Deep-learning-based background parenchymal enhancement quantification in contrast enhanced mammography: an application to neoadjuvant chemotherapy

Contrast-Enhanced Mammography (CEM) is an emerging breast imaging technique that utilizes dual-energy x-ray mammography with iodine contrast agent to enhance tumor visualization. This study focuses on the quantitative analysis of breast Background Parenchymal Enhancement (BPE) evolution during Neoadjuvant Chemotherapy (NAC) using a deep learning BPE quantification model in CEM. The dataset includes 72 patients undergoing NAC, and BPE levels are assessed in pre- and post-NAC CEM exams using a ResNet18-based deep learning model. Results confirm that BPE level decreases during NAC. The analysis also highlights a linear correlation between BPE change and initial pre-NAC BPE levels. This emphasizes the need to consider not only the absolute value of the BPE change, but also the BPE change residual to the linear fit for unbiased analysis of the association between the completeness of the NAC treatment response and BPE evolution. No significant association between BPE evolution and NAC treatment response was observed on the unstratified dataset. After stratifying the patient population according to age, tumor phenotype, and grade, no statistical differences were found between the distributions of BPE residuals in the pathological non-complete and complete response groups.