Using Multiparametric MRI with Optimized Synthetic Correlated Diffusion Imaging to Enhance Breast Cancer Pathologic Complete Response Prediction

TL;DR

This study improves breast cancer treatment prediction by combining optimized synthetic correlated diffusion imaging with MRI, achieving over 93% accuracy in predicting complete response to chemotherapy.

Contribution

It introduces an optimized CDI$^s$ technique integrated with multiparametric MRI to enhance noninvasive prediction of breast cancer treatment outcomes.

Findings

Achieved 93.28% accuracy in prediction

Over 5.5% improvement over previous methods

Validated the effectiveness of optimized CDI$^s$ in breast cancer

Abstract

In 2020, 685,000 deaths across the world were attributed to breast cancer, underscoring the critical need for innovative and effective breast cancer treatment. Neoadjuvant chemotherapy has recently gained popularity as a promising treatment strategy for breast cancer, attributed to its efficacy in shrinking large tumors and leading to pathologic complete response. However, the current process to recommend neoadjuvant chemotherapy relies on the subjective evaluation of medical experts which contain inherent biases and significant uncertainty. A recent study, utilizing volumetric deep radiomic features extracted from synthetic correlated diffusion imaging (CDI$^s$), demonstrated significant potential in noninvasive breast cancer pathologic complete response prediction. Inspired by the positive outcomes of optimizing CDI$^s$ for prostate cancer delineation, this research investigates the application of optimized CDI$^s$ to enhance breast cancer pathologic complete response prediction. Using multiparametric MRI that fuses optimized CDI$^s$ with diffusion-weighted imaging (DWI), we obtain a leave-one-out cross-validation accuracy of 93.28%, over 5.5% higher than that previously reported.