Predictors of Ovarian Cancer and Endometrial Cancer for Artificial-Intelligence-Based Screening Tools

November 24, 2023 updated by: Bumi Herman, Hasanuddin University

Associated Factors of Ovarian Cancer and Endometrial Cancer in Indonesia. A Study for Developing Artificial-Intelligence-Based Screening Tools

The goal of this observational study is to explore the possible associated factors of ovarian cancer and endometrial cancer in Indonesia and develop screening tools that could predict the risk of both types of cancer

The specific objectives of the study are

  1. Elaborating the situation of ovarian and endometrial cancer in Indonesia
  2. Exploring the possible clinical, demography and laboratory predictors of these diseases
  3. Develop artificial-intelligence-based screening tools for both type of cancer based on possible predictors

This study will utilize the patient registry diagnosed with ovarian and endometrial cancer. We assumed that several demography, clinical, and laboratory predictors might possess good screening performance with higher sensitivity and specificity (>80%).

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Methodology :

This study will involve two different stages

  1. The first stage will conduct a cohort study to identify the possible predictors of each type of cancer
  2. The second stage will cover the development of point-of-care testing based on an artificial intelligence model to predict cancer occurrence and prospective testing of the new participants using a diagnostic study method. The tools will predict the current histopathology result and possible future histopathology within one year.

Participants and source of data In the study centre, women with or without gynaecology-associated symptoms underwent gynaecological and pathology assessments to rule out ovarian and endometrial cancer in our study centre were involved. Data is stored digitally and extraction will be done accordingly

Variables and outcome measurement

  1. Demographic data and health data this information is obtained from the initial assessment of the patients including age, body mass index, chronic diseases, gynaecological and obstetric profile, menstrual pattern, and contraception
  2. Clinical and laboratory data this include, a complete blood count, selected cancer-associated biomarker (for example Cancer Antigen 125 (Ca-125)), involvement of lymph node, histopathology of pertinent tissues, and signs of metastases through clinical or radiological data
  3. Outcome final histopathology type and classification assessed by at least two pathologists to determine the type of cancer. The guidelines of classification follow the World Health Organization's classification

Development of Artificial-Intelligence-based screening tools

  1. The researcher will develop

    - an information-based model where the user will provide a response to each predictor

    - an image-based model where the user will provide a captured image for prediction

    - a mixed-based model where the user can combine captured images and information for each predictor

  2. proposed model

    - scoring-based derived from the coefficient of regression

    - decision tree

    - random forest

    - artificial neural network

    • convolutional neural network
  3. Selection of model
  1. Screening performance on split data (or using cross-validation technique)

  2. evaluation of log-loss or likelihood

    Timeline

    1. For the first stage of the study, there will be a time-varying assessment for each participant, however, at least participants undergo an Assessment of all factors and outcomes at baseline. Repeated evaluation as suggested by the physician will be done within one year after the baseline assessment.

    2. The second study will apply prospective screening. The artificial intelligence-based screening tool will be used concurrently with the gold standard of diagnosis.

    Possible Bias procedural bias particularly in reliability outcome interpretation is handled by involving multiple pathologists. The pathologist and the screener will perform the screening independently to reduce the tendency of prior results provided by the newly-developed screening tools.

    Sample size

    1. The first stage of the research assumes that

a. The prevalence of both cancer among all cancers in women accounted for 5% b. Type I error set at 5% c. absolute error of the prevalence 1% using the one-sample proportion formula, the estimated sample size is 1825 participants.

2. Following the diagnostic study, we state that the new screening tools model will show non-inferiority performance to histopathology as gold-standard, assuming that

a. the expected difference in sensitivity value is 5% assuming that the new screening tools will possess 85% sensitivity and the sensitivity of histopathology is 90% b. cross-over testing will be done, creating an equal allocation of screening intervention c. Type 1 error of the study set at 5% d. Power of the study set at 80% the total sample size for the prospective screening tool will be 1080 participants

Data Quantification and discretization several clinical information will be classified according to the established guideline for example body mass index.

Proposed Statistical Analysis

  1. Descriptive statistic and bivariate analysis
  2. A cox-regression will be conducted following the baseline-to-event timeline
  3. Subgroup analysis will be done, particularly in certain demographic and comorbidity.

as for the second stage, the analysis will identify the

  1. sensitivity
  2. specificity
  3. accuracy
  4. precision
  5. The number Needed to Treat selected models will be deployed into an application.

Study Type

Observational

Enrollment (Estimated)

2905

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Indonesia
    • South Sulawesi
      • Makassar, South Sulawesi, Indonesia, 90245
        • Recruiting
        • Hasanuddin University Hospital
        • Contact:
        • Principal Investigator:
          • Rina Masadah, MD PhD

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

As this study is utilizing a patient registry, we will involve all eligible participants who undergo gynaecological and pathology assessment for ovarian and endometrial cancer in study centres, based on suggestive signs and symptoms

Description

Inclusion Criteria:

  • Women with gynaecological symptoms but not limited to

    1. Irregular menstruation
    2. Heavy bleeding during menstruation
    3. pelvic pain
    4. vaginal discharge
    5. sudden weight loss
    6. pain during sexual intercourse
  • Women who underwent routine gynaecological examination

Exclusion Criteria:

  • unable to undergo serial gynaecological follow-up

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Retrospective

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Suspect of Ovarian Cancer
The participant with high suspicion of ovarian cancer and undergo gynaecology and pathology assessment
Diagnostic test: Artificial-Intelligence Based Screening Tools
Artificial-Intelligence Based Screening Tools build on machine learning models
Diagnostic test: Pathology analysis
Pathology assessment of cells and tissues from respective organs
Suspect of Endometrial Cancer
The participant with high suspicion of Endometrial cancer (and or endometrial hyperplasia) and undergo gynaecology and pathology assessment
Diagnostic test: Artificial-Intelligence Based Screening Tools
Artificial-Intelligence Based Screening Tools build on machine learning models
Diagnostic test: Pathology analysis
Pathology assessment of cells and tissues from respective organs
Normal Cohort
The participant with lower suspicion of both types of cancer and undergo gynaecology and pathology assessment
Diagnostic test: Artificial-Intelligence Based Screening Tools
Artificial-Intelligence Based Screening Tools build on machine learning models
Diagnostic test: Pathology analysis
Pathology assessment of cells and tissues from respective organs

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Number of People developing ovarian cancer
Time Frame: from baseline to twelve month after entering cohort
Number of people developing ovarian cancer diagnosed with gynaecology and pathology assessment
from baseline to twelve month after entering cohort
Number of People developing endometrial cancer
Time Frame: from baseline to twelve month after entering cohort
Number of people developing endometrial cancer diagnosed with gynaecology and pathology assessment
from baseline to twelve month after entering cohort

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Screening Performance of Artificial-Intelligence-based Screening tools
Time Frame: from baseline assessment up to one year
The sensitivity, specificity, accuracy, precision of selected Artificial-Intelligence-based model to predict the ovarian and/or endometrial cancer
from baseline assessment up to one year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Hasanuddin University

Collaborators

Chulalongkorn University

Investigators

  • Study Chair: Rina Masadah, Ph.D, Hasanuddin University
  • Principal Investigator: Bumi Herman, Ph.D, Chulalongkorn University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

February 28, 2023

Primary Completion (Estimated)

February 28, 2024

Study Completion (Estimated)

June 30, 2024

Study Registration Dates

First Submitted

January 14, 2023

First Submitted That Met QC Criteria

January 14, 2023

First Posted (Actual)

January 26, 2023

Study Record Updates

Last Update Posted (Actual)

November 27, 2023

Last Update Submitted That Met QC Criteria

November 24, 2023

Last Verified

November 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 0901231327

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

The individual participant data will be shared after de-identification and the purpose of the data utilization is verified by the investigators

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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