Differentiation Between High-Grade Glioma and Single Brain Metastases Based on Three-Dimensional DenseNet / 中国医学影像学杂志

ABSTRACT

Purpose To explore the value of three-dimensions densely connected convolutional networks(3D-DenseNet)in the differential diagnosis of high-grade gliomas(HGGs)and single brain metastases(BMs)via MRI,and to compare the diagnostic performance of models built with different sequences.Materials and Methods T2WI and T1WI contra-enhanced(T1C)imaging data of 230 cases of HGGs and 111 cases of BMs confirmed by surgical pathology in Lanzhou University Second Hospital from June 2016 to June 2021 were retrospectively collected,and the volume of interest under the 3D model was delineated in advance as the input data.All data were randomly divided into a training set(n=254)and a validation set(n=87)in a ratio of 7∶3.Based on the 3D-DenseNet,T2WI,T1C and two sequence fusion prediction models(T2-net,T1C-net and TS-net)were constructed respectively.The predictive efficiency of each model was evaluated and compared by the receiver operating characteristic curve,and the predictive performance of models built with different sequences were compared.Results The area under curve(AUC)of T1C-net,T2-net and TS-net in the training and validation sets were 0.852,0.853,0.802,0.721,0.856 and 0.745,respectively.The AUC and accuracy of the validation set of T1C-net were significantly higher than those of T2-net and TS-net,respectively,and the AUC and accuracy of the validation set of TS-net were significantly higher than those of T2-net.There was a significant difference between T1C-net and T2-net models(P<0.05),while there were no statistical differences between the models of TS-net and T2-net,T1C-net and TS-net(P>0.05).The T1C-net model based on 3D-DenseNet had the best performance,the accuracy of the validation set was 80.5%,the sensitivity was 90.9%,the specificity was 62.5%.Conclusion The 3D-DenseNet model based on MRI conventional sequence has better diagnostic performance,and the model built by T1C-net sequence has better performance in differentiating HGGs and BMs.Deep learning models can be a potential tool to identify HGGs and BMs and to guide the clinical formulation of precise treatment plans.