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.

通过使用 Nelder-Mead simplex 优化策略，本研究探索了调整计算中的相关扩散成像（CDI$^s$）系数以实现乳腺癌肿瘤识别，显示出最佳的受试者工作特性曲线下面积（AUC），优于黄金标准模态 0.0044，同时查明优化后的 CDI$^s$ 模态比未优化的 CDI$^s$ 模态具有高于 0.02 的 AUC 值，彰显了为特定肿瘤应用优化 CDI$^s$ 指数的重要性。