What type of study is this?

This is a Quantitative Study study.

September 17, 2025

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

Key Points

Best-performing stacking model achieved AUCs of 0.82 using radiomic features from two echoes.
MDME-derived radiomic features outperformed BI-RADS with an AUC of 0.67 in malignant lesion distinction.
Machine learning algorithms were developed for analysis of imaging features to differentiate breast lesions.
This method shows potential to simplify scanning protocols and protocol design for breast cancer detection.

Abstract

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 BIRADS, 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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