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Abstract #0144

Predicting malignancy in additional lesion in breast cancer: A machine learning approach combining radiomics and clinical imaging analysis

Tien Anh Nguyen1, Hyo Jae Lee2, Luu-Ngoc Do1, Hyo-Soon Lim2,3, and Ilwoo Park3,4,5
1Radiology, Chonnam National University, GWANGJU, Korea, Republic of, 2Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea, Republic of, 3Radiology, Chonnam National University, Gwangju, Korea, Republic of, 4Radiology, Chonnam National University Hospital, GWANGJU, Korea, Republic of, 5Artificial Intelligence Convergence, Chonnam National University, Gwangju, Korea, Republic of

The purpose of this study was to investigate the feasibility of machine learning classifiers combining radiomics and clinical imaging interpretation for predicting malignancy in additional MR-detected enhancing lesions on multiparametric breast MRI. Machine learning algorithms trained with the combination of radiomics features extracted from breast MRI and clinical imaging interpretation what was obtained by an experienced breast radiologist demonstrated the maximal accuracy and AUC of 86.2% and 92.6%, respectively. The results from this study suggest that our approach may provide a noninvasive assisting tool to guide proper management that can reduce the use of unnecessary US or biopsy.

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