Ignite Creation Date:
2024-10-26 @ 3:42 PM
Last Modification Date:
2024-10-26 @ 3:42 PM
Study NCT ID:
NCT06638866
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-10-09
Brief Title:
Artificial Intelligence-based Early Screening of Pancreatic Cancer and High Risk Tracing 2 ESPRIT-AI-2
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Artificial Intelligence-based Health Information Management System and Key Technology Study of Early Screening and Hierarchical Diagnosis and Treatment of Pancreatic Cancer 2
Status:
RECRUITING
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
ESPRIT-AI-2
Brief Summary:
Pancreatic ductal adenocarcinoma PDAC is a highly aggressive cancer with a very low survival rate due to its covert onset and low early diagnosis rate This study uses a pancreatic imaging AI model to improve early detection and high-risk monitoring of pancreatic cancer through non-contrast CT scans The goal is to validate the AI models diagnostic performance particularly in identifying early-stage resectable PDAC
Detailed Description:
PDAC is one of the most aggressive malignancies with a dismal 5-year survival rate of merely 13 The poor prognosis is primarily due to its insidious onset and low early diagnosis rate Clinical studies have shown that 51 of pancreatic cancer patients present with distant metastasis at the time of diagnosis with a 5-year survival rate of less than 5 in contrast patients at stage IA demonstrate a clear therapeutic benefit with a postoperative 5-year survival rate of up to 837 Early detection of pancreatic cancer is crucial for improving survival rates In this study investigators will utilize our developed pancreatic imaging AI model to conduct prospective clinical trials aiming to validate the models diagnostic performance in real-world applications particularly its efficacy in detecting early-stage PDAC
This study is led by Jin Gang Director of Department of General Surgery of Shanghai Changhai Hospital in collaboration with Yinzhou Hospital Affiliated to Medical School of Ningbo University The Second Affiliated Hospital of Jiaxing University The Central Hospital of Lishui City Jingning County Peoples Hospital Meinian Onehealth Health Examination Center and the Medical AI Imaging Laboratory Team at Alibaba DAMO Academy This is a translational study focused on early detection and high-risk monitoring of pancreatic cancer based on artificial intelligence and it will directly impact clinical care pathways The study population consists of individuals who have undergone non-contrast abdominal or chest CT scans at medical institutions or health examination centers defined as an opportunistic screening population The pancreatic imaging AI model will be used to automatically detect pancreatic lesions including PDAC and non-PDAC subtypes Subjects identified with positive lesions by the AI model will be required to undergo questionnaire blood tests including tumor marker analysis and further imaging examinations such as contrast CT MRI and EUS to confirm the final pancreatic lesion status and formulate a treatment plan The ultimate aim is to assess the AI models diagnostic performance particularly its detection rate for early resectable pancreatic cancer
This study is led by Jin Gang Director of Department of General Surgery of Shanghai Changhai Hospital in collaboration with Yinzhou Hospital Affiliated to Medical School of Ningbo University The Second Affiliated Hospital of Jiaxing University The Central Hospital of Lishui City Jingning County Peoples Hospital Meinian Onehealth Health Examination Center and the Medical AI Imaging Laboratory Team at Alibaba DAMO Academy This is a translational study focused on early detection and high-risk monitoring of pancreatic cancer based on artificial intelligence and it will directly impact clinical care pathways The study population consists of individuals who have undergone non-contrast abdominal or chest CT scans at medical institutions or health examination centers defined as an opportunistic screening population The pancreatic imaging AI model will be used to automatically detect pancreatic lesions including PDAC and non-PDAC subtypes Subjects identified with positive lesions by the AI model will be required to undergo questionnaire blood tests including tumor marker analysis and further imaging examinations such as contrast CT MRI and EUS to confirm the final pancreatic lesion status and formulate a treatment plan The ultimate aim is to assess the AI models diagnostic performance particularly its detection rate for early resectable pancreatic cancer
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None