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Ovarian CancerOCISSNCT05496517

Ovarian Cancer Individualized Scoring System Scoring System

An observational study of Ovarian Cancer, run by Assiut University at 2 sites in Egypt.

Unknown status

Status not confirmed. The sponsor has not verified this record for over two years, so it may be out of date.

Official record
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Trial summary

This project aims at creating an individualized prognostic model using patient characteristics and disease features to determine disease prognosis using machine learning technology. The model can be used to determine the optimal management plan per patient in priori and highlight risk and timing of disease recurrence. Read more about this study

Who is it for?
Ages 18 to 80 years, women onlyHealthy volunteers can take part
Conditions the study is for
Ovarian Cancer
How many people
1,000 participants
Where
2 sitesEgypt
Study dates
11 Nov 2022to 22 Nov 2023
Phase
Observational
Are there results?
None posted in the registry

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages 18 to 80 years
  • Women only
  • Healthy volunteers accepted
  • Adult, Older adult

Study population: All women who will be diagnosed with primary ovarian cancer at any stage, of all histological types and grades eligible for the study

Who may be able to join

  • Women diagnosed with ovarian cancer between January 2010 and December 2016.
  • Primary non-recurrent diagnosis of ovarian cancer.
  • Women should be diagnosed and managed by the corresponding center.
  • Patients with adequate clinical and pathological data

Who cannot join

  • • Inadequate information and follow-up for at least 5 years.
  • Authorization to use anonymous patient data for research purposes.

Trial locations and contacts

2 sites in 1 country.

Egypt2 sites
  • Alexandria University Main Hospital

    Alexandria, 21516

    Ahmed H. Ismail, 01144557597, ahmed.ismail.mogge@gmail.com

  • Assiut Hospitals university

    Asyut, 71511

    Manar M. Ahmed, 01128793950, Manar.mahran.mogge@gmail.com

Other trials in Egypt

About this study

Official title: Ovarian Cancer Individualized Scoring System (OCISS) for Prediction of Ovarian Cancer Prognosis

This project aims at creating an individualized prognostic model using patient characteristics and disease features to determine disease prognosis using machine learning technology. The model can be used to determine the optimal management plan per patient in priori and highlight risk and timing of disease recurrence.

Read the detailed description

Ovarian cancer (OC) is one of the most common types of malignant tumors and the eighth cause of cancer-related mortality in women.[1] Among gynecological cancers, it is ranked the third following cervical and uterine cancers and is associated with the worst prognosis

[1]. Globally, there are 313,959 new cases and 207,252 deaths of OC annually [1].

Compared to breast cancer, OC is approximately three times more lethal [2]. The high mortality rate of OC is attributed to the capacious anatomical space through which the tumor can grow before it causes significant symptoms, growth of the tumor within abdominal cavity rendering spread of malignant cells widespread and prompt, direct lymphatic drainage to aortic lymph nodes, lack of specific diagnostic symptoms, and unavailability of an efficient screening strategy [3,4]. Symptoms of OC are nonspecific and include vague abdominal pain, abdominal bloating, urinary frequency, early satiety, feeling full, or changes in bowel habits, most of which mimic common gastrointestinal symptoms [5]. Risk factors of OC include obesity, old age, smoking, genetic predisposition, and endometriosis [6,7]. FIGO staging is considered the standard classification system that determines prognosis and management of newly diagnosed OC. However, there are numerous gaps in this staging system that would limit interpretation of clinically relevant data [8]. For instance, the staging system does not consider crucial disease prognostic factors, such as histological type and grade, which are usually considered separately based on available evidence and internal policies. This multi-layer guidance adds to the complexity of decision making. Similarly, personalized management is overlooked since these staging systems do not appreciate individual characteristics such as age, menopausal states, comorbidities, and genetic predisposition. All patients with positive lymph nodes are grouped into a single stage in FIGO staging system, which creates a very diverse group of patients with highly variable survival rates [9]. Management of ovarian cancer is surgical and comprises bilateral sapling-oophorectomy, total abdominal hysterectomy , and infracolic omentectomy. Additional surgical steps and neoadjuvant therapy are potentially determined by disease characteristics. Extent of surgery and neoadjuvant treatment is directly related to postoperative comorbidities and contributes to long term prognosis.

[10]. Therefore, development of an individualized prognostic and decision-making system, based on large multicenter studies, would facilitate accurate prediction of disease prognosis and determination of individualized management strategy.

The study will comprise at least 8 international cancer centers. Data of patients, newly diagnosed with OC between January 2010 and December 2016, will be retrospectively collected. Therefore, a follow-up of at least 5 years would be granted. All women who will be diagnosed with primary ovarian cancer at any stage, of all histological types and grades eligible for the study. All contributing centers should acquire institutional review board (IRB) approval prior to data collection.

Inclusion criteria:

  • Women diagnosed with ovarian cancer between January 2010 and December 2016.
  • Primary non-recurrent diagnosis of ovarian cancer.
  • Women should be diagnosed and managed by the corresponding center.
  • Patients with adequate clinical and pathological data

Exclusion criteria:

  • Inadequate information and follow-up for at least 5 years.
  • Authorization to use anonymous patient data for research purposes. Data will be collected using an excel spreadsheet designed for this study and shared among contributing centers. Data include patients' demographics such as age, parity, body mass index, ethnicity, smoking index, contraception method, menopausal status, medical comorbidities [coronary artery disease, diabetes on insulin, hypertension, chronic renal 3 disease, chronic lung disease, thyroid dysfunction], preoperative imaging [cancer stage, involvement of ovaries, surface involvement, uterine involvement, tubal involvement, inguinal lymph nodes (number, largest diameter), extra abdominal lymph nodes (size and enlargement), abdominal invasion (omental deposits > 2cm, peritoneal carcinomatosis), other pelvic invasion], positive cytology, grade (high/low), pleural effusion and cytology, ascites, performance status, histological type, biomarkers, BRCA I and II (germline or somatic), and serum albumin level. Details of management plan will be collected including treatment approach [Time from diagnosis to surgery, Surgical approach, PA lymphadenectomy (systematic, selective, none)], chemotherapy [systematic or intraperitoneal], and other treatments given.

Treatment outcomes such as complications, debulking success, spill, nodal metastasis, microscopic peritoneal metastasis, microscopic omental metastasis, response to chemotherapy, and CA 125 changes will be included. Data will not include any identifiable information.

Conditions
Ovarian Cancer
Keywords
ovarian neoplasm
Medical subject headings
Ovarian Neoplasms

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Cancer-specific survival (CSS) rate at 5 years

    Percentage of women newly diagnosed with ovarian cancer who do not die from ovarian cancer after 5 years

    Measured: Within 5 years after diagnosis of ovarian cancer

  2. Cancer-specific survival (CSS) rate at 3 years

    Percentage of women newly diagnosed with ovarian cancer who do not die from ovarian cancer after 3 years

    Measured: Within 3 years after diagnosis of ovarian cancer

Secondary outcomes (2)
  1. Recurrence-free survival (RFS) rate at 5 years

    Percentage of newly diagnosed women who do not experience disease recurrence during follow-up

    Measured: Within 5 years of diagnosis of ovarian cancer

  2. Recurrence-free survival (RFS) rate at 3 years

    Percentage of newly diagnosed women who do not experience disease recurrence during follow-up

    Measured: Within 3 years of diagnosis of ovarian cancer

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted11 Aug 2022
  2. Study start11 Nov 2022 (estimated)
  3. Primary completion11 Aug 2023 (estimated)
  4. Study completion22 Nov 2023 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Cohort
Time perspective
Retrospective
Enrolment
1,000 participants (estimated)
IDs, dates and publications
ClinicalTrials.gov ID
NCT05496517
Sponsor's protocol ID
MOGGE-GO03
Acronym
OCISS
Status last verified
Aug 2022
First submitted
9 Aug 2022
Last updated
11 Aug 2022
Results posted
No
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
Undecided

Publications

  • Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.

    PubMed 33538338
  • Caan BJ, Thomson CA. Breast and ovarian cancer. Optim Women's Heal through Nutr. Published online 2007:229-263. doi:10.1369/0022155411428469

  • Urban N. Early detection of ovarian cancer: Methodological considerations. Int J Gynecol Obstet. 2000;70:D9-D9. doi:10.1016/s0020-7292(00)82512-6

  • Jacobs IJ, Menon U. Progress and challenges in screening for early detection of ovarian cancer. Mol Cell Proteomics. 2004 Apr;3(4):355-66. doi: 10.1074/mcp.R400006-MCP200. Epub 2004 Feb 5.

    PubMed 14764655
  • Goff BA, Mandel LS, Melancon CH, Muntz HG. Frequency of symptoms of ovarian cancer in women presenting to primary care clinics. JAMA. 2004 Jun 9;291(22):2705-12. doi: 10.1001/jama.291.22.2705.

    PubMed 15187051
  • Jordan SJ, Green AC, Whiteman DC, Webb PM; Australian Ovarian Cancer Study Group. Risk factors for benign, borderline and invasive mucinous ovarian tumors: epidemiological evidence of a neoplastic continuum? Gynecol Oncol. 2007 Nov;107(2):223-30. doi: 10.1016/j.ygyno.2007.06.006. Epub 2007 Jul 27.

    PubMed 17662378
  • Momenimovahed Z, Tiznobaik A, Taheri S, Salehiniya H. Ovarian cancer in the world: epidemiology and risk factors. Int J Womens Health. 2019 Apr 30;11:287-299. doi: 10.2147/IJWH.S197604. eCollection 2019.

    PubMed 31118829
  • Salvo G, Odetto D, Pareja R, Frumovitz M, Ramirez PT. Revised 2018 International Federation of Gynecology and Obstetrics (FIGO) cervical cancer staging: A review of gaps and questions that remain. Int J Gynecol Cancer. 2020 Jun;30(6):873-878. doi: 10.1136/ijgc-2020-001257. Epub 2020 Apr 1.

    PubMed 32241876
  • Wright JD, Matsuo K, Huang Y, Tergas AI, Hou JY, Khoury-Collado F, St Clair CM, Ananth CV, Neugut AI, Hershman DL. Prognostic Performance of the 2018 International Federation of Gynecology and Obstetrics Cervical Cancer Staging Guidelines. Obstet Gynecol. 2019 Jul;134(1):49-57. doi: 10.1097/AOG.0000000000003311.

    PubMed 31188324
  • McCorkle R, Pasacreta J, Tang ST. The silent killer: psychological issues in ovarian cancer. Holist Nurs Pract. 2003 Nov-Dec;17(6):300-8. doi: 10.1097/00004650-200311000-00005.

    PubMed 14650572

Questions about this trial

What is this clinical trial studying?

An observational study of Ovarian Cancer, run by Assiut University at 2 sites in Egypt.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: status not confirmed, as of the sponsor's last update on 11 Aug 2022. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 2 sites in Egypt. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

Results are not available in the source record.

How can a doctor refer a patient or contact the research team?

Contact Sherif Shazly (+4407554480388, sherif.shazly.mogge@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Cancer-specific survival (CSS) rate at 5 years (time frame: Within 5 years after diagnosis of ovarian cancer). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT05496517, which also lists 10 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 11 Aug 2022.

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OCISS Clinical Trial NCT05496517 | AzTrials