| Category |
Oncology |
| Manuscript Type |
Retrospective Study |
| Article Title |
Predicting three-year risk of incident colorectal precancerous conditions using non-invasive predictors and machine-learning algorithms: A multi-center retrospective study
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Xiao-Yang Huang, Li Zhao, Hui He, Rui Peng, Han Xue, Si-Min Lai, Wei-Xing Wu, Xiang-Xiang Qiu, Xu-Hao Cai, Jia-Hui He, Wen-Jian Qin, Wen-Ting Zhou, Wei-Qing Wu and Yong-Ping Lin |
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Sanming Project of Medicine in Shenzhen |
No. SZSM202311002 |
| Shenzhen Longhua District 2024 Science and Technology Innovation Special Fund |
No. 2548A20241107A140254 |
| Shenzhen Health Economics Society |
No. 202675 |
|
| Corresponding Author |
Wei-Qing Wu, Director, MD, PhD, Professor, Department of Health Management, Shenzhen People’s Hospital (The First Affiliated Hospital, Southern University of Science and Technology, The Second Clinical Medical College, Jinan University), No. 1017 Dongmenbei Street, Shenzhen 518020, Guangdong Province, China. wweiqing007@sina.com |
| Key Words |
Early detection of cancer; Precancerous conditions; Colorectal neoplasms; Machine learning; Retrospective study |
| Core Tip |
This retrospective study developed and validated a machine learning model using seven non-invasive predictors, serum thymidine kinase 1 protein, cytokeratin 19 fragment, carbohydrate antigen 19-9, age, diastolic blood pressure, fecal immunochemical test, and sex, to predict the three-year risk of incident advanced adenoma (AA) in patients with non-AA. The model achieved high discriminative performance [area under the receiver operating characteristic: (1) 0.988 in training; and (2) 0.767 in external validation] and superior clinical utility over traditional methods. An open-access web application was deployed to enable precise detection of patients at high-risk of developing AA, offering a novel tool to guide more intensive follow-up for this high-risk subgroup. |
| Citation |
Huang XY, Zhao L, He H, Peng R, Xue H, Lai SM, Wu WX, Qiu XX, Cai XH, He JH, Qin WJ, Zhou WT, Wu WQ, Lin YP. Predicting three-year risk of incident colorectal precancerous conditions using non-invasive predictors and machine-learning algorithms: A multi-center retrospective study. World J Gastroenterol 2026; In press
|
| PDF |
121223-in-press.pdf
|
