Artificial Intelligence Enables Precision Diagnosis of Cervical Cytology Grades and Cervical Cancer

Study Overview
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Brief Summary
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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.

Official Title
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Artificial Intelligence Enables Precision Diagnosis of Cervical Cytology Grades and Cervical Cancer

Conditions
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Cervical Cancer, Diagnostic Platform, Diagnosing Cervical Cytology Grades and Cancer, Artificial Intelligence

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: 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). Exclusion Criteria: 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.

Ages Eligible for Study
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Minimum Age: 25 Years
Maximum Age: 65 Years

Sexes Eligible for Study
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Female

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Training dataset<br>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<br>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<br>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<br>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<br>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<br>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 the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Area under ROC curve (AUC) | Area under the curve | Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 (%). | 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 (%). | 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). | Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Diagnostic Platform, Cervical Cytology Grade Diagnosis, Thinprep Cytologic Test, Artificial Intelligence Deep Learning Algorithm