Trial Title:
Prediction of Peritoneal Metastasis for Gastric Cancer Based on Radiomics
NCT ID:
NCT05722275
Condition:
Gastric Cancer
Peritoneal Metastases
Conditions: Official terms:
Neoplasm Metastasis
Stomach Neoplasms
Conditions: Keywords:
Artificial Intelligence
Classification
Gastric Cancer
Peritoneal Metastases
Computed Tomography
Radiomics
Study type:
Observational
Overall status:
Recruiting
Study design:
Time perspective:
Prospective
Intervention:
Intervention type:
Diagnostic Test
Intervention name:
Peritoneal metastasis status ascertainment
Description:
Each participant with gastric cancer will undergo enhanced CT examination for detection
of peritoneal metastasis. Within two weeks of CT examination, the participant will
undergo diagnostic laparoscopy to confirm the status of peritoneal metastasis.
Arm group label:
Fujian Medical University Union Hospital
Arm group label:
Guangdong Provincial People's Hospital
Arm group label:
Guangzhou Medical University
Arm group label:
Guizhou Provincial People's Hospital
Arm group label:
Henan Cancer Hospital
Arm group label:
Nanfang Hospital of Southern Medical University
Arm group label:
Peking University Cancer Hospital & Institute
Arm group label:
Scientific Institute San Raffaele
Arm group label:
Shanxi Province Cancer Hospital
Arm group label:
Sun Yat-sen University
Arm group label:
The First Affiliated Hospital of Zhengzhou University
Arm group label:
Yunnan Cancer Hospital
Arm group label:
Zhenjiang First People's Hospital
Summary:
Peritoneal metastasis of gastric cancer is difficult to be detected in time, thus
delaying treatment. Based on the conventional CT images of gastric cancer, this study
plans to develop, improve and validate an intelligent analysis system based on radiomics.
By extracting and combining the radiomics features related to peritoneal metastasis of
gastric cancer, the intelligent analysis system could predict the risk of peritoneal
metastasis, and provide personalized decision suggestions for the treatment of gastric
cancer.
Detailed description:
Peritoneal metastasis of gastric cancer is difficult to be detected in time, thus
delaying treatment. Based on the conventional CT images of gastric cancer, this study
plans to develop, improve and validate an intelligent analysis system based on radiomics.
By extracting and combining the radiomics features related to peritoneal metastasis of
gastric cancer, the intelligent analysis system could predict the risk of peritoneal
metastasis, and provide personalized decision suggestions for the treatment of gastric
cancer.
Criteria for eligibility:
Study pop:
All included patients are initially diagnosed as peritoneal metastasis-negative by CT,
but later confirmed with the actual peritoneal metastasis status in laparoscopic
exploration.
Sampling method:
Non-Probability Sample
Criteria:
Inclusion Criteria:
- (1) diagnosed advanced gastric cancer (≥cT3) by endoscopy-biopsy pathology, combined
with CT and/or endoscopic ultrasound;
- (2) with both enhanced CT and laparoscopy;
- (3) without typical peritoneal metastasis indications in CT (diffuse omental nodules
or omental cake, large amount of ascites, obvious irregular thickening with high
peritoneal enhancement);
- (4) without other evidence of distant metastasis, and no stage IV features on CT.
Exclusion Criteria:
- (1) previous abdominal surgery;
- (2) previous abdominal malignancies or inflammatory diseases;
- (3) time intervals between CT and laparoscopy longer than 2 weeks;
- (4) CT image artifacts that undermine peritoneal lesion assessment.
Gender:
All
Minimum age:
18 Years
Maximum age:
N/A
Healthy volunteers:
No
Locations:
Facility:
Name:
Peking University Cancer Hospital & Institute
Address:
City:
Beijing
Country:
China
Status:
Recruiting
Contact:
Last name:
Lei Tang
Facility:
Name:
Fujian Medical University Union Hospital
Address:
City:
Fuzhou
Country:
China
Status:
Recruiting
Contact:
Last name:
Changming Huang, MD
Facility:
Name:
Affiliated Cancer Hospital and Institute of Guangzhou Medical University
Address:
City:
Guangzhou
Country:
China
Status:
Recruiting
Contact:
Last name:
Shuzhong Cui, MD
Facility:
Name:
Guangdong Provincial People's Hospital
Address:
City:
Guangzhou
Country:
China
Status:
Recruiting
Contact:
Last name:
Zaiyi Liu, MD
Facility:
Name:
Nanfang Hospital of Southern Medical University
Address:
City:
Guangzhou
Country:
China
Status:
Recruiting
Contact:
Last name:
Guoxin Li
Facility:
Name:
Sun Yat-Sen University Cancer Hospital
Address:
City:
Guangzhou
Country:
China
Status:
Recruiting
Contact:
Last name:
Linquan Tang, MD
Facility:
Name:
Guizhou Provincial People's Hospital
Address:
City:
Guiyang
Country:
China
Status:
Recruiting
Contact:
Last name:
Rongping Wang
Facility:
Name:
Yunnan Cancer Hospital
Address:
City:
Kunming
Country:
China
Status:
Recruiting
Contact:
Last name:
Zhenhui Li, MD
Facility:
Name:
Shanxi Province Cancer Hospital
Address:
City:
Taiyuan
Country:
China
Status:
Recruiting
Contact:
Last name:
Yanfen Cui, MD
Facility:
Name:
Henan Cancer Hospital
Address:
City:
Zhengzhou
Country:
China
Status:
Recruiting
Contact:
Last name:
Jing Li
Facility:
Name:
The First Affiliated Hospital of Zhengzhou University
Address:
City:
Zhengzhou
Country:
China
Status:
Recruiting
Contact:
Last name:
Jianbo Gao
Facility:
Name:
Zhenjiang First People's Hospital
Address:
City:
Zhenjiang
Country:
China
Status:
Recruiting
Contact:
Last name:
Xiuhong Shan
Facility:
Name:
Scientific Institute San Raffaele
Address:
City:
Milan
Country:
Italy
Status:
Recruiting
Contact:
Last name:
Francesco De Cobelli
Start date:
January 1, 2023
Completion date:
December 31, 2028
Lead sponsor:
Agency:
Chinese Academy of Sciences
Agency class:
Other
Collaborator:
Agency:
Peking University Cancer Hospital & Institute
Agency class:
Other
Collaborator:
Agency:
Zhenjiang First People's Hospital
Agency class:
Other
Collaborator:
Agency:
The First Affiliated Hospital of Zhengzhou University
Agency class:
Other
Collaborator:
Agency:
Nanfang Hospital, Southern Medical University
Agency class:
Other
Collaborator:
Agency:
Guizhou Provincial People's Hospital
Agency class:
Other
Collaborator:
Agency:
Henan Cancer Hospital
Agency class:
Other
Collaborator:
Agency:
Yunnan Cancer Hospital
Agency class:
Other
Collaborator:
Agency:
Guangdong Provincial People's Hospital
Agency class:
Other
Collaborator:
Agency:
Guangzhou Medical University
Agency class:
Other
Collaborator:
Agency:
Fujian Medical University Union Hospital
Agency class:
Other
Collaborator:
Agency:
Shanxi Province Cancer Hospital
Agency class:
Other
Collaborator:
Agency:
Sun Yat-sen University
Agency class:
Other
Collaborator:
Agency:
Beihang University
Agency class:
Other
Collaborator:
Agency:
Scientific Institute San Raffaele
Agency class:
Other
Source:
Chinese Academy of Sciences
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05722275
