Assessment of Ovarian Cysts Using Machine Learning

Ovarian Cyst Clinical Trial

— OCID  

Official title:

Prediction of Malignant Potential of Ovarian Cysts Using Machine Learning Models

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT05342298
• Other study ID #: MCOG-AI02
• Status: Not yet recruiting
• Start date: October 1, 2022
• Est. completion date: September 1, 2023

Study information

• Verified date: August 2022
• Source: Assiut University
• Contact: Sherif Shazly, MSc
• Phone: +4407554480388
• Email: sherif.shazly.mogge[@]gmail.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 1000
• Est. completion date: September 1, 2023
• Est. primary completion date: June 1, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Female
• Age group: 15 Years to 80 Years

Eligibility

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.

Related Conditions & MeSH terms

• Cysts
• Ovarian Cyst
• Ovarian Cysts

Intervention

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.

Locations

Country	Name	City	State
Egypt	Alexandria University Main Hospital	Alexandria
Egypt	Assiut University	Assiut

Sponsors (1)

Lead Sponsor	Collaborator
Assiut University

Country where clinical trial is conducted

Egypt, 

References & Publications (6)

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. — View Citation

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

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.

Mobeen S, Apostol R. Ovarian Cyst. 2022 Jun 13. StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022 Jan-. Available from http://www.ncbi.nlm.nih.gov/books/NBK560541/ — View Citation

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. Review. — View Citation

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.

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	Incidence of acute events during follow-up and prior to final diagnosis.	Incidence of ovarian torsion, cyst rupture and intra-abdominal hemorrhage requiring surgical intervention	Within 3 years of diagnosis of ovarian cyst