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.

优化后的 CDI$^s$ 与扩散加权成像（DWI）融合的多参数 MRI 技术，可对乳腺癌病理完全缓解进行准确预测，交叉验证的准确性达到 93.28%，比之前报道的结果高出 5.5%。

利用多参数 MRI 与优化的合成相关扩散成像提高乳腺癌病理完全缓解预测

Using Multiparametric MRI with Optimized Synthetic Correlated Diffusion  Imaging to Enhance Breast Cancer Pathologic Complete Response Prediction

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%。