Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence, Synthetic MR, MDME
Motivation: Lesion characteristics were investigated by MDME MRI-derived tissue-relaxometry, however, radiomics features of MDME generated images in predicting breast lesion malignancy has not been explored much
Goal(s): The aim of this study is to find the best imaging features from MDME generated images in distinguishing malignant lesion and compare its performance with BI-RADS
Approach: ML algorithms were explored and best-performing model using clinical-features, radiomic-features of four saturation-delay and two-echos scanned before and after contrast injection.
Results: Test prediction based on BI-RADS achieved AUCs of 0.67 in contrast best-performing stacking model achieved AUCs of 0.82 using image radiomic-features of two-echoes of 2nd-saturation delay and clinical-features.
Impact: Comparing with BI_RADS, post contrast MDME derived radiomics-based machine learning shows promising potential in differentiating malignant breast lesion. Which may simplify of breast image scanning protocols and pulse-sequence-design for malignancy check.
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