Ke Nie1, Dustin Newell1, Jeon-Hor Chen1,2, Chieh-Chih Hsu2, Hon J. Yu1, Orhan Nalcioglu1, Min-Ying Lydia Su1
1Tu & Yuen Center for Functional Onco-Imaging, University of California, Irvine, CA, USA; 2Department of Radiology, China Medical University, Taiwan
Diagnostic features to differentiate between malignant and benign lesions presenting as mass and non-mass types were investigated using 116 lesions. The morphology of lesion (shape/margin and enhancement texture) and the enhancement kinetic parameters were obtained, and then a best feature set was selected by artificial neural network for making differential diagnosis. Morphology parameters can diagnose mass type benign and malignant lesions with a high accuracy (AUC=0.87), and adding Ktrans will further improve to 0.90. On the other hand, neither the morphology nor the kinetic parameters analyzed from outlined lesion ROI for non-mass lesions could differentiate between malignant and benign lesions.