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Abstract #4882

To evaluate the role of machine learning for characterization of breast lesion using multi-parametric MRI.

Snekha Thakran1, Dinil Sasi S1, Rupsa Bhattacharjee1, Ayan Debnath1, Rakesh Kumar Gupta2, and Anup Singh1,3

1Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India, 2Department of Radiology, Fortis Memorial Research Institute, Haryana, Gurgaon, India, 3Department of Biomedical Engineering, All India Institute of Medical Science, New Delhi, India

The role of machine learning in medical imaging is increasing day by day. It can help in combining a variety of complementary information obtained using multi-parametric MRI(mpMRI). The objective of this study was to differentiate benign vs. malignant breast tumor using machine learning with optimized feature set obtained from mpMRI data. The study included mpMRI data of 49 patients with breast cancer. Quantitative mpMRI parameters as well as texture features were used as feature set in machine learning. The combination of the wrapper method with SVM resulted in high sensitivity (100%) and specificity (93.75%) in the binary classification of benign and malignant.

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