PURPOSE: To develop and test a computer-aided diagnosis (CAD) system to improve the performance of radiologists in classifying lesions on breast MRI (BMRI). MATERIALS AND METHODS: A CAD system was developed that uses a semiautomated segmentation method. After segmentation, 42 features based on lesion shape, texture, and enhancement kinetics were computed, and the 13 best features were selected and used as inputs to a backpropagation neural network (BNN). The BNN was trained and tested using the leave-one-out method on 80 BMRI lesions (37 benign, 43 malignant). Lesion histopathology was used as the reference standard. Five human readers classified the 80 lesions first without and then with CAD assistance. The performance of the computer classifier and the human readers was assessed using receiver operating characteristic curves; the performance of the human readers was also evaluated using multireader multicase (MRMC) analysis. RESULTS: The performance of the human readers significantly improved when aided by the CAD system (P < 0.05). MRMC analysis showed that human reader performance with and without CAD system assistance can be generalized to the population of cases (P < 0.001). CONCLUSION: A CAD system based on lesion morphology and enhancement kinetics can improve the performance of human readers in classifying lesions on breast MRI.
PURPOSE: To develop and test a computer-aided diagnosis (CAD) system to improve the performance of radiologists in classifying lesions on breast MRI (BMRI). MATERIALS AND METHODS: A CAD system was developed that uses a semiautomated segmentation method. After segmentation, 42 features based on lesion shape, texture, and enhancement kinetics were computed, and the 13 best features were selected and used as inputs to a backpropagation neural network (BNN). The BNN was trained and tested using the leave-one-out method on 80 BMRI lesions (37 benign, 43 malignant). Lesion histopathology was used as the reference standard. Five human readers classified the 80 lesions first without and then with CAD assistance. The performance of the computer classifier and the human readers was assessed using receiver operating characteristic curves; the performance of the human readers was also evaluated using multireader multicase (MRMC) analysis. RESULTS: The performance of the human readers significantly improved when aided by the CAD system (P < 0.05). MRMC analysis showed that human reader performance with and without CAD system assistance can be generalized to the population of cases (P < 0.001). CONCLUSION: A CAD system based on lesion morphology and enhancement kinetics can improve the performance of human readers in classifying lesions on breast MRI.
Authors: Christian D Klose; Alexander D Klose; Uwe J Netz; Alexander K Scheel; Jurgen Beuthan; Andreas H Hielscher Journal: J Biomed Opt Date: 2010 Nov-Dec Impact factor: 3.170
Authors: Ludguier D Montejo; Jingfei Jia; Hyun K Kim; Uwe J Netz; Sabine Blaschke; Gerhard A Müller; Andreas H Hielscher Journal: J Biomed Opt Date: 2013-07 Impact factor: 3.170