Assessment of Ovarian Cysts Using Machine Learning

Study Overview
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Brief Summary
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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
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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.

Official Title
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Prediction of Malignant Potential of Ovarian Cysts Using Machine Learning Models

Conditions
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Ovarian Cyst

Intervention / Treatment
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* Other: prediction model


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 15 Years
Maximum Age: 80 Years

Sexes Eligible for Study
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Female

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Intervention/Treatment |
| --- |
|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.|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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machine learning, ovarian masses, ovarian cystectomy