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

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

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
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.

Gallbladder carcinomaComputed tomographyDeep learningRadiomics

Fei Xiang、Qing-Tao Meng、Jing-Jing Deng、Jie Wang、Xiao-Yuan Liang、Xing-Yu Liu、Sheng Yan

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Department of Hepatobiliary Pancreatic Surgery,Second Affiliated Hospital,Zhejiang University School of Medicine,Hangzhou 310003,China

Department of Radiology,Affiliated Chuzhou First People's Hospital,Anhui Medical University,Chuzhou 239000,China

National Natural Science Foundation of ChinaKey Research and Development Project of Science and Technology Department of ZhejiangChen Xiao-Ping Foundation for the Development of Science and Technology of Hubei ProvinceZhejiang Provincial Program for the Cultivation of Highlevel Innovative Health Talents

815729752015C03053CXPJJH11900009-07

2024

10.1016/j.hbpd.2023.04.001

国际肝胆胰疾病杂志(英文版)
浙江省医学学术交流管理中心,浙江大学医学院附属第一医院,浙江大学出版社有限责任公司

国际肝胆胰疾病杂志(英文版)

影响因子：0.668
ISSN：1499-3872
年,卷(期)：2024.23(4)