Application of AutoML Based on Texture Features in Judging the
Staging of Esophageal Carcinoma by NBI-ME
HE Yu1, XUE Yuhan1, ZHOU Yijia1, YIN Minyue1,2, LIN Jiaxi1,2, GAO Xin1,2, HU Kewei1,3, ZHU Jinzhou1,2
1. College of Suzhou Medical, Soochow University, Suzhou Jiangsu 215123, China; 2. Department of Gastroenterology, The First
Affiliated Hospital of Soochow University, Suzhou Jiangsu 215006, China; 3. Department of Gastroenterology, The Second Affiliated
Hospital of Soochow University, Suzhou Jiangsu 215004, China
Abstract：Objective To explore the application of automated machine learning (AutoML) based on the texture features in
distinguishing early and advanced stages of esophageal squamous cell carcinoma by magnifying endoscopy with narrow-band
imaging (NBI-ME). Methods A total of 1507 NBI-ME images of esophageal squamous cell carcinoma were collected from the
Endoscopy Centre of The First Affiliated Hospital of Soochow University. These images were randomly divided into a training
set (n=1264) and a validation set (n=243). MATLAB software was used to extract a total of 32 texture features from the whole
endoscopic images. The above variables were loaded into the H2O platform for AutoML binary classification modeling. In addition,
278 endoscopic images from the Second Affiliated Hospital of Soochow University were collected as an external test set. Moreover,
one junior endoscopist and one senior endoscopist were invited to interpret the images from the external test set. Receiver operating
characteristic (ROC) area under curve (AUC) and accuracy (ACC) were used to evaluate the efficiency of identification.
Results The AutoML model based on random forest algorithm performed best in the external test set, with AUC of 0.975 and ACC
of 0.939, which was significantly better than other models, including the generalized linear model (AUC: 0.776, ACC: 0.687) and
the extreme gradient boosting model (AUC: 0.968, ACC: 0.863). It was also better than that of the junior endoscopist (AUC: 0.868,
ACC: 0.871) and the senior endoscopist (AUC: 0.919, ACC: 0.921). Conclusion The AutoML model based on the texture features of
endoscopic images shows excellent discriminative ability in judging the staging of esophageal carcinoma.
何宇1，薛雨涵1，周亦佳1，殷民月1,2，林嘉希1,2，高欣1,2，胡可伟1,3，朱锦舟1,2. 基于纹理特征的AutoML在NBI-ME
判断食管癌分期中的应用[J]. 中国医疗设备, 2023, 38(11): 6-10.
HE Yu1, XUE Yuhan1, ZHOU Yijia1, YIN Minyue1,2, LIN Jiaxi1,2, GAO Xin1,2, HU Kewei1,3, ZHU Jinzhou1,2. Application of AutoML Based on Texture Features in Judging the
Staging of Esophageal Carcinoma by NBI-ME. China Medical Devices, 2023, 38(11): 6-10.
