Breast cancer was diagnosed for over 7.8 million women between 2015 to 2020.
Grading plays a vital role in breast cancer treatment planning. However, the
current tumor grading method involves extracting tissue from patients, leading
to stress, discomfort, and high medical costs. A recent paper leveraging
volumetric deep radiomic features from synthetic correlated diffusion imaging
(CDI$^s$) for breast cancer grade prediction showed immense promise for
noninvasive methods for grading. Motivated by the impact of CDI$^s$
optimization for prostate cancer delineation, this paper examines using
optimized CDI$^s$ to improve breast cancer grade prediction. We fuse the
optimized CDI$^s$ signal with diffusion-weighted imaging (DWI) to create a
multiparametric MRI for each patient. Using a larger patient cohort and
training across all the layers of a pretrained MONAI model, we achieve a
leave-one-out cross-validation accuracy of 95.79%, over 8% higher compared to
that previously reported.

利用体积深度放射特征从合成相关扩散成像（CDI$^s$）预测乳腺癌分级的最新研究结果表明，非侵入性方法在乳腺癌分级中有巨大潜力，该论文通过使用优化的 CDI$^s$ 改善乳腺癌分级来受到了启发。将优化后的 CDI$^s$ 信号与扩散加权成像（DWI）融合，为每位患者创建多参数 MRI，在更大的患者队列上进行训练，通过在预训练的 MONAI 模型的所有层进行训练，我们实现了 95.79% 的留一交叉验证准确性，比先前报道的高出 8%。