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Abstract #1554

Assessment of machine learning model performance to differentiate benign and malignant breast lesion: Finding best radiomic features on MDME MRI

Hasnine HAQUE1,2, Takuya Matsuda2, Megumi Matsuda2, Shotaro Fuchibe1,2, and Teruhito Kido2
1GE HealthCare, Tokyo, Japan, 2Ehime University School of Medicine, Matsuyama, Japan

Synopsis

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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Keywords