APA
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Wu, C., Jarrett, A. M., Zhou, Z., Elshafeey, N., Adrada, B., Candelaria, R., … Yankeelov, T. (2022). MRI-based digital models forecast patient-specific treatment responses to neoadjuvant chemotherapy in triple-negative breast cancer. Cancer Research.
Chicago/Turabian
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Wu, Chengyue, A. M. Jarrett, Zijian Zhou, N. Elshafeey, B. Adrada, R. Candelaria, Rania M M Mohamed, et al. “MRI-Based Digital Models Forecast Patient-Specific Treatment Responses to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer.” Cancer Research (2022).
MLA
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Wu, Chengyue, et al. “MRI-Based Digital Models Forecast Patient-Specific Treatment Responses to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer.” Cancer Research, 2022.
BibTeX Click to copy
@article{chengyue2022a,
title = {MRI-based digital models forecast patient-specific treatment responses to neoadjuvant chemotherapy in triple-negative breast cancer.},
year = {2022},
journal = {Cancer Research},
author = {Wu, Chengyue and Jarrett, A. M. and Zhou, Zijian and Elshafeey, N. and Adrada, B. and Candelaria, R. and Mohamed, Rania M M and Boge, M. and Huo, L. and White, J. and Tripathy, D. and Valero, V. and Litton, J. and Yam, C. and Son, J. and Ma, Jingfei and Rauch, G. and Yankeelov, T.}
}
Triple-negative breast cancer (TNBC) is persistently refractory to therapy, and methods to improve targeting and evaluation of responses to therapy in this disease are needed. Here, we integrate quantitative magnetic resonance imaging (MRI) data with biologically-based mathematical modeling to accurately predict the response of TNBC to neoadjuvant systemic therapy (NAST) on an individual basis. Specifically, 56 TNBC patients enrolled in the ARTEMIS trial (NCT02276443) underwent standard-of-care doxorubicin/cyclophosphamide (A/C) and then paclitaxel for NAST, where dynamic contrast-enhanced MRI and diffusion-weighted MRI were acquired before treatment and after two and four cycles of A/C. A biologically-based model was established to characterize tumor cell movement, proliferation, and treatment-induced cell death. Two evaluation frameworks were investigated using: 1) images acquired before and after two cycles of A/C for calibration and predicting tumor status after A/C, and 2) images acquired before, after two cycles, and after four cycles of A/C for calibration and predicting response following NAST. For Framework 1, the concordance correlation coefficients between the predicted and measured patient-specific, post-A/C changes in tumor cellularity and volume were 0.95 and 0.94, respectively. For Framework 2, the biologically-based model achieved an area under the receiver operator characteristic curve of 0.89 (sensitivity/specificity = 0.72/0.95) for differentiating pathological complete response (pCR) from non-pCR, which is statistically superior (P < 0.05) to the value of 0.78 (sensitivity/specificity = 0.72/0.79) achieved by tumor volume measured after four cycles of A/C. Overall, this model successfully captured patient-specific, spatiotemporal dynamics of TNBC response to NAST, providing highly accurate predictions of NAST response.
