NCT04214782

Brief Summary

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.

Trial Health

35
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
10,000

participants targeted

Target at P75+ for not_applicable ovarian-cancer

Timeline
Completed

Started Oct 2022

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

December 28, 2019

Completed
5 days until next milestone

First Posted

Study publicly available on registry

January 2, 2020

Completed
2.7 years until next milestone

Study Start

First participant enrolled

October 1, 2022

Completed
1 year until next milestone

Primary Completion

Last participant's last visit for primary outcome

October 1, 2023

Completed
1 year until next milestone

Study Completion

Last participant's last visit for all outcomes

October 1, 2024

Completed
Last Updated

October 7, 2021

Status Verified

October 1, 2021

Enrollment Period

1 year

First QC Date

December 28, 2019

Last Update Submit

October 6, 2021

Conditions

Ovarian Cancer

Outcome Measures

Primary Outcomes (1)

  • diagnostic accuracy

    diagnostic accuracy comparison between Transvaginal Ultrasound diagnosis with and without Artificial Intelligence algorithm for ovarian cancer

    2 years

Secondary Outcomes (1)

  • time cost for Transvaginal Ultrasound image interpretation

    2 years

Study Arms (2)

Transvaginal Ultrasound diagnosis

NO INTERVENTION

radiologists interpretTransvaginal Ultrasound images without the help of Artificial Intelligence (AI) algorithm

AI enabled Transvaginal Ultrasound diagnosis

EXPERIMENTAL

radiologists interpretTransvaginal Ultrasound images with the help of Artificial Intelligence algorithm

Diagnostic Test: Artificial Intelligence Enabled Transvaginal Ultrasound Imaging algorithm

Interventions

Artificial Intelligence Enabled Transvaginal Ultrasound Imaging algorithmDIAGNOSTIC_TEST

AI Enabled Transvaginal Ultrasound diagnosis for ovarian cancer

Also known as: AI Enabled Transvaginal Ultrasound diagnosis
AI enabled Transvaginal Ultrasound diagnosis

Eligibility Criteria

Age18 Years - 80 Years
Sexfemale
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)

You may qualify if:

  • 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.

You may not qualify if:

  • 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.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

MeSH Terms

Conditions

Ovarian Neoplasms

Condition Hierarchy (Ancestors)

Endocrine Gland NeoplasmsNeoplasms by SiteNeoplasmsOvarian DiseasesAdnexal DiseasesGenital Diseases, FemaleFemale Urogenital DiseasesFemale Urogenital Diseases and Pregnancy ComplicationsUrogenital DiseasesGenital Neoplasms, FemaleUrogenital NeoplasmsGenital DiseasesEndocrine System DiseasesGonadal Disorders

Study Officials

  • Qinglei Gao, MD, PhD

    Tongji Hospital

    STUDY CHAIR

Central Study Contacts

Qinglei Gao, MD, PhD

CONTACT

13871127473qingleigao@hotmail.com

Ding Ma, MD, PhD

CONTACT

13886090620dingma424@126.com

Study Design

Study Type
interventional
Phase
not applicable
Allocation
RANDOMIZED
Masking
DOUBLE
Who Masked
PARTICIPANT, OUTCOMES ASSESSOR
Purpose
DIAGNOSTIC
Intervention Model
PARALLEL
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Clinical Professor

Study Record Dates

First Submitted

December 28, 2019

First Posted

January 2, 2020

Study Start

October 1, 2022

Primary Completion

October 1, 2023

Study Completion

October 1, 2024

Last Updated

October 7, 2021

Record last verified: 2021-10

Data Sharing

IPD Sharing
Will share

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

Shared Documents
STUDY PROTOCOL, SAP
Time Frame
6 months after the study completed
Access Criteria
all investigators in this study field can contact Prof. Gao for access by e-mail