Computer-aided diagnosis(CAD) systems have been proposed to assist radiologists in making diagnostic decisions by providing helpful information. As one of the most important sequences in prostate magnetic resonance im...Computer-aided diagnosis(CAD) systems have been proposed to assist radiologists in making diagnostic decisions by providing helpful information. As one of the most important sequences in prostate magnetic resonance imaging(MRI), image features from T2-weighted images(T2WI) were extracted and evaluated for the diagnostic performances by using CAD. We extracted 12 quantitative image features from prostate T2-weighted MR images. The importance of each feature in cancer identification was compared in the peripheral zone(PZ) and central gland(CG), respectively. The performance of the computer-aided diagnosis system supported by an artificial neural network was tested. With computer-aided analysis of T2-weighted images, many characteristic features with different diagnostic capabilities can be extracted. We discovered most of the features(10/12) had significant difference(P<0.01) between PCa and non-PCa in the PZ, while only five features(sum average, minimum value, standard deviation, 10 th percentile, and entropy) had significant difference in CG. CAD prediction by features from T2 w images can reach high accuracy and specificity while maintaining acceptable sensitivity. The outcome is convictive and helpful in medical diagnosis.展开更多
文摘Computer-aided diagnosis(CAD) systems have been proposed to assist radiologists in making diagnostic decisions by providing helpful information. As one of the most important sequences in prostate magnetic resonance imaging(MRI), image features from T2-weighted images(T2WI) were extracted and evaluated for the diagnostic performances by using CAD. We extracted 12 quantitative image features from prostate T2-weighted MR images. The importance of each feature in cancer identification was compared in the peripheral zone(PZ) and central gland(CG), respectively. The performance of the computer-aided diagnosis system supported by an artificial neural network was tested. With computer-aided analysis of T2-weighted images, many characteristic features with different diagnostic capabilities can be extracted. We discovered most of the features(10/12) had significant difference(P<0.01) between PCa and non-PCa in the PZ, while only five features(sum average, minimum value, standard deviation, 10 th percentile, and entropy) had significant difference in CG. CAD prediction by features from T2 w images can reach high accuracy and specificity while maintaining acceptable sensitivity. The outcome is convictive and helpful in medical diagnosis.
