NCT05342298

Brief Summary

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

Trial Health

43
At Risk

Trial Health Score

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

Trial has exceeded expected completion date
Enrollment
1,000

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Oct 2022

Shorter than P25 for all trials

Geographic Reach
1 country

2 active sites

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

April 16, 2022

Completed
6 days until next milestone

First Posted

Study publicly available on registry

April 22, 2022

Completed
5 months until next milestone

Study Start

First participant enrolled

October 1, 2022

Completed
8 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

June 1, 2023

Completed
3 months until next milestone

Study Completion

Last participant's last visit for all outcomes

September 1, 2023

Completed
Last Updated

August 23, 2022

Status Verified

August 1, 2022

Enrollment Period

8 months

First QC Date

April 16, 2022

Last Update Submit

August 22, 2022

Conditions

Ovarian Cyst

Keywords

machine learning, ovarian masses, ovarian cystectomy

Outcome Measures

Primary Outcomes (1)

  • Final diagnosis of ovarian cyst type

    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 Outcomes (1)

  • Incidence of acute events during follow-up and prior to final diagnosis.

    Within 3 years of diagnosis of ovarian cyst

Interventions

prediction modelOTHER

Data will be pre-processed prior to final analysis, including data cleaning, imputation of missing values, dimensionality reduction, and removal of outliers. Data will be utilized as Xi and Yi where Xi presents input (features) and Yi presents dependent variables (outcomes). Different classification algorithms will be tested for accuracy to build the final model including logistic regression, SVM, XGboost and random forest algorithms. Data will be split at 0.8:0.2 for model training and testing, respectively.

Eligibility Criteria

Age15 Years - 80 Years
Sexfemale(Gender-based eligibility)
Healthy VolunteersYes
Age GroupsChild (0-17), Adult (18-64), Older Adult (65+)
Sampling MethodProbability 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.

You may qualify if:

  • 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

You may not qualify if:

  • 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.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (2)

Alexandria University Main Hospital

Alexandria, 21516, Egypt

Location

Assiut University

Asyut, 71511, Egypt

Location

Related Publications (6)

  • Ross EK, Kebria M. Incidental ovarian cysts: When to reassure, when to reassess, when to refer. Cleve Clin J Med. 2013 Aug;80(8):503-14. doi: 10.3949/ccjm.80a.12155.

    PMID: 23908107BACKGROUND

  • Mobeen S, Apostol R. Ovarian Cyst. 2023 Jun 5. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan-. Available from http://www.ncbi.nlm.nih.gov/books/NBK560541/

    PMID: 32809376BACKGROUND

  • Boos J, Brook OR, Fang J, Brook A, Levine D. Ovarian Cancer: Prevalence in Incidental Simple Adnexal Cysts Initially Identified in CT Examinations of the Abdomen and Pelvis. Radiology. 2018 Jan;286(1):196-204. doi: 10.1148/radiol.2017162139. Epub 2017 Sep 14.

    PMID: 28914598BACKGROUND

  • Farghaly SA. Current diagnosis and management of ovarian cysts. Clin Exp Obstet Gynecol. 2014;41(6):609-12.

    PMID: 25551948BACKGROUND

  • Shazly, S.; Laughlin-Tommaso, S.K. Ovarian Tumors. In Gynecology: A CREOG and Board Exam Review; Springer International Publishing: Cham, Switzerland, 2020; pp. 489-519.

    BACKGROUND

  • Mehasseb MK, Siddiqui NA, Bryden F. The Management of Ovarian Cysts in Postmenopausal Women. Royal College of Obstetricians and Gynaecologist. RCOG Green-top Guideline. 2016;34:1-31.

    BACKGROUND

MeSH Terms

Conditions

Ovarian Cysts

Condition Hierarchy (Ancestors)

CystsNeoplasmsOvarian DiseasesAdnexal DiseasesGenital Diseases, FemaleFemale Urogenital DiseasesFemale Urogenital Diseases and Pregnancy ComplicationsUrogenital DiseasesGenital DiseasesGonadal DisordersEndocrine System Diseases

Study Officials

  • Sherif Shazly, MSc

    Assiut University

    STUDY DIRECTOR

Central Study Contacts

Sherif Shazly, MSc

CONTACT

+4407554480388sherif.shazly.mogge@gmail.com

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Assistant lecturer

Study Record Dates

First Submitted

April 16, 2022

First Posted

April 22, 2022

Study Start

October 1, 2022

Primary Completion

June 1, 2023

Study Completion

September 1, 2023

Last Updated

August 23, 2022

Record last verified: 2022-08

Locations

EG(2)