Assessment of Ovarian Cysts Using Machine Learning (OCID)

August 22, 2022 updated by: Sherif Abdelkarim Mohammed Shazly, Assiut University

Prediction of Malignant Potential of Ovarian Cysts Using Machine Learning Models

The study aims at creating a prediction model using machine learning algorithms that is capable of predicting malignant potential of ovarian cysts/masses based on patient characteristics, sonographic findings, and biochemical markers

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Ovarian cysts are one of the most common gynecologic disorders encountered in clinical practice. Approximately 20% of women may experience ovarian cysts at least once in their lifetime. However, incidence of significant ovarian cysts is 8% in premenopausal. In fact, many ovarian cysts are discovered incidentally while pelvic imaging is done for other indications. Interestingly, prevalence of ovarian cysts may reach up to 14-18% in menopausal women, many of which are likely persistent (2). Although most ovarian cysts are benign, definitive diagnosis cannot be made based on one time sonographic findings. Simple cysts are typically benign. Complex and solid cysts are still likely benign. However, malignancy is more common in this group of cysts. Definitive diagnosis by histopathology warrants surgical removal of the cyst/ovary. Because the condition is common and is mostly benign, surgery is not considered unless malignancy is reasonably a concern or the cyst is symptomatic.

Therefore, most ovarian cysts are expectantly managed. Aim of expectant management is to determine cyst changes. Follow-up may extend beyond a year. However, recommendations have not been consistent among internationally recognized guidelines, and different cut-offs of cyst size and different frequencies and durations of follow-up were considered (5, 6). Similarly, there are different systems that are adopted by these guidelines to triage women with ovarian cysts based on sonographic and biochemical indicators.

This project aims at creating a prediction model using machine learning algorithms that can be applied to women with ovarian cysts. The aim of this mode is to determine probability of cancer and management plan including surgery, long-term or short-term follow-up.

Retrieved records will be reviewed for eligibility. Patients will be considered for inclusion if they are postmenarchal, have documented follow-up for at least 1 year following initial presentation unless surgically managed, and provide authorization to use their medical records for research purposes. They should have received their care in the receptive centers. Women will be excluded from the study if they were admitted for an acute event including cyst torsion, rupture or hemorrhage with no prior documentation of ovarian cysts. Women with cysts smaller than 3 cm will not be eligible.

A standardized data collection spreadsheet is designed for the purpose of the study and will be shared with all contributing centers. Data collection will include patient demographics (e.g., age, parity, body mass index, ethnicity, smoking status), gynecologic history (e.g., menstrual abnormalities, contraceptive status), medical history (e.g., including chronic health issues and personal history of cancers), surgical history, family history of cancers including any diagnosed familial cancer syndromes. Specific information on current presentation will comprise presenting symptoms, if any, relevant physical signs, sonographic features (e.g., cyst size, side, consistency, locularity, presence of septa, solid areas, papillae, intracystic fluid texture, associated pelvic fluid or ascites), features noted in other imaging modalities if any, tumor markers (CA125, HCG, ALP, LDH,HE-4), management plan including surgical findings and histopathological diagnosis, follow-up including follow-up findings and cyst/mass complications during follow-up.

Study Type

Observational

Enrollment (Anticipated)

1000

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 Locations

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

15 years to 80 years (Child, Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

Female

Sampling Method

Probability Sample

Study Population

Any female who are postmenarchal, have documented follow-up for at least 1 year following initial presentation unless surgically managed, and provide authorization to use their medical records for research purposes. They should have received their care in the receptive centers.

Description

Inclusion Criteria:

Females who are postmenarchal, have documented follow-up for at least 1 year following initial presentation unless surgically managed, and provide authorization to use their medical records for research purposes. They should have received their care in the receptive centers

Exclusion Criteria:

Women will be excluded from the study if they were admitted for an acute event including cyst torsion, rupture or hemorrhage with no prior documentation of ovarian cysts. Women with cysts smaller than 3 cm will not be eligible.

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

  • Observational Models: Cohort
  • Time Perspectives: Retrospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Final diagnosis of ovarian cyst type
Time Frame: Within 3 years of diagnosis of ovarian cyst
Diagnosis of whether the cyst is benign or malignant based on histopathology, or cyst resolution or shrinkage on follow-up
Within 3 years of diagnosis of ovarian cyst

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Incidence of acute events during follow-up and prior to final diagnosis.
Time Frame: Within 3 years of diagnosis of ovarian cyst
Incidence of ovarian torsion, cyst rupture and intra-abdominal hemorrhage requiring surgical intervention
Within 3 years of diagnosis of ovarian cyst

Collaborators and Investigators

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

Sponsor

Assiut University

Investigators

  • Study Director: Sherif Shazly, MSc, Assiut University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

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)

June 1, 2023

Study Completion (Anticipated)

September 1, 2023

Study Registration Dates

First Submitted

April 16, 2022

First Submitted That Met QC Criteria

April 16, 2022

First Posted (Actual)

April 22, 2022

Study Record Updates

Last Update Posted (Actual)

August 23, 2022

Last Update Submitted That Met QC Criteria

August 22, 2022

Last Verified

August 1, 2022

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • MCOG-AI02

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

Undecided

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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