Development and validation of an artificial intelligence-assisted esophageal cytological risk prediction model for detecting esophageal precancerous lesions
10.3760/cma.j.cn321463-20240904-00378
- VernacularTitle:人工智能食管细胞学风险预测模型在食管癌前病变中的构建和验证
- Author:
Huishan JIANG
1
;
Ye GAO
;
Han LIN
;
Lei XIN
;
Wei WANG
;
Zhaoshen LI
;
Luowei WANG
Author Information
1. 海军军医大学第一附属医院消化内科，上海 200433
- Keywords:
Esophagus;
Esophageal cancer;
Screening;
Precancerous lesions;
Esophageal cytology;
Non-endoscopic screening
- From:
Chinese Journal of Digestive Endoscopy
2024;41(10):762-767
- CountryChina
- Language:Chinese
-
Abstract:
Objective:Artificial intelligence-assisted esophageal cytology was used to develop and validate a risk prediction model for screening esophageal precancerous lesions.Methods:This study was a secondary analysis of data from the esophageal cancer screening trial (EAST). A total of 17 294 subjects were included who underwent upper gastrointestinal endoscopy and artificial intelligence-assisted esophageal cytology screening at 39 tertiary or secondary hospitals and 5 community service centers in areas with high incidence of esophageal squamous cell carcinoma in China from January 1, 2021 to June 30, 2022. Subjects ( n=14 415) screened in the hospital constituted the hospital opportunistic screening cohort, which served as the training set. An artificial intelligence-assisted esophageal cytological risk prediction model (LightGBM model for short) was developed based on light-gradient boosting machine (LightGBM) machine learning algorithm. Subjects undergoing screening at 5 community health service centers ( n=2 879) constituted a community screening cohort, which served as a validation set. The diagnostic efficacy of LightGBM model for esophageal precancerous lesions in the community screening cohort was evaluated by using pathological results of endoscopic biopsy as the golden standard. Results:The LightGBM model, trained in the opportunistic screening cohort, exhibited an area under the receiver operator characteristic (ROC) curve of 0.93 (95% CI: 0.91-0.95) for detecting precancerous lesions. The cutoff value of the ROC curve was determined as 0.08 based on the maximum Youden index. The sensitivity and specificity of LightGBM model were 91.0% (95% CI: 86.9%-95.1%) and 86.2% (95% CI: 85.7%-86.8%), respectively, when the risk prediction score was >0.08 as the screening criterion for precancerous lesions. The sensitivity, specificity, and accuracy of LightGBM model for precancerous lesions in the community screening cohort were 95.2% (20/21), 87.5% (2 502/2 858), and 87.6% (2 522/2 879), respectively. Conclusion:The artificial intelligence-assisted esophageal cytology risk prediction model showcased remarkable sensitivity and specificity in screening for esophageal precancerous lesions, underscoring its potential for widespread adoption and application in esophageal cancer screening.
