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This is a Quantitative Study study.

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

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The model achieved high diagnostic accuracy, effectively distinguishing benign from malignant lesions.
Using the LR classifier, the model demonstrated enhanced sensitivity in differentiating BI-RADS 4 breast lesions.
Machine learning techniques were employed, incorporating DWI, ADC, and DCE-MRI sequences for better outcomes.
This approach may significantly reduce unnecessary biopsies, thus improving patient care in breast cancer diagnostics.

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Analysis reveals high diagnostic accuracy for distinguishing benign and malignant BI-RADS 4 lesions, suggesting improved patient management.

li et al. (2025) studied this question.

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Analysis reveals high diagnostic accuracy for distinguishing benign and malignant BI-RADS 4 lesions, suggesting improved patient management.