A deep learning model based on contrast-enhanced computed tomography for differential diagnosis of gallbladder carcinomaOA

Background:Gallbladder carcinoma(GBC)is highly malignant,and its early diagnosis remains difficult.This study aimed to develop a deep learning model based on contrast-enhanced computed tomography(CT)images to assist radiologists in identifying GBC. Methods:We retrospectively enrolled 278 patients with gallbladder lesions(＞10 mm)who underwent contrast-enhanced CT and cholecystectomy and divided them into the training(n=194)and validation(n=84)datasets.The deep learning model was developed based on ResNet50 network.Radiomics and clinical models were built based on support vector machine(SVM)method.We comprehensively com-pared the performance of deep learning,radiomics,clinical models,and three radiologists. Results:Three radiomics features including LoG_3.0 gray-level size zone matrix zone variance,HHL first-order kurtosis,and LHL gray-level co-occurrence matrix dependence variance were significantly different between benign gallbladder lesions and GBC,and were selected for developing radiomics model.Multi-variate regression analysis revealed that age ≥ 65 years[odds ratios(OR)=4.4,95％confidence inter-val(CI):2.1-9.1,P＜0.001],lesion size(OR=2.6,95％CI:1.6-4.1,P＜0.001),and CA-19-9＞37 U/mL(OR=4.0,95％CI:1.6-10.0,P=0.003)were significant clinical risk factors of GBC.The deep learning model achieved the area under the receiver operating characteristic curve(AUC)values of 0.864(95％CI:0.814-0.915)and 0.857(95％CI:0.773-0.942)in the training and validation datasets,which were compa-rable with radiomics,clinical models and three radiologists.The sensitivity of deep learning model was the highest both in the training[90％(95％CI:82％-96％)]and validation[85％(95％CI:68％-95％)]datasets. Conclusions:The deep learning model may be a useful tool for radiologists to distinguish between GBC and benign gallbladder lesions.