Cervical CancerNCT04551287
Artificial Intelligence Enables Precision Diagnosis of Cervical Cytology Grades and Cervical Cancer
An observational study of Cervical Cancer and Diagnostic Platform, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 3 sites in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 25 to 65 years, women onlyHealthy volunteers can take part
- Conditions the study is for
- Cervical Cancer, Diagnostic Platform, Diagnosing Cervical Cytology Grades and Cancer
- How many people
- 16,164 participants
- Where
- 3 sitesChina
- Study dates
- 1 Jul 2019to 14 Dec 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 25 to 65 years
- Women only
- Healthy volunteers accepted
- Adult, Older adult
Study population: 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.
Who may be able to join
- 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).
Who cannot join
- 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.
Trial locations and contacts
3 sites in 1 country.
China3 sites
Guangzhou Women and Children's Medical Center
Guangzhou, Guangdong, 510000
The Third Affiliated Hospital of Guangzhou Medical University
Guangzhou, Guangdong, 510000
Sun Yat-Sen Memorial Hospital of Sun Yat-sen University
Guangzhou, Guangdong, 510120
About this study
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.
- Conditions
- Cervical Cancer, Diagnostic Platform, Diagnosing Cervical Cytology Grades and Cancer, Artificial Intelligence
- Keywords
- Diagnostic Platform, Cervical Cytology Grade Diagnosis, Thinprep Cytologic Test, Artificial Intelligence Deep Learning Algorithm
- Medical subject headings
- Uterine Cervical Neoplasms, Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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). |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under ROC curve (AUC)
Area under the curve
Measured: Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained
Secondary outcomes (3)
Specificity
The true negative rate (TNR) of the diagnostic platform, which is the ratio between the number of negative individuals correctly categorized by platform and the total number of actual negative individuals (%).
Measured: Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained
Sensitivity
The true positive rate (TPR) of the diagnostic platform, which is the ratio between the number of positive individuals correctly categorized by platform and the total number of actual positive individuals (%).
Measured: Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained
Accuracy
The quantity of true positive (TP) plus true negative (TN) over the quantity of (TP) plus true negative (TN) plus false positive (FP) plus false negative (FN).
Measured: Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Sep 2020
- Study start1 Jul 2019
- Primary completion14 Dec 2020
- Study completion14 Dec 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Cervical CancerStudies of the same condition, open and under way.
- More trials run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 16,164 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Guangzhou Women and Children's Medical Center, The Third Affiliated Hospital of Guangzhou Medical University
- Responsible party
- Principal investigator, Herui Yao, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Investigators
- Herui Yao, PhD, Principal investigator, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04551287
- Sponsor's protocol ID
- 2020-KY-114
- Status last verified
- Aug 2023
- First submitted
- 6 Sep 2020
- Last updated
- 8 Aug 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
Study at a glance
From the registry record, last updated by the sponsor on 8 Aug 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence Enables Precision Diagnosis of Cervical Cytology Grades and Cervical Cancer
- ClinicalTrials.gov ID
- NCT04551287
- Acronym / sponsor ID
- 2020-KY-114
- Conditions
- Cervical Cancer, Diagnostic Platform, Diagnosing Cervical Cytology Grades and Cancer, Artificial Intelligence
- Keywords
- Diagnostic Platform, Cervical Cytology Grade Diagnosis, Thinprep Cytologic Test, Artificial Intelligence Deep Learning Algorithm
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 16,164 participants (actual)
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityUniversity, hospital or other
- Collaborators
- Guangzhou Women and Children's Medical Center, The Third Affiliated Hospital of Guangzhou Medical University
- Study start
- 1 Jul 2019 (actual)
- Primary completion
- 14 Dec 2020 (actual)
- Study completion
- 14 Dec 2020 (actual)
- First posted
- 16 Sep 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 6
| Arm | Type | Receives |
|---|---|---|
| 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). |
Who may qualify
- Ages
- 25 to 65 years
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- 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.
Key inclusion criteria (2 listed)
- 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).
Key exclusion criteria (2 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Area under ROC curve (AUC)
Time frame: Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained
Area under the curve
3 secondary outcomes
Specificity
Time frame: Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained
Sensitivity
Time frame: Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained
Accuracy
Time frame: Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Aug 2023.
- Individual participant dataSharing planned: No. 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.
Related trials for Cervical Cancer
RecruitingPhase 1/2
Study of INBRX-106 and INBRX-106 in Combination With Pembrolizumab (Keytruda®) in Subjects With Locally Advanced or Metastatic Solid Tumors (Hexavalent OX40 Agonist)
Solid Tumor, Non-Small Cell Lung Cancer
RecruitingObservational study
Proton Re-irradiation in Patients With Recurrent or Second Primary Head and Neck Cancer , a Multicentre Phase II Single Arm Trial
Head & Neck Cancer
RecruitingPhase 2/3
Multicenter Validation Trial of [18F]AlF-FAPI-74 for PET Imaging of Cancer-associated Fibroblasts Through Fibroblast Activation Protein Inhibitors (FAPI) in Different Tumor Types
Oesophageal Cancer, Gastric Cancer
RecruitingPhase 2
Proton Spatially Fractionated Radiotherapy or Photon Stereotactic Body Radiotherapy in Combination With Pembrolizumab Before Surgery for the Treatment of Locally Advanced and Radiation-Naive Locoregionally Recurrent Oral Cavity Cancer, OSPRI Trial
Locally Advanced Oral Cavity Squamous Cell Carcinoma, Locally Recurrent Oral Cavity Squamous Cell Carcinoma
RecruitingPhase 4
Lidocaine Infiltration and Neuroimmunomodulation in Head and Neck Cancer Surgery
Head and Neck Cancer (H&N)
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Herui Yao, PhD principal investigator
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cervical Cancer and Diagnostic Platform, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 3 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 8 Aug 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under ROC curve (AUC) (time frame: Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Aug 2023.