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Bayesian Classifier for Predicting Malignant Renal Cysts on MDCT: Early Clinical Experience

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Authors
Lee, Youngjoo; Kim, Namkug; Cho, Kyoung-Sik; Kang, Suk-Ho; Kim, Dae Yoon; Jung, Yoon Young; Kim, Jeong Kon
Issue Date
2009-01
Publisher
American Roentgen Ray Society
Citation
AJR 2009; 193:W106-W111
Keywords
artificial intelligenceBayesian predictionliver diseasemachine learningMDCToncologic imagingrenal cysts
Abstract
OBJECTIVE. The objective of our study was to evaluate the feasibility and usefulness of
the Bayesian classifier for predicting malignant renal cysts on MDCT.
MATERIALS AND METHODS. Ninety-three complicated cysts with pathologic confirmation
were enrolled. Patient age and sex and seven morphologic features of the cysts including
the maximum diameter, wall features, wall thickness, septa features, measurable enhancement
of the wall and septa, presence of calcification, and presence of an enhancing
soft-tissue component were used to train the Bayesian classifier. Four radiologists independently
reviewed the MDCT images, and the probability of malignancy in each cyst was rated
by the radiologists and the Bayesian classifier. The diagnostic performances of the radiologists’
visual decisions and the Bayesian classifier were then compared using receiver operating
characteristic (ROC) curve analysis. The sensitivity and specificity were also compared
between the visual decisions and the Bayesian classifier.
RESULTS. The area under the ROC curve for predicting malignant renal cysts by the
Bayesian classifier was greater than the visual decisions of three readers (reader 1, p = 0.02;
reader 2, p < 0.01; reader 4, p = 0.02) and was similar to the visual decision of one reader
(reader 3, p = 0.51). The specificity for predicting malignant renal cysts was greater by the
Bayesian classifier than by the visual decisions in readers 2 (p = 0.04) and 4 (p = 0.02) and
was similar in readers 1 (p = 0.68) and 3 (p = 1.00). In terms of sensitivity, there was no significant
difference between the Bayesian classifier and the visual decisions in all four readers
(p > 0.05).
CONCLUSION. For predicting malignant renal cysts on MDCT, the Bayesian classifier
is feasible and may improve diagnostic performance.
ISSN
1546-3141 (online)
0361-803X (print)
Language
English
URI
http://hdl.handle.net/10371/7640
DOI
https://doi.org/10.2214/AJR.08.1858
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Industrial Engineering (산업공학과)Journal Papers (저널논문_산업공학과)
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