Artificial Intelligence Enables Precision Diagnosis of Cervical Cytology Grades and Cervical Cancer
1 other identifier
observational
16,164
1 country
3
Brief Summary
Cervical cancer, the fourth most common cancer globally and the fourth leading cause of cancer-related deaths, can be effectively prevented through early screening. Detecting precancerous cervical lesions and halting their progression in a timely manner is crucial. However, accurate screening platforms for early detection of cervical cancer are needed. Therefore, it is urgent to develop an Artificial Intelligence Cervical Cancer Screening (AICS) system for diagnosing cervical cytology grades and cancer.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jul 2019
3 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
July 1, 2019
CompletedFirst Submitted
Initial submission to the registry
September 6, 2020
CompletedFirst Posted
Study publicly available on registry
September 16, 2020
CompletedPrimary Completion
Last participant's last visit for primary outcome
December 14, 2020
CompletedStudy Completion
Last participant's last visit for all outcomes
December 14, 2020
CompletedAugust 8, 2023
August 1, 2023
1.5 years
September 6, 2020
August 6, 2023
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Area under ROC curve (AUC)
Area under the curve
Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained
Secondary Outcomes (3)
Specificity
Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained
Sensitivity
Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained
Accuracy
Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained
Study Arms (6)
Training dataset
11,468 eligible individuals' slides for the cervical cytology screening collected from the Sun Yat-sen Memorial Hospital (SYSMH, Guangzhou, China) between January 2016 and January 2020, were randomly assigned to the training dataset (n = 9,316) and the internal validation dataset (n = 2,152) in order to train and validate the Artificial Intelligence Cervical Cancer Screening (AICS).
SYSMH internal validation dataset
11,468 eligible individuals' slides for the cervical cytology screening collected from the Sun Yat-sen Memorial Hospital (SYSMH, Guangzhou, China) between January 2016 and January 2020, were randomly assigned to the training dataset (n = 9,316) and the internal validation dataset (n = 2,152) in order to train and validate the Artificial Intelligence Cervical Cancer Screening (AICS).
TAHGMU external validation dataset
600 slides from 600 eligible individuals were obtained in the Third Affiliated Hospital of Guangzhou Medical University (TAHGMU, Guangzhou, China) between January 2016 and January 2020, which was used to validate the Artificial Intelligence Cervical Cancer Screening (AICS).
GWCMC external validation dataset
600 slides from 600 eligible individuals were obtained in Guangzhou Women and Children Medical Center (GWCMC, Guangzhou, China) between January 2016 and January 2020, which was used to validate the Artificial Intelligence Cervical Cancer Screening (AICS).
Prospective validation dataset
A prospective validation dataset was conducted to distinguish the diagnostic performance of the cytopathologists, AICS, and AICS-assisted cytopathologists, in which 2,780 eligible slides from 2,780 individuals were obtained and prospectively labeled between August 28, 2020 and October 16, 2020 at SYSMH.
Randomized controlled trial
A prospective randomized controlled trial was conducted to compare the performance of the cytopathologists, AICS, and AICS-assisted cytopathologists in SYSMH. Here, 618 slides were collected between August 13, 2020, and December 14, 2020, to build the SYSMH randomized controlled trial. The remaining 608 slides after quality control were randomly assigned (1:1:1) to the AICS group (n = 201), the cytopathologists group (n = 203), and the AICS-assisted cytopathologists group (n = 204).
Eligibility Criteria
Female patients who were 18 years or older with clear diagnostic results of cervical liquid-based cytological examination were included. All cases were collected from Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University.
You may qualify if:
- Women Aged 25-65 years old.
- Availability of confirmed diagnostic results of the cervical liquid-based cytological examination, and satisfactory digital images from the liquid-based cytology pap test: at least 5000 uncovered and observable squamous epithelial cells, samples with abnormal cells (atypical squamous cells or atypical glandular cells and above).
You may not qualify if:
- Unsatisfactory samples of cervical liquid-based cytological examination: less than 5000 uncovered, observable squamous epithelial cells, and more than 75% of squamous epithelial cells affected because of blood, inflammatory cells, epithelial cells over-overlapping, poor fixation, excessive drying, or contamination of unknown components.
- Women diagnosed with other malignant tumors other than cervical cancer.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (3)
Guangzhou Women and Children's Medical Center
Guangzhou, Guangdong, 510000, China
The Third Affiliated Hospital of Guangzhou Medical University
Guangzhou, Guangdong, 510000, China
Sun Yat-Sen Memorial Hospital of Sun Yat-sen University
Guangzhou, Guangdong, 510120, China
Biospecimen
Samples Without DNA: Samples retained, with no potential for DNA extraction from any retained samples (e.g., fixed tissue, plasma)
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Herui Yao, PhD
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- OTHER
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Principal Investigator
Study Record Dates
First Submitted
September 6, 2020
First Posted
September 16, 2020
Study Start
July 1, 2019
Primary Completion
December 14, 2020
Study Completion
December 14, 2020
Last Updated
August 8, 2023
Record last verified: 2023-08
Data Sharing
- IPD Sharing
- Will not share
Requests for the data collected and analyzed in this study will be considered if the application is in line with public benefits and the applicant is willing to sign a data access agreement. Contact can be through the corresponding author.