Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging
<|reference_start|>Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging: 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.<|reference_end|>
@article{tai2024improving,
    title={Improving Breast Cancer Grade Prediction with Multiparametric MRI
  Created Using Optimized Synthetic Correlated Diffusion Imaging},
    author={Chi-en Amy Tai, Alexander Wong},
    journal={arXiv preprint arXiv:2405.07861},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07861},
    primaryClass={eess.IV cs.CV}
}