High-grade Serous Ovarian Carcinoma (HGSOC)
Conditions
Brief summary
The subjects of this study are patients with high-grade serous ovarian cancer who have undergone surgical resection. The aim is to construct an artificial intelligence model for predicting postoperative recurrence, metastasis and overall survival of patients based on multimodal data such as surgical pathological images (HE images and IHC images of immune microenvironment-related markers), preoperative baseline imaging data and detailed clinical information of patients. In addition, multi-omics sequencing analysis will be used to deeply explore the heterogeneity of the tumor microenvironment, with the expectation of quantifying the postoperative recurrence risk of patients with high-grade serous ovarian cancer and providing new ideas for the precise diagnosis and treatment of patients with high-grade serous ovarian cancer.
Interventions
OTHERNo Intervention: Observational Cohort
This study is an retrospective observational study. There will be no interventions on the participants.
Sponsors
The Affiliated Hospital of Qingdao University
Shandong Provincial Hospital
Yantai Yuhuangding Hospital
Peking University Third Hospital
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to 80 Years
Healthy volunteers
No
Inclusion criteria
* Histologically confirmed high-grade serous ovarian carcinoma using resection specimens. * Well-preserved surgical paraffin-embedded tumor specimens. * Complete clinical information and survival follow-up data.
Exclusion criteria
* Received preoperative neoadjuvant treatment. * Poor preservation of surgical paraffin specimens. * Incomplete clinical information and survival follow-up data. * With concurrent malignancies other than high-grade serous ovarian carcinoma.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Disease-free survival | through study completion, an average of 1 year |
Contacts
CONTACTXiaoming Xing
Outcome results
None listed