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

arXiv: https://arxiv.org/abs/2405.07854 · HTML: https://mlsanity.com/paper/2405.07854/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 13 May 2024.

## Builds on (most cited first, 2 of 2)

- [Med3D: Transfer Learning for 3D Medical Image Analysis](https://mlsanity.com/paper/1904.00625/) (2019)
- [Enhancing Clinical Support for Breast Cancer with Deep Learning Models using Synthetic Correlated Diffusion Imaging](https://mlsanity.com/paper/2211.05308/) (2022)

## Abstract (arXiv)

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 (CDIs), demonstrated significant potential in noninvasive breast cancer pathologic complete response prediction. Inspired by the positive outcomes of optimizing CDIs for prostate cancer delineation, this research investigates the application of optimized CDIs to enhance breast cancer pathologic complete response prediction. Using multiparametric MRI that fuses optimized CDIs 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.