A deep learning model based on contrast-enhanced computed tomography for differential diagnosis of gallbladder carcinoma
Fei Xiang a , Qing-Tao Meng b , Jing-Jing Deng b , Jie Wang b , Xiao-Yuan Liang a , Xing-Yu Liu a , Sheng Yan a , ∗
a Department of Hepatobiliary Pancreatic Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
b Department of Radiology, Affiliated Chuzhou First People’s Hospital, Anhui Medical University, Chuzhou 239000, China
∗Corresponding author.
E-mail address: shengyan@zju.edu.cn (S. Yan).
Abstract：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 compared 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.
. [J]. Hepatobiliary Pancreat Dis Int, 2024, 23(4): 376-384.
Xiang F, Meng QT, Deng JJ, Wang J, Liang XY, Liu XY, Yan S. A deep learning model based on contrast-enhanced computed tomography for differential diagnosis of gallbladder carcinoma. Hepatobiliary Pancreat Dis Int, 2024, 23(4): 376-384.
2024, Vol. 23 
