Optimizing Synthetic Correlated Diffusion Imaging for Breast Cancer Tumour Delineation

Breast cancer is a significant cause of death from cancer in women globally, highlighting the need for improved diagnostic imaging to enhance patient outcomes. Accurate tumour identification is essential for diagnosis, treatment, and monitoring, emphasizing the importance of advanced imaging technologies that provide detailed views of tumour characteristics and disease. Synthetic correlated diffusion imaging (CDIˢ) is a recent method that has shown promise for prostate cancer delineation compared to current MRI images. In this paper, we explore tuning the coefficients in the computation of CDIˢ for breast cancer tumour delineation by maximizing the area under the receiver operating characteristic curve (AUC) using a Nelder-Mead simplex optimization strategy. We show that the best AUC is achieved by the CDIˢ - Optimized modality, outperforming the best gold-standard modality by 0. 0044. Notably, the optimized CDIˢ modality also achieves AUC values over 0. 02 higher than the Unoptimized CDIˢ value, demonstrating the importance of optimizing the CDIˢ exponents for the specific cancer application.