Model Study on Cervical Cancer Screening Strategies and Risk Prediction

Artificial Intelligence Clinical Trial

Official title:

Model Study on Cervical Cancer Screening Strategies and Risk Prediction

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06204133
• Other study ID #: CSRM2304
• Status: Recruiting
• Start date: November 1, 2023
• Est. completion date: June 30, 2024

Study information

• Verified date: April 2024
• Source: Fujian Maternity and Child Health Hospital
• Contact: Binhua Dong
• Phone: +8659187558732
• Email: dongbinhua86[@]fjmu.edu.cn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1500000
• Est. completion date: June 30, 2024
• Est. primary completion date: May 31, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Female
• Age group: 25 Years to 64 Years

Eligibility

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;

Related Conditions & MeSH terms

• Artificial Intelligence
• Cervical Cancer Screening
• Machine Learning
• Risk Assessment
• Uterine Cervical Neoplasms

Intervention

Other:
• 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.

Locations

Country	Name	City	State
China	Shunde Women's and Children's Hospital of Guangdong Medical University	Foshan	Guangdong
China	Fujian Maternity and Child Health Hospital	Fuzhou	Fujian
China	Ningde maternal and child health hospital	Ningde	Fujian
China	Shenzhen Maternal and Child Health Hospital	Shenzhen	Guangdong
China	Hubei Maternal and Child Health Hospital	Wuhan	Hubei

Sponsors (1)

Lead Sponsor	Collaborator
Fujian Maternity and Child Health Hospital

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Cervical histopathology	Cervical histopathological diagnosis within 8 weeks	within 8 weeks,
Primary	colposcopy	Colposcopists use colposcopic equipment to investigate the occurrence of cervical and vaginal lesions within 8 weeks	Percentage of patients diagnosed with cervical intraepithelial neoplasia of grade 3 (CIN3) or worse by cervical histopathological measurements within 8 weeks