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 Singh^1,3

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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