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September 17, 2025

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

Key Points

A combination of clinicopathologic variables and fractal dimensions predicts pCR with an AUC of 0.832, indicating high accuracy.
The study involved 232 breast cancer patients undergoing neoadjuvant chemotherapy, making it a robust evaluation of treatment response.
Logistic regression and linear mixed-effects models were employed to analyze the relationship between MRI-derived features and treatment outcomes.
This approach highlights the potential of using DCE-MRI morphological complexity as a significant biomarker in personalizing breast cancer treatment.

Abstract

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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