Abstract #1556

Deep learning-based prediction of response to neoadjuvant immunochemotherapy in triple-negative breast cancer based on pretreatment DCE-MRI

Ziyu Fu¹, Yuexin Liu¹, Zhan Xu¹, Jong Bum Son¹, Xiaofei Huo², Tanya Moseley³, Beatriz E. Adrada³, Clinton Yam⁴, Jingfei Ma¹, and Gaiane M. Rauch²

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

Synopsis

Keywords: Diagnosis/Prediction, Cancer, TNBC, DCE-MRI, treatment response

Motivation: Accurate prediction of the response to neoadjuvant immunochemotherapy (NICT) in triple negative breast cancer (TNBC) is valuable in guiding individualized treatment.

Goal(s): To develop a deep learning-based model to predict response before the initiation of NICT.

Approach: Our deep-learning model used pretreatment DCE-MRI from 100 TNBC patients.

Results: Although preliminary and limited by a small patient number, our model yielded an average AUC of 0.63 in predicting the response to NICT in an independent testing.

Impact: A deep learning model has the potential to predict TNBC response to NICT before treatment and to help with the clinical management.

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