Trial Title:
Ovarian Cancer Individualized Scoring System Scoring System
NCT ID:
NCT05496517
Condition:
Ovarian Cancer
Conditions: Official terms:
Ovarian Neoplasms
Carcinoma, Ovarian Epithelial
Conditions: Keywords:
ovarian neoplasm
Study type:
Observational
Overall status:
Unknown status
Study design:
Time perspective:
Retrospective
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.
Criteria for eligibility:
Study pop:
All women who will be diagnosed with primary ovarian cancer at any stage, of all
histological types and grades eligible for the study
Sampling method:
Probability Sample
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.
Gender:
Female
Gender based:
Yes
Minimum age:
18 Years
Maximum age:
80 Years
Healthy volunteers:
Accepts Healthy Volunteers
Locations:
Facility:
Name:
Alexandria University Main Hospital
Address:
City:
Alexandria
Zip:
21516
Country:
Egypt
Contact:
Last name:
Ahmed H. Ismail
Phone:
01144557597
Email:
ahmed.ismail.mogge@gmail.com
Facility:
Name:
Assiut Hospitals university
Address:
City:
Assiut
Zip:
71511
Country:
Egypt
Contact:
Last name:
Manar M. Ahmed
Phone:
01128793950
Email:
Manar.mahran.mogge@gmail.com
Start date:
November 11, 2022
Completion date:
November 22, 2023
Lead sponsor:
Agency:
Assiut University
Agency class:
Other
Source:
Assiut University
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05496517
