Clinical trial NCT05342298: Assessment of Ovarian Cysts Using Machine Learning
Official title: Prediction of Malignant Potential of Ovarian Cysts Using Machine Learning Models
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Ovarian Cyst
Keywords: machine learning, ovarian masses, ovarian cystectomy
Lead sponsor: Assiut University
Sponsor class: OTHER
First posted: 2022-04-22
Enrollment: 1000
Sex: FEMALE
Minimum age (years): 15
Maximum age (years): 80
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: RETROSPECTIVE
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.

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

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.

Eligibility 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.

Primary outcomes:
measure: Final diagnosis of ovarian cyst type
timeFrame: Within 3 years of diagnosis of ovarian cyst
Secondary outcomes:
measure: Incidence of acute events during follow-up and prior to final diagnosis.
timeFrame: Within 3 years of diagnosis of ovarian cyst
Other outcomes:
Locations:
city: Alexandria
country: Egypt
facility: Alexandria University Main Hospital
city: Assiut
country: Egypt
facility: Assiut University
Study officials:
affiliation: Assiut University
name: Sherif Shazly, MSc
role: STUDY_DIRECTOR
References:
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.
citation: 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/
citation: 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.
citation: Farghaly SA. Current diagnosis and management of ovarian cysts. Clin Exp Obstet Gynecol. 2014;41(6):609-12.
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.
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.