Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from XXXX at Multiparametric MR Imaging.

@entity1 @entity32 @entity263 @entity66 @entity5 @entity1067 @entity194 @entity5436
UNASSIGNED: Purpose To determine the best features to discriminate @entity263 from benign disease and its relationship to benign disease class and @entity5 grade. Materials and Methods The institutional review board approved this study and waived the need for informed consent. A retrospective cohort of 70 @entity1 (age range, 48-70 years; median, 62 years), all of whom were scheduled to undergo radical prostatectomy and underwent preoperative 3-T multiparametric magnetic resonance (MR) imaging, including T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced imaging, were included. The digitized prostatectomy slides were annotated for @entity5 and @entity66 and coregistered to MR imaging with an interactive deformable coregistration scheme. Computer-identified features for each of the @entity66 categories (eg, @entity1067 [ @entity1067 ], @entity5436 [ @entity5436 ], @entity32 , and @entity194 ) and @entity263 were extracted. Feature selection was performed to identify the features with the highest discriminatory power. The performance of these five features was evaluated by using the area under the receiver operating characteristic curve (AUC). Results High-b-value diffusion-weighted images were more discriminative in distinguishing @entity1067 from @entity263 than apparent diffusion coefficient, which was most suitable for distinguishing @entity5436 from @entity263 . The focal appearance of lesions on dynamic contrast-enhanced images may help discriminate @entity194 and @entity32 from @entity5 . Which imaging features are discriminative for different benign lesions is influenced by @entity5 grade. The apparent diffusion coefficient appeared to be the most discriminative feature in identifying high-grade @entity5 . Classification results showed increased performance by taking into account specific benign types (AUC = 0.70) compared with grouping all noncancerous findings together (AUC = 0.62). Conclusion The best features with @entity263 from noncancerous benign disease depend on the type of benign disease and @entity5 grade. Use of the best features may result in better diagnostic performance. (  ) RSNA, 2015 Online supplemental material is available for this article.
