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Abstract #3254

Longitudinal DCE-MRI-Based Quantification of Tumor Morphological Complexity for Predicting Treatment Response in Patients with Breast Cancer

Yao Huang1, Ying Cao1, Xiaoyu Zhou1, Qian Xu1, Guangjing Weng1, Huanhuan Ren1, Xiaoxia Wang1, Jiuquan Zhang1, and Ting Yin2
1Chongqing University Cancer Hospital, Chongqing, China, 2MR Collaborations, Siemens Healthineers, Chengdu, China

Synopsis

Keywords: Data Processing, Breast

Motivation: Currently, there is a lack of standardized morphological biomarkers for Predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients in breast cancer patients.

Goal(s): To evaluate whether fractal dimensions (FDs) derived from longitudinal DCE-MRI can improve pCR prediction in breast cancer patients undergoing NAC.

Approach: A total of 232 patients received DCE-MRI before and after two NAC cycles. Variables were assessed using logistic regression and linear mixed-effects models.

Results: A model combining clinicopathologic variables and FDs showed good performance for predicting pCR to NAC with an AUC of 0.832.

Impact: A model combining clinicopathologic variables and longitudinal fractal dimensions from DCE-MRI effectively predicts pathologic complete response to NAC in breast cancer, offering a valuable tool to enhance treatment decision-making and personalized care.

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