To hear about similar clinical trials, please enter your email below
Trial Title:
Individualized Health Management of Epithelial Ovarian Cancer: A Retrospective Study
NCT ID:
NCT06085456
Condition:
Epithelial Ovarian Cancer
Conditions: Official terms:
Ovarian Neoplasms
Carcinoma, Ovarian Epithelial
Conditions: Keywords:
Epithelial Ovarian Cancer
Diagnosis
Prognosis
Study type:
Observational
Overall status:
Recruiting
Study design:
Time perspective:
Retrospective
Intervention:
Intervention type:
Diagnostic Test
Intervention name:
Hematologic features
Description:
Hematologic features including blood routine tests, blood biochemical indicators, and
tumor markers before surgery
Arm group label:
Control group
Arm group label:
EOC group
Summary:
The purpose of this study is to identify the demographic and sociological characteristics
of epithelial ovarian cancer in a cohort, identify the risk factors of epithelial ovarian
cancer, effectively identify the high-risk population of epithelial ovarian cancer in the
population, implement standardized health management, and clarify the effect of
standardized health management on the incidence and prognosis of epithelial ovarian
cancer. It can also provide a case control population for the clinical cohort of
epithelial ovarian cancer to benefit the majority of postoperative patients.
Detailed description:
1. The clinical characteristics, preoperative hematological parameters of patients with
epithelial ovarian cancer and patients with benign gynecological diseases, and the
pathological stage, grade and features extracted by PET/CT images of patients with
epithelial ovarian cancer were recorded.
2. Patients from Renji Hospital were divided into training group and test group at a
ratio of 7:3, and patients from Shanghai First Maternity and Infant Hospital were
used as external validation group.
3. The training group was used to establish the diagnosis and prognosis prediction
model of epithelial ovarian cancer, and the test group and the external validation
group were used to verify the model, and the area under the ROC curve, accuracy,
specificity, and sensitivity were used to evaluate the effect of the model.
4. For machine learning models, SHAP and LIME algorithms were used for model
interpretation.
5. Unsupervised clustering algorithm was used to distinguish the subgroups of
epithelial ovarian cancer patients, and KM was used to analyze the overall survival
(OS) and progression-free survival (PFS) to predict the survival and recurrence of
the subgroups. Overall survival (OS) was defined as the time from the first
diagnosis of epithelial ovarian cancer to the confirmation of death or the end of
follow-up. Progression-free survival (PFS) was defined as the time from the first
diagnosis of epithelial ovarian cancer to the confirmation of disease progression or
the end of follow-up.
Criteria for eligibility:
Study pop:
Patients diagnosed as primary epithelial ovarian cancer or patients diagnosed as benign
gynecological diseases including ovarian cysts, uterine fibroids, and uterine prolapse
Sampling method:
Probability Sample
Criteria:
Inclusion Criteria:
- Patients were diagnosed as primary epithelial ovarian cancer with definite
pathological stage and grade and underwent preoperative PET/CT examination, or
patients diagnosed as benign gynecological diseases including ovarian cysts, uterine
fibroids, and uterine prolapse.
- age between 18 to 80 years old;
- complete preoperative blood routine test results, blood biochemical indicators, and
tumor markers;
Exclusion Criteria:
- complicated with acute or chronic genital tract infectious diseases;
- patients with diagnosed tumors other than ovarian cancer;
- complicated with severe systemic diseases;
- pregnant or lactating women;
- patients diagnosed with recurrent epithelial ovarian cancer.
Gender:
Female
Minimum age:
18 Years
Maximum age:
80 Years
Healthy volunteers:
No
Locations:
Facility:
Name:
Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
Address:
City:
Shanghai
Zip:
200127
Country:
China
Status:
Recruiting
Contact:
Last name:
Sijia Gu
Phone:
86+15021845201
Email:
gusijia47@163.com
Start date:
September 1, 2021
Completion date:
June 30, 2024
Lead sponsor:
Agency:
RenJi Hospital
Agency class:
Other
Source:
RenJi Hospital
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT06085456
