| Autor: |
Yadong Feng, Bin Wang, Liang Pan, Bin Yao, Bin Deng, Yan Liang, Yongzhen Sun, Juncai Zang, Xinyi Xu, Jie Song, Mengjie Li, Guangpeng Xu, Kai Zhao, Cui-E. Cheng, Ruihua Shi |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
BMC Cancer, Vol 22, Iss 1, Pp 1-7 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1471-2407 |
| DOI: |
10.1186/s12885-022-10220-3 |
| Popis: |
Abstract Background Endoscopic screening is the widely accepted screening strategy for esophageal squmaous cell carcinoma (ESCC). However, massive endoscopic screening is expensive and not cost-efficient, and novel pre-endoscopy detection used as a preliminary screening method arouses new concerns. We are planning to launch an artificial intelligence (AI) assisted sponge cytology for detecting esophageal squmaous high-grade intraepithelial neoplasia (HGIN) and above lesions. The aim of this trail is to investigate the efficiency of AI-assisted sponge cytology in population-based screening of early esophageal squmaous epithelial lesions. Methods The study will be prospectively conducted in five regions with a high prevalence of ESCC. AI-assisted sponge cytology and endoscopic examination will be sequentially performed. Based on our previous data, at least 864 patients with esophageal HGIN and above lesions are needed to achieve enough statistical power. And, a calculated 112,500 individuals with high risks of ESCC will be recruited. In the first stage, each 24,000 participants who meet the inclusion criteria will be recruited on a voluntary basis. Setting pathological results as standard reference, diagnostic threshold and according performance of AI-assisted detection will be evaluated. A prediction model will be constructed by co-analyzing cytological results and relevant risk factors. Then, an external validation cohort will be used for validation of the model efficiency. Also, cost-efficiency analysis will be performed. This study protocol was registered on chineseclinicaltrial.gov (ChiCTR1900028524). Discussion Our study will determine whether this AI-assisted sponge cytology can be used as an effective pre-endoscopy detection tool for large-scale screening for ESCC in high-risk areas. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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