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Assessment of Ovarian Cysts Using Machine Learning (OCID)

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ClinicalTrials.gov Identifier: NCT05342298
Recruitment Status : Not yet recruiting
First Posted : April 22, 2022
Last Update Posted : April 22, 2022
Sponsor:
Information provided by (Responsible Party):
Sherif Abdelkarim Mohammed Shazly, Assiut University

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

Condition or disease Intervention/treatment
Ovarian Cyst Other: prediction model

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.

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Study Type : Observational
Estimated Enrollment : 1000 participants
Observational Model: Cohort
Time Perspective: Retrospective
Official Title: Prediction of Malignant Potential of Ovarian Cysts Using Machine Learning Models
Estimated Study Start Date : July 1, 2022
Estimated Primary Completion Date : June 1, 2023
Estimated Study Completion Date : September 1, 2023

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Ovarian Cysts


Intervention Details:
  • Other: prediction model
    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.


Primary Outcome Measures :
  1. 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


Secondary Outcome Measures :
  1. 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



Information from the National Library of Medicine

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Ages Eligible for Study:   15 Years to 80 Years   (Child, Adult, Older Adult)
Sexes Eligible for Study:   Female
Gender Based Eligibility:   Yes
Accepts Healthy Volunteers:   Yes
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.
Criteria

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.


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): NCT05342298


Contacts
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Contact: Sherif Shazly, MSc +4407554480388 sherif.shazly.mogge@gmail.com

Locations
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Egypt
Alexandria University Main Hospital
Alexandria, Egypt, 21516
Contact: Ahmed H. Ismail    01144557597    ahmed.ismail.mogge@gmail.com   
Assiut University
Assiut, Egypt, 71511
Contact: Manar M. Ahmed    01128793950    manar.mahran.mogge@gmail.com   
Sponsors and Collaborators
Assiut University
Investigators
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Study Director: Sherif Shazly, MSc Assiut University
Publications:
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Responsible Party: Sherif Abdelkarim Mohammed Shazly, Assistant lecturer, Assiut University
ClinicalTrials.gov Identifier: NCT05342298    
Other Study ID Numbers: MCOG-AI02
First Posted: April 22, 2022    Key Record Dates
Last Update Posted: April 22, 2022
Last Verified: April 2022
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Undecided

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Sherif Abdelkarim Mohammed Shazly, Assiut University:
machine learning, ovarian masses, ovarian cystectomy
Additional relevant MeSH terms:
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Cysts
Ovarian Cysts
Neoplasms
Pathological Conditions, Anatomical
Ovarian Diseases
Adnexal Diseases
Gonadal Disorders
Endocrine System Diseases