Abstract #4114

T2-Weighted MRI-Derived Texture Features in Characterization of Prostate Cancer

Dharmesh Singh¹, Virendra Kumar², Chandan J Das³, Anup Singh¹, and Amit Mehndiratta¹

¹Centre for Biomedical Engineering (CBME), Indian Institute of Technology (IIT) Delhi, New Delhi, India, ²Department of NMR, All India Institute of Medical Sciences (AIIMS) Delhi, New Delhi, India, ³Department of Radiology, All India Institute of Medical Sciences (AIIMS) Delhi, New Delhi, India

Automatic grading of prostate cancer (PCa) can play a major role in its early diagnosis, which has a significant impact on patient survival rates. The objective of this study was to develop and validate a framework for classification of PCa grades using texture features of T2-weighted MR images. Evaluation of classification result shows accuracy of 85.10 ± 2.43% using random forest feature selection and Gaussian support-vector machine classifier.

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