Development and validation of a feature visualization prediction system for invasion depth of superficial esophageal squamous cell carcinoma
10.3760/cma.j.cn321463-20240201-00020
- VernacularTitle:特征可视化浅表食管鳞状细胞癌浸润深度预测系统的构建及验证
- Author:
Renquan LUO
1
;
Lihui ZHANG
;
Chaijie LUO
;
Honggang YU
Author Information
1. 武汉大学人民医院消化内科，武汉 430060
- Keywords:
Artificial intelligence;
Deep learning;
Superficial esophageal squamous cell carcinoma;
Endoscopic diagnosis;
Depth of infiltration
- From:
Chinese Journal of Digestive Endoscopy
2024;41(10):774-781
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a feature visualization system utilizing deep learning for superficial esophageal squamous cell carcinoma (SESCC) under magnifying endoscopy with narrow band imaging (ME-NBI) to predict the infiltration depth of SESCC.Methods:The feature visualization system consisted of four models: two for segmenting the intrapapillary capillary loops (IPCL) area and avascular area (AVA) in ME-NBI images of SESCC lesions (models 1 and 2, respectively), one for obtaining the principal component of color (PCC) in ME-NBI images of SESCC lesions (model 3), and another for automatically predicting the depth of SESCC infiltration based on the features extracted from the first three models (model 4). A total of 2 341 ME-NBI images of SESCC lesions from April 2016 to October 2021 were used to develop the feature visualization system, which was divided into 3 datasets: dataset 1 (1 077 ME-NBI images) was used to train and test models 1-3, dataset 2 (1 069 ME-NBI images) was expanded by 20 times through feature combination to generate 21 380 feature synthetic images to train and test model 4, and dataset 3 (195 ME-NBI images), containing 146 ME-NBI images with lesion invasion depth from the epithelium to the upper 1/3 of the submucosa (EP-SM1), and 49 ME-NBI images with lesion invasion depth from the middle 1/3 to the lower 1/3 of the submucosa (SM2-SM3), was used to validate the diagnostic performance of the feature visualization system in predicting the invasion depth of SESCC (EP-SM1/SM2-SM3). In order to evaluate the superiority of the feature visualization system, the prediction results of dataset 3 of the traditional deep learning system (trained directly with ME-NBI images), single-item feature models (single-item IPCL feature model, single-item AVA feature model and single-item PCC feature model) were compared with the prediction results of the feature visualization system. In order to evaluate the clinical utility of the feature visualization system, 4 expert physicians (with more than 10 years of endoscopic operation, expert physician group) and 5 senior physicians (with more than 5 years of endoscopic operation, senior physician group) were invited to participate in the human-computer competition to diagnose dataset 3, and the results were compared with the feature visualization system.Results:The accuracy, sensitivity and specificity of the feature visualization system in predicting the invasion depth of SESCC (EP-SM1/SM2-SM3) were 83.08% (162/195), 82.88% (121/146) and 83.67% (41/49), respectively. The above indicators were 60.00% (117/195), 52.05% (76/146) and 83.67% (41/49) for the traditional deep learning system, 74.87% (146/195), 75.34% (110/146) and 73.47% (36/49) for the single IPCL feature model, 58.97% (115/195), 60.27% (88/146) and 55.10% (27/49) for single AVA feature model, 71.28% (139/195), 71.23% (104/146) and 71.43% (35/49) for single PCC feature model, respectively. The results were 66.67%, 78.22% and 32.24% in senior physician group, and 72.31%, 85.96% and 31.63% in expert physician group, respectively. The accuracy of the feature visualization system in predicting the invasion depth of SESCC was significantly higher than that of the other 6 groups ( P<0.05). The sensitivity of feature visualization system was slightly higher than that of senior physician group ( χ2=1.59, P=0.21) and single-item IPCL feature model ( χ2=2.51, P=0.11), slightly lower than that of expert physician group ( χ2=0.89, P=0.35), and significantly higher than that of three other groups ( P<0.05). The specificity of the feature visualization system was similar to the traditional deep learning system ( χ2=0.00, P=1.00), slightly higher than that of single-item IPCL feature model ( χ2=1.52, P=0.22) and single-item PCC feature model (χ2=2.11, P=0.15), and significantly higher than that of the single AVA feature model ( χ2=9.42, P<0.01), senior physician group ( χ2=44.71, P<0.01) and expert physician group ( χ2=43.57, P<0.01). Conclusion:The developed deep learning-based feature visualization system using ME-NBI shows excellent diagnostic performance in predicting the infiltration depth of SESCC (EP-SM1/SM2-SM3), surpassing the accuracy levels of experienced endoscopists with over 10 years of experience.
