Enhancing Breast Cancer Diagnosis through Deep Learning-Based DWI in Conjunction with Kaiser Score
Wanjun Xia, Yong Zhang, Kaiyu Wang, Jingliang Cheng · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 2024
Motivation: While the Kaiser score serves a pivotal role in diagnosing breast cancer, it still encounters scenarios where false positives necessitate biopsy confirmation. Goal(s): This study aims to investigate approaches to enhance the diagnostic efficacy of the Kaiser score through MRI. Approach: Leveraging deep learning to enhance both the quality of DWI images and diagnosis, we sought more effective indicators in conjunction with the Kaiser score. Results: ADC values derived from DWI images reconstructed using deep learning, with a b-value of 800 s/mm², in tandem with the Kaiser score, significantly enhance the diagnostic performance nearing 1. Impact: Integrating DWI under deep learning with the Kaiser score can elevate the accuracy of differentiating between benign and malignant breast cancers to almost 100%, leading to substantial improvements in breast cancer diagnosis and a reduction in unnecessary biopsies.