Satisfactory Debulking Prediction Model for Advanced Ovarian Cancer Based on PET-CT Image Data

Trial Title: Satisfactory Debulking Prediction Model for Advanced Ovarian Cancer Based on PET-CT Image Data

NCT ID: NCT06533709

Condition: Ovarian Cancer
Image
Predation

Conditions: Official terms:
Ovarian Neoplasms
Carcinoma, Ovarian Epithelial

Conditions: Keywords:
Ovarian Cancer
PET-CT Image
Debulking Prediction Model

Study type: Observational

Overall status: Recruiting

Study design:

Time perspective: Retrospective

Intervention:

Intervention type: Diagnostic Test
Intervention name: Radio-score
Description: A score based on the LASSO regression model predicting the R0 resection of the primary debulking surgery of advanced ovarian cancer.
Arm group label: SYSU cohort
Arm group label: ZJCH cohort

Summary: This project intends to conduct a multicenter retrospective study to evaluate the satisfactory reduction of advanced ovarian cancer using PET-CT images, and explore the correlation between molecular biological characteristics and clinical characteristics of ovarian cancer through high-throughput sequencing genomics combined with radiomics.

Detailed description: Ovarian cancer is the gynecological malignant tumor with the highest fatality rate. More than 70% of patients are diagnosed with advanced stage, often involving various organs of the pelvis and abdomen, which increases the difficulty of surgical resection, and the 5-year survival rate is only 30%. Surgical treatment is the cornerstone of the treatment of ovarian cancer, and whether it can achieve satisfactory tumor reduction is an important factor affecting the prognosis of ovarian cancer. At present, the methods used to evaluate whether satisfactory tumor reduction can be achieved include Suidan score based on CT image and Fagotti score based on laparoscopic exploration, but there are problems such as low sensitivity, poor specificity or strong subjectivity, and the efficiency of predicting satisfactory tumor reduction is only about 60%. In recent years, PET-CT has been widely used in tumor diagnosis. Pet-ct combined with PET metabolic imaging technology and traditional CT scanning can help to distinguish the nature of tumors, assess the systemic tumor load, define the scope of the lesion, and provide the metabolic status of various parts of the body. The application value of PET-CT related imaging features and metabolic information in ovarian cancer needs to be clarified. Our team's previous study found that PET-CT related images and metabolic information showed certain advantages in predicting satisfactory resection of ovarian cancer, and the AUC reached 0.85, which was better than the current CT image score and laparoscopic score. Therefore, this project intends to conduct a multicenter retrospective study to evaluate the satisfactory tumor reduction rate of advanced ovarian cancer using PET-CT images to guide clinical practice and predict the prognosis of patients. At the same time, we will explore the molecular biological characteristics and clinical relevance of ovarian cancer through the combination of high-throughput sequencing genomics and radiomics.

Criteria for eligibility:

Study pop:
A total of 96 patients with advanced ovarian cancer who underwent PET-CT examination before the surgery, and received primary debulking surgery at our center from July 2017 to April 2024 were included for model development. Additionally, 50 patients from Zhejiang Cancer Hospital were included for model validation.

Sampling method: Non-Probability Sample
Criteria:
Inclusion Criteria: - Pathological type is epithelial ovarian cancer. - Underwent primary debulking surgery at our hospital. - Postoperative pathological staging is FIGO stage IIB or above. - Clinical, surgical, and pathological data of the patient are mostly complete. Exclusion Criteria: - Pathological type is non-epithelial ovarian cancer. - Underwent fertility-preserving surgery or palliative surgery. - Presence of infection during PET/CT image acquisition. - Concurrent other malignant tumors. - Severe diseases of other major organs.

Gender: Female

Minimum age: 18 Years

Maximum age: 80 Years

Healthy volunteers: No

Locations:

Facility:
Name: The Sun Yat-sen Memorial Hospital of Sun Yat-sen University

Address:
City: Guangzhou
Zip: 520120
Country: China

Status: Recruiting

Contact:
Last name: huaiwu Lu

Phone: 86+ 18688395806
Email: luhuaiwu@mail.sysu.edu.cn

Start date: June 1, 2024

Completion date: May 31, 2026

Lead sponsor:
Agency: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Agency class: Other

Source: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Record processing date: ClinicalTrials.gov processed this data on November 12, 2024

Source: ClinicalTrials.gov page: https://clinicaltrials.gov/ct2/show/NCT06533709