Using Multiparametric MRI with Optimized Synthetic Correlated Diffusion Imaging to Enhance Breast Cancer Pathologic Complete Response Prediction
This work addresses the need for more objective and accurate prediction of treatment response in breast cancer patients, potentially reducing reliance on subjective expert evaluations, but it is incremental as it builds on prior optimization techniques from prostate cancer.
The paper tackled the problem of predicting breast cancer pathologic complete response to neoadjuvant chemotherapy by using multiparametric MRI with optimized synthetic correlated diffusion imaging, achieving a leave-one-out cross-validation accuracy of 93.28%, which is over 5.5% higher than previous methods.
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.