A two-center study of a deep learning model based on enhanced CT to predict Ki-67 expression in gastrointestinal stromal tumors
LI Gen
LIU Kun
YU Haiyun
LIU Meng
YIN Xiaoping
LIU Yang
JI Qian
Abstract：Objective To investigate the value of a deep learning model based on enhanced CT in preoperative noninvasive prediction of Ki-67 expression in gastrointestinal stromal tumor(GIST).Methods The clinical and imaging data of 262 patients(140 males and 122 females,mean age 55.82±10.13 years)with GIST confirmed by surgery and pathology were retrospectively collected.They underwent postoperative immunopathological staining in two hospitals.A total of 190 patients collected from one hospital were randomly divided into training group(133 cases)and internal validation group(57 cases)using a 7∶3 ratio,while 72 patients collected from another hospital were used as external validation group.Five basic networks(Resnet34,Resnet50,Densenet,Efficientnet,and Efficientnetv2)were employed to train and optimize the model.Area under the receiver operating characteristics curves(AUC),accuracy,specificity,and sensitivity were used to assess the predictive power of the model.The differences in AUC values between models were compared using the DeLong test to obtain the best network model.A gradient-class weighted activation mapping(Grad-CAM)visualization method was applied to generate heatmap of attention on original CT images.Results All models,using a learning rate(Lr)of 0.000 5,outperformed Lr=0.000 1,exhibiting better AUC values in the training,internal validation,and external validation groups.The Densenet(Lr=0.000 5)model demonstrated superior predictive efficacy and accuracy for Ki-67 expression in the training,internal validation,and external validation groups,with AUCs of 0.983,0.930,and 0.925,and predictive accuracies of 92.77%,88.14%,and 87.77%,respectively.The Efficientnet model had the best predictive sensitivity and the Efficientnetv2 model had the highest predictive specificity but lower accurate than the other models.The attention heatmap showed that the model can accurately identify the tumor region within the rectangular region of interest(ROI)and provided a reasonable explanation of decision logic.Conclusions The deep learning model based on enhanced CT has good stability and diagnostic efficacy,and is a potential method for noninvasive prediction of Ki-67 expression in GIST.
Keywords：TomographyX-ray computedDeep learningGastrointestinal stromal tumorKi-67
Publication Date：2024-03-15
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 172-177 )