Effectiveness research of opportunistic screening for osteoporosis based on chest CT and deep convolutional neural network / 实用放射学杂志

ABSTRACT

Objective To analyze the feasibility and efficacy of a deep convolutional neural network(DCNN)model based on chest CT images to evaluate bone mineral density(BMD).Methods A total of 1 048 health check subjects'2 096 central level images of lumbar 1 and 2 vertebral bodies were used for experiments and analysis in this retrospective study.According to the results of quanti-tative computed tomography(QCT)BMD measurement,the subjects were divided into three categories:normal,osteopenia,osteopo-rosis(OP).Herein,a DCNN segmentation model was constructed based on chest CT images[training set(n=1 096),tuning set(n=200),and test set(n=800)],the segmentation performance was evaluated using the Dice similarity coefficient(DSC)to com-pare the consistency with the manually sketched region of vertebral body.Then,the DCNN classification models 1(fusion feature construction of lumbar 1 and 2 vertebral bodies)and model 2(image feature construction of lumbar 1 alone)was developed based on the training set(n=530).Model performance was compared in a test set(n=418)by the receiver operating characteristic(ROC)curve analysis.Results When the number of images in the training set(n=300)was adopted,the DSC value was 0.950 in the test set.The results showed that the sensitivity,specificity and area under the curve(AUC)of model 1 and model 2 in diagno-sing osteopenia and OP were 0.716,0.960,0.952;0.941,0.948,0.980;0.638,0.954,0.940;0.843,0.959,0.978,respectively.The AUC value of normal model 1 was higher than that of model 2(0.990 vs 0.983,P=0.033),while there was no significant difference in AUC values between osteopenia and OP(P=0.210,0.546).Conclusion A DCNN may have the potential to evaluate bone mass based on chest CT images,which is expected to become an effective tool for OP screening.