Clinical trial NCT04551287: Artificial Intelligence Enables Precision Diagnosis of Cervical Cytology Grades and Cervical Cancer
Official title: Artificial Intelligence Enables Precision Diagnosis of Cervical Cytology Grades and Cervical Cancer
Study type: OBSERVATIONAL
Overall status: COMPLETED
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
Lead sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Sponsor class: OTHER
Collaborators: name: Guangzhou Women and Children's Medical Center
name: The Third Affiliated Hospital of Guangzhou Medical University
First posted: 2020-09-16
Enrollment: 16164
Sex: FEMALE
Minimum age (years): 25
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: OTHER
Sampling method: NON_PROBABILITY_SAMPLE
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.

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.


Eligibility criteria: Inclusion Criteria:

1. Women Aged 25-65 years old.
2. 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:

1. 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.
2. Women diagnosed with other malignant tumors other than cervical cancer.

Primary outcomes:
measure: Area under ROC curve (AUC)
timeFrame: Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained
Secondary outcomes:
measure: Specificity
timeFrame: Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained
measure: Sensitivity
timeFrame: Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained
measure: Accuracy
timeFrame: Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: Guangzhou Women and Children's Medical Center
state: Guangdong
city: Guangzhou
country: China
facility: The Third Affiliated Hospital of Guangzhou Medical University
state: Guangdong
city: Guangzhou
country: China
facility: Sun Yat-Sen Memorial Hospital of Sun Yat-sen University
state: Guangdong
Study officials:
affiliation: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
name: Herui Yao, PhD
role: PRINCIPAL_INVESTIGATOR
References: