Evaluation of a neural network classifier for pancreatic masses based on CT findings.
We have investigated a neural network classifier based on CT findings extracted by a radiologist for the differential diagnosis between the pancreatic ductal adenocarcinoma and mass-forming pancreatitis, and compared its classification performance with that of Bayesian analysis, Hayashi's quantification method II, and radiologists. The three computerized classification methods were designed to classify categorized CT findings extracted by a radiologist, and were trained and tested on 71 cases. There was comparable performance between the neural the network, the Bayesian analysis, Hayashi's quantification method II, and the radiologists, in classifying pancreatic carcinoma and inflammatory mass.
Ikeda
M
M
Department of Medical Information and Medical Records, Nagoya University Hospital, Japan.
Ito
S
S
Ishigaki
T
T
Yamauchi
K
K
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000704:Analysis of Variance; D001499:Bayes Theorem; D018270:Carcinoma, Ductal, Breast / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010190:Pancreatic Neoplasms / Q000000981:diagnostic imaging*; D010195:Pancreatitis / Q000000981:diagnostic imaging*; D012372:ROC Curve; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed