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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.
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.
Age
18 - 80 years
Sex
FEMALE
Healthy Volunteers
No
Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
Shanghai, Shanghai Municipality, China
Start Date
September 1, 2021
Primary Completion Date
June 30, 2024
Completion Date
June 30, 2024
Last Updated
October 17, 2023
1,000
ESTIMATED participants
Hematologic features
DIAGNOSTIC_TEST
Lead Sponsor
RenJi Hospital
NCT07432633
NCT05867251
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