in Brier score. These data indicate that combining multiparametric MRI with hormone receptor status has a high likelihood of improved prediction of pathologic response to NAT in breast cancer.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Hakmook Kang, Allison Hainline, Lori R. Arlinghaus, Stephanie Elderidge, Xia Li, Vandana G. Abramson, Anuradha Bapsi Chakravarthy, Richard G. Abramson, Brian Bingham, Kareem Fakhoury, Thomas E. Yankeelov, "Combining multiparametric MRI with receptor information to optimize prediction of pathologic response to neoadjuvant therapy in breast cancer: preliminary results," Journal of Medical Imaging 5(1), 011015 (29 December 2017). https://doi.org/10.1117/1.JMI.5.1.011015 . Submission: Received: 30 June 2017; Accepted: 5 December 2017
Received: 30 June 2017; Accepted: 5 December 2017; Published: 29 December 2017
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