Abstract #1119

Early prediction of pathologic complete response to neoadjuvant systemic therapy for triple-negative breast cancer using deep learning

Zijian Zhou¹, David E. Rauch¹, Jong Bum Son¹, Benjamin C. Musall¹, Nabil A. Elshafeey², Jason B. White³, Mark D. Pagel⁴, Stacy Moulder³, and Jingfei Ma¹

¹Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ²Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ³Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ⁴Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States

Prediction of response to neoadjuvant systemic therapy for triple-negative breast cancer is important for patient management. Here we constructed a deep learning convolutional and recursive neural network ensemble for early prediction of pathologic complete response utilizing pre-treatment DCE and DWI breast MRIs. Images from 135 patients were partitioned into training/validation/testing groups with the ratio of 80/20/35. For the testing group, the network achieved an accuracy of 69%, with the sensitivity of 75% and specificity of 63%. The area under the receiver operating characteristic curve was 0.68.

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