Skip to Main Content

An official website of the United States government

Artificial intelligence based model to predict diagnosis of ovarian cancer using laboratory tests: study protocol for a multicenter, retrospective study

Principal Investigator
Name
Xin Zhen
Degrees
Ph.D
Institution
Southern Medical University
Position Title
Professor
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCO-1656
Initial CDAS Request Approval
Sep 5, 2024
Title
Artificial intelligence based model to predict diagnosis of ovarian cancer using laboratory tests: study protocol for a multicenter, retrospective study
Summary
Ovarian cancer (OC) has been acknowledged as the most lethal gynecological cancer. Timely diagnosis of OC is difficult due to the lack of effective biomarkers. We aim to systematically evaluate the value of routine laboratory tests on the prediction of OC, and develop a robust and generalizable ensemble artificial intelligence model to assist in identifying patients with OC. Routine laboratory items and age before treatment are collected to build an artificial intelligence-based prediction model for OC diagnosis prediction. Meanwhile, we will evaluate the value of routine laboratory tests for the prediction of OC, which might mine the potentials of laboratory items for further mechanism research on OC oncogenesis and development. This study will allow identifying the main blood- or urine-based determinants of OC to better propose primary and secondary preventive measures in order to contribute to enhanced OC diagnosis and patient care.
Aims

We aim to systematically evaluate the value of routine laboratory tests on the prediction of OC, and develop a robust and generalizable ensemble artificial intelligence model to assist in identifying patients with OC. This study will allow identification of the key blood- or urine-based determinants of OC, facilitating more effective recommendations for primary and secondary preventive measures and ultimately enhancing the diagnosis and patient care for OC patients.

Collaborators

1. Guangyao Cai: Department of Gynecologic Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
2. Fangjun Huang: School of Biomedical Engineering, Southern Medical University, Guangzhou, P. R China.
3. Yue Gao: Cancer Biology Research Centre (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China.
4. Xiao Li: Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, P. R. China.
5. Jianhua Chi: Cancer Biology Research Centre (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China.
6. Jincheng Xie: School of Biomedical Engineering, Southern Medical University, Guangzhou, P. R China.
7. Linghong Zhou: School of Biomedical Engineering, Southern Medical University, Guangzhou, P. R China.
8. Yanling Feng: Department of Gynecologic Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
9. He Huang: Department of Gynecologic Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
10. Ting Deng: Department of Gynecologic Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
11. Yun Zhou: Department of Gynecologic Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
12. Chuyao Zhang: Department of Gynecologic Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
13. Xiaolin Luo: Department of Gynecologic Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
14. Xing Xie: Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, P. R. China.
15. Qinglei Gao: Cancer Biology Research Centre (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China.
16. Jihong Liu: Department of Gynecologic Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.