Impact of neoadjuvant chemotherapy on breast tissue density and its correlation with pathologic response: a retrospective artificial intelligence-enhanced radiological study.

OBJECTIVES: This study evaluates the impact of neoadjuvant chemotherapy (NAT) on breast tissue density and examines the correlation between density changes and pathological response, while also assessing inter-reader agreement among radiologists and artificial intelligence (AI) tools. METHODS: The study included 135 women with triple-negative and/or HER2+ invasive ductal breast cancer who underwent NAT. Digital mammography was performed pre- and post-NAT, with evaluations by radiologists of varying experience levels and comparison using an in-house developed AI tool based on AlexNet CNN architecture. Statistical analyses included Wilcoxon signed-rank test, linear regression, McNemar test, Spearman's rank correlation, and intraclass correlation coefficients (ICC). RESULTS: Post-NAT assessments showed a significant decrease in breast tissue density, with initial heterogeneously dense and extremely dense breasts decreasing from 32% and 15% to 18% and 7%, respectively (P < .003). Significant relationships were found between density reduction and age, menopausal status, and baseline density, and the reduction correlated moderately with pathological response to NAT. Intraclass correlation coefficient values reflected good agreement among experienced radiologists (0.68), fair agreement between expert and resident (0.55), poor agreement between expert and non-radiologist (0.39), and substantial agreement between the expert and AI tool (0.78). CONCLUSIONS: Neoadjuvant chemotherapy significantly reduces breast tissue density, correlating with pathological response, and AI tools provide consistent density evaluations. Artificial intelligence tools show promise in improving assessment consistency, with substantial agreement with expert radiologists. The findings highlight the need for standardized protocols and training to optimize breast density evaluations and suggest further refinement and validation of AI tools for clinical use. ADVANCES IN KNOWLEDGE: This study evaluates the impact of NAT on breast tissue density while investigating its correlation with pathological response and inter-reader agreement among radiologists and AI tools. It contributes to the field by demonstrating the potential of AI to improve consistency in density evaluations and emphasizing the importance of standardized protocols and training for optimized clinical assessments.