Ovarian Cancer Individualized Scoring System Scoring System

NCT ID: NCT05496517

Last Updated: 2022-08-11

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 1000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-11-11
• Study Completion Date: 2023-11-22

Brief 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.

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

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Eligibility Criteria

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.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: Yes

Sponsors

Assiut University

OTHER

Sponsor Role: lead

Responsible Party

Sherif Abdelkarim Mohammed Shazly

Assistant lecturer

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Alexandria University Main Hospital

Alexandria, , Egypt

Assiut Hospitals university

Asyut, , Egypt

Countries

Egypt

Central Contacts

Sherif Shazly

Role: CONTACT

sherif.shazly.mogge@gmail.com

+4407554480388

Facility Contacts

Ahmed H. Ismail

Role: primary

ahmed.ismail.mogge@gmail.com

01144557597

Manar M. Ahmed

Role: primary

Manar.mahran.mogge@gmail.com

01128793950

References

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Reference Type: BACKGROUND

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Reference Type: BACKGROUND

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Reference Type: BACKGROUND

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Reference Type: BACKGROUND

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Reference Type: BACKGROUND

PMID: 15187051 (View on PubMed)

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Reference Type: BACKGROUND

PMID: 17662378 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 31118829 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 32241876 (View on PubMed)

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.

Reference Type: BACKGROUND

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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.

Reference Type: BACKGROUND

PMID: 14650572 (View on PubMed)

Other Identifiers

MOGGE-GO03

Identifier Type: -

Identifier Source: org_study_id