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Abstract #1561

Based on Multiparameter-MRI radiomic features: differential diagnosis of BI-RADS 4 Breast lesions with DCE-TIC type II

yuanfei li1, lina zhang1, siqi zhao1, yueqi wu1, Moyun Zhang1, and Haonan Guan2
1The First Affiliated Hospital of Dalian Medical University, Dalian, China, 2GE Healthcare, Beijing, China

Synopsis

Keywords: AI Diffusion Models, Breast

Motivation: Accurately differentiating BI-RADS 4 breast lesions is critical in breast cancer diagnostics, yet conventional imaging methods often result in unclear distinctions, leading to unnecessary biopsies.

Goal(s): This study aimed to develop a multi-parameter MRI radiomics model to improve diagnostic accuracy in distinguishing between benign and malignant BI-RADS 4 lesions.

Approach: The model incorporated DWI, ADC, and DCE-MRI sequences, with radiomic features extracted and analyzed using machine learning classifiers, including RF, SVM, and LR.

Results: The model, particularly with the LR classifier, showed high diagnostic accuracy and sensitivity, effectively distinguishing benign from malignant lesions and offering clinical support to reduce unnecessary biopsies.

Impact: This study’s multi-parameter MRI radiomics model enhances diagnostic accuracy for BI-RADS 4 breast lesions, offering radiologists a reliable tool for distinguishing benign from malignant cases, reducing unnecessary biopsies, and improving patient management in breast cancer diagnostics.

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