Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917664381730816 |
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| author | Tai, Chi-en Amy Wong, Alexander |
| author_facet | Tai, Chi-en Amy Wong, Alexander |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07861 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging Tai, Chi-en Amy Wong, Alexander Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.07861 |