Value of deep learning technology for the differential diagnosis of endoscopic ultrasonography images of gastrointestinal stromal tumors and leiomyomas
10.3760/cma.j.cn321463-20231210-00629
- VernacularTitle:深度学习技术对胃肠道间质瘤与平滑肌瘤超声内镜图像的鉴别诊断价值
- Author:
Kangli GUO
1
;
Jianwei ZHU
;
Zhanghao HUANG
;
Chunping LIU
;
Duanmin HU
Author Information
1. 湖北文理学院附属医院 襄阳市中心医院消化二科，襄阳 441021
- Keywords:
Artificial intelligence;
Deep learning technology;
Endoscopic ultrasonography;
Gastrointestinal stromal tumors;
Leiomyomas
- From:
Chinese Journal of Digestive Endoscopy
2024;41(6):449-454
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a classification model for endoscopic ultrasonography (EUS) images of gastrointestinal stromal tumors (GISTs) and leiomyomas (LM) based on deep learning technology, and to verify its value for differential diagnosis.Methods:From October 2014 to October 2021, 69 patients of GISTs and 73 of LM who underwent EUS and were pathologically confirmed by surgery or endoscopic resection in the Second Affiliated Hospital of Soochow University were retrospectively studied. One clear EUS image with typical lesion was selected for each case. Using the hold-out method, the images of each disease were divided into the training set and the validation set according to the ratio of the number of images in the training set to the number of images in the validation set, which was 8∶2. Finally, 113 EUS images (55 GISTs and 58 LM) were used to form the training set, and 29 EUS images (14 GISTs and 15 LM) were used to form the validation set. The training set was used to train and optimize the deep learning model, and the validation set was used to verify the classification model. The main observation indicators included the sensitivity, the specificity, the positive predictive value, the negative predictive value and the accuracy of differential diagnosis.Results:The accuracy of the classification model established by Resnet 34 network structure in the differential diagnosis of GISTs and LM tended to be 0.89, better than the classification model established by Resnet 50 network structure (0.81). The sensitivity, the specificity, the positive predictive value, the negative predictive value and the accuracy of the classification model based on Resnet 34 network structure for differentiating EUS images in the validation set were 85.71% (12/14, 95% CI: 67.38%-100.00%), 93.33% (14/15, 95% CI: 80.71%-100.00%), 92.31% (12/13, 95% CI: 77.82%-100.00%), 87.50% (14/16, 95% CI: 71.30%-100.00%) and 89.66% (26/29, 95% CI: 78.57%-100.00%), respectively. Conclusion:It is feasible to use deep learning technology in the differential diagnosis of EUS images of GISTs and LM, which can provide auxiliary diagnostic opinions for clinicians. The deep learning model based on Resnet 34 network structure shows higher accuracy in the differential diagnosis of EUS images of GISTs and LM.
