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

Cited 4 time in Web of Science Cited 6 time in Scopus
Authors
Lee, Youngjoo; Kim, Namkug; Cho, Kyoung-Sik; Kang, Suk-Ho; Jung, Yoon Young; Kim, Jeong Kon; Kim, Dae Yoon
Issue Date
2009-08-01
Publisher
AMER ROENTGEN RAY SOC
Citation
AMERICAN JOURNAL OF ROENTGENOLOGY; Vol.193 2; W106-W111
Keywords
artificial intelligenceBayesian predictionmachine learningoncologic imagingrenal cystsMDCTliver disease
Abstract
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
0361-803X
Language
English
URI
http://hdl.handle.net/10371/75347
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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