COMPARATIVE STUDY
JOURNAL ARTICLE

Computer-Aided Diagnosis for Distinguishing Pancreatic Mucinous Cystic Neoplasms From Serous Oligocystic Adenomas in Spectral CT Images

Chao Li, Xiaozhu Lin, Chun Hui, Kin Man Lam, Su Zhang
Technology in Cancer Research & Treatment 2016, 15 (1): 44-54
25520271

OBJECTIVE: This preliminary study aims to verify the effectiveness of the additional information provided by spectral computed tomography (CT) with the proposed computer-aided diagnosis (CAD) scheme to differentiate pancreatic serous oligocystic adenomas (SOAs) from mucinous cystic neoplasms of pancreas cystic lesions.

MATERIALS AND METHODS: This study was conducted from January 2010 to October 2013. Twenty-three patients (5 men and 18 women; mean age, 43.96 years old) with SOA and 19 patients (3 men and 16 women; 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 CAD 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 CAD scheme were tumor size, contour, location, and low-energy CT values (43 keV). Iodine-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 long axis, short axis, 40 keV monochromatic CT value in AP, iodine (water) density in AP, 43 keV monochromatic CT value in PP, and iodine (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 CAD scheme. The quantitative information of spectral CT may prove useful in the diagnosis and classification of SOAs and MCNs with machine learning algorithms.

Full Text Links

Find Full Text Links for this Article

Discussion

You are not logged in. Sign Up or Log In to join the discussion.

Related Papers

Remove bar
Read by QxMD icon Read
25520271
×

Save your favorite articles in one place with a free QxMD account.

×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"