Ovarian CancerOCISSNCT05496517
Ovarian Cancer Individualized Scoring System Scoring System
An observational study of Ovarian Cancer, run by Assiut University at 2 sites in Egypt.
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
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.
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
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)
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
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.
- First posted11 Aug 2022
- Study start11 Nov 2022 (estimated)
- Primary completion11 Aug 2023 (estimated)
- Study completion22 Nov 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (10)Papers the sponsor has linked to this study.
- Other trials for Ovarian CancerStudies of the same condition, open and under way.
- More trials run by Assiut UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
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)
Sponsor and investigators
- Lead sponsor
- Assiut University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Sherif Abdelkarim Mohammed Shazly, Assiut University
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 33538338Caan 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 14764655Goff 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 15187051Jordan 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 17662378Momenimovahed 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 31118829Salvo 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 32241876Wright 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 31188324McCorkle 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
Study at a glance
From the registry record, last updated by the sponsor on 11 Aug 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Ovarian Cancer Individualized Scoring System (OCISS) for Prediction of Ovarian Cancer Prognosis
- ClinicalTrials.gov ID
- NCT05496517
- Acronym / sponsor ID
- OCISS / MOGGE-GO03
- Conditions
- Ovarian Cancer
- Keywords
- ovarian neoplasm
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Assiut UniversityUniversity, hospital or other
- Study start
- 11 Nov 2022 (estimated)
- Primary completion
- 11 Aug 2023 (estimated)
- Study completion
- 22 Nov 2023 (estimated)
- First posted
- 11 Aug 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 to 80 years
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- 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
Key inclusion criteria (4 listed)
- Women diagnosed with ovarian cancer between January 2010 and December 2016.
Key exclusion criteria (2 listed)
- • Inadequate information and follow-up for at least 5 years.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Cancer-specific survival (CSS) rate at 5 years
Time frame: Within 5 years after diagnosis of ovarian cancer
Percentage of women newly diagnosed with ovarian cancer who do not die from ovarian cancer after 5 years
Cancer-specific survival (CSS) rate at 3 years
Time frame: Within 3 years after diagnosis of ovarian cancer
Percentage of women newly diagnosed with ovarian cancer who do not die from ovarian cancer after 3 years
2 secondary outcomes
Recurrence-free survival (RFS) rate at 5 years
Time frame: Within 5 years of diagnosis of ovarian cancer
Recurrence-free survival (RFS) rate at 3 years
Time frame: Within 3 years of diagnosis of ovarian cancer
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Aug 2022.
- Background: PubMed 33538338Sung 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.
- BackgroundCaan BJ, Thomson CA. Breast and ovarian cancer. Optim Women's Heal through Nutr. Published online 2007:229-263. doi:10.1369/0022155411428469
- BackgroundUrban N. Early detection of ovarian cancer: Methodological considerations. Int J Gynecol Obstet. 2000;70:D9-D9. doi:10.1016/s0020-7292(00)82512-6
- Background: PubMed 14764655Jacobs 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.
- Background: PubMed 15187051Goff 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.
- Background: PubMed 17662378Jordan 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.
- Background: PubMed 31118829Momenimovahed 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.
- Background: PubMed 32241876Salvo 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.
- Background: PubMed 31188324Wright 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.
- Background: PubMed 14650572McCorkle 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.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
2 sites in 1 country: see every site and its status.
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.