Artificial Intelligence (AI), Ovarian, Fallopian, and Primary Peritoneal Cancer
Conditions
Brief summary
This study integrates data from the randomized controlled SUNNY trial (RCT) and real-world (RWD) data, and employs multimodal data fitting to construct a medical artificial intelligence model to identify the clinical characteristics of patient subgroups suitable for primary debulking surgery (PDS) or interval debulking surgery (IDS), and the cutoff values for selecting different timings of surgery for advanced ovarian cancer.
Interventions
None listed
Sponsors
Shanghai Gynecologic Oncology Group
Shanghai Zhongshan Hospital
Sun Yat-sen University Cancer Centre
Fudan University
Zhejiang Cancer Hospital
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Age ≥ 18 years * Patients who were included in the SUNNY study or who were newly diagnosed with stage IIIC or IV primary epithelial ovarian cancer, fallopian tube cancer, or primary peritoneal cancer during the SUNNY study period (2015-2023) * Underwent primary debulking surgery or interval debulking surgery * Data avaliable on first-line treatment and follow-up
Exclusion criteria
* Non-epithelial ovarian cancer or borderline tumors. * Low-grade tumors. * Mucinous ovarian cancer. * Missing data on first-line treatment and follow-up
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| The accuracy of predicting the 3-year overall survival (OS) difference between primary debulking surgery (PDS) and interval debulking surgery (IDS). | 3 years |
Outcome results
None listed