Detection of Ovarian Cancer Using an Artificial Intelligence Enabled Transvaginal Ultrasound Imaging Algorithm

October 6, 2021 updated by: Qinglei Gao, Tongji Hospital
Ovarian cancer is relatively rare but fatal with an annual incidence rate of 11.8 per 100 000 and a high mortality-to-incidence ratio of >0.6. The modest diagnostic accuracy of TVU has risen some concerns about the over-treatment.Now, with the development of artificial intelligence (AI), we may have a better chance to interpret TVU imagines with high efficiency, reproducibility and accuracy.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Study Type

Interventional

Enrollment (Anticipated)

10000

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

  • Name: Ding Ma, MD, PhD
  • Phone Number: 13886090620 13886090620
  • Email: dingma424@126.com

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 80 years (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

Female

Description

Inclusion Criteria:

  • Women scheduled for Transvaginal Ultrasound examination for adnexal lesions;
  • Women aged over 18 years old;
  • Women willing to participant in this study evidenced by signing the informed consent.

Exclusion Criteria:

  • Women without adnexa for any reasons at the time of Transvaginal Ultrasound examination, including but not limited to receiving surgical removal for adnexa;
  • Women with a pathologic diagnosis of ovarian cancer before the Transvaginal Ultrasound examination;
  • Women with mental abnormal;
  • Women did not cooperate or participate in other clinical trials;
  • Pregnant or lactating women.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Double

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
No Intervention: Transvaginal Ultrasound diagnosis
radiologists interpretTransvaginal Ultrasound images without the help of Artificial Intelligence (AI) algorithm
Experimental: AI enabled Transvaginal Ultrasound diagnosis
radiologists interpretTransvaginal Ultrasound images with the help of Artificial Intelligence algorithm
Diagnostic test: Artificial Intelligence Enabled Transvaginal Ultrasound Imaging algorithm
AI Enabled Transvaginal Ultrasound diagnosis for ovarian cancer
Other Names:
  • AI Enabled Transvaginal Ultrasound diagnosis

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
diagnostic accuracy
Time Frame: 2 years
diagnostic accuracy comparison between Transvaginal Ultrasound diagnosis with and without Artificial Intelligence algorithm for ovarian cancer
2 years

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
time cost for Transvaginal Ultrasound image interpretation
Time Frame: 2 years
time cost for radiologists to interpret Transvaginal Ultrasound images
2 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Tongji Hospital

Collaborators

First Affiliated Hospital, Sun Yat-Sen University
Henan Cancer Hospital
Qilu Hospital of Shandong University
Hubei Cancer Hospital
The First People's Hospital of Jingzhou
Xiangyang Central Hospital

Investigators

  • Study Chair: Qinglei Gao, MD, PhD, Tongji Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Anticipated)

October 1, 2022

Primary Completion (Anticipated)

October 1, 2023

Study Completion (Anticipated)

October 1, 2024

Study Registration Dates

First Submitted

December 28, 2019

First Submitted That Met QC Criteria

December 28, 2019

First Posted (Actual)

January 2, 2020

Study Record Updates

Last Update Posted (Actual)

October 7, 2021

Last Update Submitted That Met QC Criteria

October 6, 2021

Last Verified

October 1, 2021

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2019-TJ-OVAB

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

Yes

IPD Plan Description

contact Prof. Gao for detailed study protocol or data after the study completed by e-mail

IPD Sharing Time Frame

6 months after the study completed

IPD Sharing Access Criteria

all investigators in this study field can contact Prof. Gao for access by e-mail

IPD Sharing Supporting Information Type

  • Study Protocol
  • Statistical Analysis Plan (SAP)

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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