Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging

TL;DR

This study enhances noninvasive breast cancer grading by combining optimized synthetic correlated diffusion imaging with diffusion-weighted MRI, achieving over 95% accuracy using deep learning on a large patient cohort.

Contribution

It introduces an optimized CDI$^s$ technique fused with DWI to improve breast cancer grading accuracy with deep learning, surpassing previous methods.

Findings

Achieved 95.79% accuracy in breast cancer grade prediction.

Improved accuracy by over 8% compared to previous approaches.

Utilized a larger cohort and pretrained MONAI model for robust results.

Abstract

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.