Model Study on Cervical Cancer Screening Strategies and Risk Prediction

NCT ID: NCT06204133

Last Updated: 2024-07-22

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 1112846 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-11-01
• Study Completion Date: 2024-06-30

Brief Summary

By collecting non-image medical data of women undergoing cervical screening in multiple centers in China, including age, HPV infection status, HPV infection type, TCT results, and colposcopy biopsy pathology results, a multi-source heterogeneous cervical lesion collaborative research big data platform was established. Based on artificial intelligence (AI) machine learning, cervical lesion screening features are refined, a multi-modal cervical cancer intelligent screening prediction and risk triage model is constructed, and its clinical application value is preliminarily explored.

Detailed Description

By collecting non-image medical data of women undergoing cervical screening in multiple centers in China, including age, HPV infection status, HPV infection type, TCT results, and colposcopy biopsy pathology results, a multi-source heterogeneous cervical lesion collaborative research big data platform was established. Based on artificial intelligence (AI) machine learning, cervical lesion screening features are refined, a multi-modal cervical cancer intelligent screening prediction and risk triage model is constructed, and its clinical application value is preliminarily explored. The effect of clinical application of the model was evaluated by internal data from Fujian Province and external data from several other regions in China.

Conditions

Cervical Cancer Screening; Risk Assessment; Artificial Intelligence; Machine Learning

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Interventions

Artificial intelligence model building

Using non-image medical data of cervical lesions and clinical pathology results in different medical institutions, machine learning is adopted to establish multiple multi-modal cervical cancer intelligent screening prediction models. This method was used to analyze the prediction performance of the multi-modal cervical cancer intelligent screening prediction and risk triage model, and to evaluate and optimize the self-learning ability of the established multi-modal cervical cancer intelligent screening prediction model.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• Age 25-64 years old;
• There was no history of precancerous lesions or cervical cancer;
• No previous cervical surgery or cervical removal;

Exclusion Criteria

• HPV test results are not available;
• Pregnant or lactating women;
• There is a serious immune system disease, and the disease is active;

• Minimum Eligible Age: 25 Years
• Maximum Eligible Age: 64 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: Yes

Sponsors

Fujian Maternity and Child Health Hospital

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Pengming Sun

Role: STUDY_CHAIR

Fujian Maternal and Child Health Hospital

Locations

Fujian Maternity and Child Health Hospital

Fuzhou, Fujian, China

Ningde maternal and child health hospital

Ningde, Fujian, China

Gansu Provincial Maternity and Child-care Hospital

Lanzhou, Ganshu, China

Shunde Women's and Children's Hospital of Guangdong Medical University

Foshan, Guangdong, China

Shenzhen Maternal and Child Health Hospital

Shenzhen, Guangdong, China

Guiyang maternal and child health care hospital

Guiyang, Guizhou, China

Hubei Maternal and Child Health Hospital

Wuhan, Hubei, China

Countries

China

Other Identifiers

CSRM2304

Identifier Type: -

Identifier Source: org_study_id