Clinical trial NCT06085456: Individualized Health Management of Epithelial Ovarian Cancer: A Retrospective Study
Official title: Individualized Health Management of Epithelial Ovarian Cancer: A Retrospective Study
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Epithelial Ovarian Cancer
Keywords: Epithelial Ovarian Cancer
Diagnosis
Prognosis
Lead sponsor: RenJi Hospital
Sponsor class: OTHER
First posted: 2023-10-17
Enrollment: 1000
Sex: FEMALE
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients diagnosed as primary epithelial ovarian cancer or patients diagnosed as benign gynecological diseases including ovarian cysts, uterine fibroids, and uterine prolapse

Brief 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.

Eligibility 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.

Primary outcomes:
measure: Number of diagnosed patients
timeFrame: one month after surgery
Secondary outcomes:
measure: Overall survival
timeFrame: up to 5 years
measure: Progression-free survival
timeFrame: up to 5 years
Other outcomes:
Locations:
city: Shanghai
country: China
facility: Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
state: Shanghai
Study officials:
affiliation: Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
name: Aimin Zhao, MD
role: PRINCIPAL_INVESTIGATOR
References: