Ovarian Cancer Individualized Scoring System Scoring System (OCISS)

Assiut University

Status: Not yet enrolling

Conditions: Ovarian Cancer

Study type: Observational

Funder types: Other

Identifiers

NCT05496517

MOGGE-GO03

Details and patient eligibility

About

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.

Full 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.

Enrollment: 1,000 estimated patients

Sex: Female

Ages: 18 to 80 years old

Volunteers: Accepts Healthy Volunteers

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.