Abstract #0125

Deep Learning Radiomic Analysis of DCE-MRI Predicts Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer

Yang Yang¹, Yaheng Fan², Xiaotong Xie³, Bingsheng Huang², Yuting Li⁴, Yan Li⁵, Dinghua Xu⁶, and Bihua Liu⁵

¹Department of Radiology, Suining Central Hospital, Suining, China, ²Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China, ³School of Life Science, South China Normal University, Guangzhou, China, ⁴The First Clinical Medical College, Guangdong Medical University, Zhanjiang, China, ⁵Department of Radiology, Dongguan People's Hospital, Dongguan, China, ⁶Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China

Synopsis

Keywords: Breast, Treatment, dynamic contrast-enhanced magnetic resonance imaging, pathological complete response, radiomicsBased on pre-treatment and early treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical characteristics, we established a pathological complete response (pCR) prediction model using a deep learning radiomic (DLR) method that achieved good performance in the training and validation cohorts. The model can help clinicians evaluate whether the patient can reach pCR after neoadjuvant chemotherapy (NAC) and can provide an effective diagnostic reference for accurate medical treatment of patients receiving NAC.

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