Optimizing Synthetic Correlated Diffusion Imaging for Breast Cancer Tumour Delineation

TL;DR

This study enhances breast cancer tumor delineation by optimizing synthetic correlated diffusion imaging parameters, resulting in improved diagnostic accuracy over standard MRI methods.

Contribution

The paper introduces a novel optimization approach for tuning CDI$^s$ parameters specifically for breast cancer, improving tumor delineation accuracy.

Findings

Optimized CDI$^s$ achieves higher AUC than unoptimized version.

Optimized CDI$^s$ outperforms gold-standard MRI by 0.0044 AUC.

Parameter tuning significantly improves CDI$^s$ performance.

Abstract

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 (CDI$^s$) 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 CDI$^s$ 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 CDI$^s$ - Optimized modality, outperforming the best gold-standard modality by 0.0044. Notably, the optimized CDI$^s$ modality also achieves AUC values over 0.02 higher than the Unoptimized CDI$^s$ value, demonstrating the importance of optimizing the CDI$^s$ exponents for the specific cancer application.