Objective    To preliminarily explore the value of computer-aided diagnosis (CAD) system based on Bayesian theory in the diagnosis of solitary pulmonary nodules (SPNs) with CT.     Methods    Totally 352 consecutive SPN cases (malignancy n=135

benignity n=217) were collected retrospectively to form the training set. According to Bayesian theory

the prior odds of malignant SPNs and the likelihood ratios of clinical and CT findings were derived from the training set

then these derived va

lues were used to construct a Bayesian theory based CAD system by using VC

++

language. Utilizing this system

the probability of malignancy in each case with SPN was calculated

and SPNs with ≥50% calculated probability were judged as malignancy and those with 50% calculated probability was judged as benignity. This system was also tested prospectively for its diagnostic validation on the test set (malignancy n=61

benignity n=71)

compared with the performance of the two chest radiologists and two radiologic residents using routine diagnostic method.     Results    The Bayesian theory based CAD system was constructed successfully. The sensitivity

specificity

accuracy of this system for the training set were 88.9%

93.1%

91.5%

respectively. On the test set

the sensitivity

specificity

accuracy

positive predictive value

negative predictive value of this system were 88.5%

85.9%

87.1%

84.4% and 89.7%

respectively. The accuracy of this system showed no statistical significance with that made by chest radiologist A (80.3%

χ

2

=2.37

P=0.122) and B (79.5%

χ

2

=3.12

P=0.076)

but was higher than that by radiological residents C (74.2%

χ

2

=7.05

P=0.012) and D (74.2%

χ

2

=6.56

P=0.009).     Conclusion    The Bayesian theory based CAD system can help physicians differentiate benign from malignant SPNs

especially for less experienced physicians. This system can provide a reference in determining the management of SPNs.