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

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04214782
Recruitment Status : Not yet recruiting
First Posted : January 2, 2020
Last Update Posted : January 2, 2020
Sponsor:
Collaborators:
Hubei Cancer Hospital
Qilu Hospital of Shandong University
Henan Cancer Hospital
Xiangyang Central Hospital
The First People's Hospital of Jingzhou
First Affiliated Hospital, Sun Yat-Sen University
Information provided by (Responsible Party):
Qinglei Gao, Tongji Hospital

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.

Condition or disease Intervention/treatment Phase
Ovarian Cancer Diagnostic Test: Artificial Intelligence Enabled Transvaginal Ultrasound Imaging algorithm Not Applicable

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 10000 participants
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Double (Participant, Outcomes Assessor)
Primary Purpose: Diagnostic
Official Title: Detection of Ovarian Cancer Using an Artificial Intelligence Enabled Transvaginal Ultrasound Imaging Algorithm
Estimated Study Start Date : January 1, 2020
Estimated Primary Completion Date : January 1, 2022
Estimated Study Completion Date : January 1, 2024


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 Name: AI Enabled Transvaginal Ultrasound diagnosis




Primary Outcome Measures :
  1. diagnostic accuracy [ Time Frame: 2 years ]
    diagnostic accuracy comparsion between Transvaginal Ultrasound diagnosis with and without Artificial Intelligence algorithm for ovarian cancer


Secondary Outcome Measures :
  1. time cost for Transvaginal Ultrasound image interpretation [ Time Frame: 2 years ]
    time cost for radiologists to interpret Transvaginal Ultrasound images



Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


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Ages Eligible for Study:   18 Years to 80 Years   (Adult, Older Adult)
Sexes Eligible for Study:   Female
Accepts Healthy Volunteers:   No
Criteria

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.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04214782


Contacts
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Contact: Qinglei Gao, MD, PhD 13871127473 ext 13871127473 qingleigao@hotmail.com
Contact: Ding Ma, MD, PhD 13886090620 ext 13886090620 dingma424@126.com

Sponsors and Collaborators
Tongji Hospital
Hubei Cancer Hospital
Qilu Hospital of Shandong University
Henan Cancer Hospital
Xiangyang Central Hospital
The First People's Hospital of Jingzhou
First Affiliated Hospital, Sun Yat-Sen University
Investigators
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Study Chair: Qinglei Gao, MD, PhD Tongji Hospital

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Responsible Party: Qinglei Gao, Clinical Professor, Tongji Hospital
ClinicalTrials.gov Identifier: NCT04214782    
Other Study ID Numbers: 2019-TJ-OVAB
First Posted: January 2, 2020    Key Record Dates
Last Update Posted: January 2, 2020
Last Verified: December 2019
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Yes
Plan Description: contact Prof. Gao for detialed study protocol or data after study completed by e-mail
Supporting Materials: Study Protocol
Statistical Analysis Plan (SAP)
Time Frame: 6 monthes after study completed
Access Criteria: all investigators in this study field can contact Prof. Gao for access by e-mail

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
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Ovarian Neoplasms
Carcinoma, Ovarian Epithelial
Endocrine Gland Neoplasms
Neoplasms by Site
Neoplasms
Ovarian Diseases
Adnexal Diseases
Genital Diseases, Female
Genital Neoplasms, Female
Urogenital Neoplasms
Endocrine System Diseases
Gonadal Disorders
Carcinoma
Neoplasms, Glandular and Epithelial
Neoplasms by Histologic Type