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Abstract #1419

Radiomic Analysis of Pretreatment DCE-MRI and ADC for Predicting Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer

Xuetong Tao1, Ting Liang2, Liwen Wan1, Zhanli Hu1,3,4, and Na Zhang1,3,4
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Shenzhen, China, 2Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xian, China, 3Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen, China, 4United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China

Synopsis

Keywords: Diagnosis/Prediction, Breast, Pathological Complete Response, Neoadjuvant Chemotherapy, Breast Cancer, Multiparametric MRI, Multimodal Radiomic Analysis, Radiomic Analysis

Motivation: Identifying optimal responders to neoadjuvant chemotherapy (NAC) in breast cancer before treatment is challenging but crucial for optimizing therapeutic strategies.

Goal(s): Our goal was to evaluate if combining pretreatment DCE-MRI and ADC maps, with or without clinical factors, can predict treatment response in breast cancer.

Approach: This retrospective study employed a rigorous feature selection process to identify key clinical and radiomic features from semi-automatically segmented tumor regions on pretreatment MR images, followed by multivariate logistic regression to assess models' predicitve performance.

Results: DCE-MRI and ADC independently provided valuable insights, with their combination enhancing predictive performance. Integrating clinical features further improved model performance.

Impact: This study demonstrates the potential of combining pretreatment DCE-MRI, ADC maps, and clinical factors in predicting NAC repsponse in breast cancer, offering a non-invasive approach to guide personalized treatment strategies, ultimately improving patient outcomes and reducing unnecessary interventions for non-responders.

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