Computer-Aided Diagnosis for Distinguishing Pancreatic Mucinous Cystic Neoplasms From XXXX in Spectral CT Images.

@entity1 @entity1496 @entity851 @entity5409 @entity146 @entity5 @entity1162 @entity652 @entity36915
OBJECTIVE: This preliminary study aims to verify the effectiveness of the additional information provided by spectral computed tomography (CT) with the proposed @entity146 ( @entity146 ) @entity36915 ( @entity652 ) from mucinous @entity5 of @entity5409 . MATERIALS AND METHODS: This study was conducted from January 2010 to October 2013. Twenty-three @entity1 (5 @entity1 and 18 @entity1 ; mean age, 43.96 years old) with SOA and 19 @entity1 (3 @entity1 and 16 @entity1 ; mean age, 41.74 years old) with MCN were included in this retrospective study. Two types of features were collected by dual-energy spectral CT imaging as follows: conventional and additional quantitative spectral CT features. Classification results of the @entity146 scheme were compared using the conventional features and full feature data set. Important features were selected using support vector machine classification method combined with feature-selection technique. The optimal cutoff values of selected features were determined through receiver-operating characteristic curve analyses. RESULTS: Combining conventional features with additional spectral CT features improved the overall accuracy from 88.37% to 93.02%. The selected features of the proposed @entity146 scheme were @entity5 size, contour, location, and low-energy CT values (43 keV). @entity1496 -water basis material pair densities in both arterial phase (AP) and portal venous phase (PP) were important factors for differential diagnosis of SOA and MCN. The optimal cutoff values of @entity1162 , @entity851 , 40 keV monochromatic CT value in AP, @entity1496 (water) density in AP, 43 keV monochromatic CT value in PP, and @entity1496 (water) density in PP were 3.4 mm, 3.1 mm, 35.7 Hu, 0.32533 mg/mL, 39.4 Hu, and 0.348 mg/mL, respectively. CONCLUSION: The combination of conventional features and additional information provided by dual-energy spectral CT shows a high accuracy in the @entity146 scheme. The quantitative information of spectral CT may prove useful in the diagnosis and classification of @entity652 and MCNs with machine learning algorithms.
