Ignite Creation Date:
2024-10-26 @ 3:42 PM
Last Modification Date:
2024-10-26 @ 3:42 PM
Study NCT ID:
NCT06632886
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-10-07
Brief Title:
AI-Assisted Non-Contrast CT for Multi-Cancer Screening
Sponsor:
None
Organization:
None
Study Overview
Official Title:
A Prospective Cohort Study Evaluating the Utility of Artificial Intelligence-Assisted Non-Contrast Computed Tomography for Multi-Cancer Screening in Asymptomatic Individuals Undergoing Routine Health Examinations
Status:
RECRUITING
Status Verified Date:
2024-10
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:
None
Brief Summary:
Cancer poses a major public health challenge in China Early detection can improve treatment outcomes and survival rates In this study we will conduct a large-scale prospective multi-center cohort study to evaluate the utility of AI-assisted non-contrast CT for multi-cancer screening
The study aims to enroll 1 million asymptomatic participants undergoing routine health examinations using an AI imaging model based on non-contrast CT to detect seven cancers such as lung liver gastric colorectal esophageal pancreatic and breast cancers Positive cases will be required to be referred to Shanghai Changhai Hospital for further imaging and care based on National Comprehensive Cancer Network NCCN and American College of Radiology ACR guidelines The goal is to assess the AI models diagnostic performance for seven cancer types especially for early-stage resectable tumors
The study aims to enroll 1 million asymptomatic participants undergoing routine health examinations using an AI imaging model based on non-contrast CT to detect seven cancers such as lung liver gastric colorectal esophageal pancreatic and breast cancers Positive cases will be required to be referred to Shanghai Changhai Hospital for further imaging and care based on National Comprehensive Cancer Network NCCN and American College of Radiology ACR guidelines The goal is to assess the AI models diagnostic performance for seven cancer types especially for early-stage resectable tumors
Detailed Description:
Cancer has become a major public health issue in China seriously affecting population health the economy and social development In 2022 there were an estimated 482 million new cancer cases and 257 million cancer-related deaths Lung cancer liver cancer gastric cancer colorectal cancer esophageal cancer pancreatic cancer and breast cancer are the seven leading causes of cancer-related mortality A successful earlier detection strategy would allow patients to receive timely interventions improve treatment outcomes enhance overall survival and reduce the complexity and cost of treatment
In this study we will conduct a large-scale prospective multi-center cohort study aiming to evaluate the utility of AI-assisted non-contrast CT for multi-cancer screening The population consists of individuals who have undergone non-contrast abdominal or chest CT scans at Meinian Onehealth Health Examination Center or Shanghai Changhai Health Examination Center with an expected enrollment of 1 million participants A multi-cancer screening model via non-contrast CT developed by Alibaba DAMO Academy will be integrated into the PACS system of health examination centers The imaging AI model will be used to automatically detect various cancerous lesions including lung cancer liver cancer gastric cancer colorectal cancer esophageal cancer pancreatic cancer and breast cancer Subjects identified with positive lesions by the AI model will be required to be referred to Shanghai Changhai Hospital for further imaging examinations such as contrast-enhanced CT MRI Endoscopy etc to confirm the final disease status and formulate a treatment plan Additionally the medical team should follow care pathways developed based on guidelines from NCCN and ACR and if necessary patients will be directed to the multidisciplinary team MDT clinic for specific cancer types to determine the diagnostic procedures The ultimate goal of this study is to comprehensively assess the diagnostic performance metrics of the AI model for each of the seven cancer types individually These metrics include but are not limited to sensitivity specificity and positivenegative predictive value Particular emphasis will be placed on evaluating the models efficacy in detecting early-stage resectable tumors The overarching aim is to determine whether the implementation of this AI-assisted screening approach could potentially lead to improved overall survival rates through earlier detection and intervention
In this study we will conduct a large-scale prospective multi-center cohort study aiming to evaluate the utility of AI-assisted non-contrast CT for multi-cancer screening The population consists of individuals who have undergone non-contrast abdominal or chest CT scans at Meinian Onehealth Health Examination Center or Shanghai Changhai Health Examination Center with an expected enrollment of 1 million participants A multi-cancer screening model via non-contrast CT developed by Alibaba DAMO Academy will be integrated into the PACS system of health examination centers The imaging AI model will be used to automatically detect various cancerous lesions including lung cancer liver cancer gastric cancer colorectal cancer esophageal cancer pancreatic cancer and breast cancer Subjects identified with positive lesions by the AI model will be required to be referred to Shanghai Changhai Hospital for further imaging examinations such as contrast-enhanced CT MRI Endoscopy etc to confirm the final disease status and formulate a treatment plan Additionally the medical team should follow care pathways developed based on guidelines from NCCN and ACR and if necessary patients will be directed to the multidisciplinary team MDT clinic for specific cancer types to determine the diagnostic procedures The ultimate goal of this study is to comprehensively assess the diagnostic performance metrics of the AI model for each of the seven cancer types individually These metrics include but are not limited to sensitivity specificity and positivenegative predictive value Particular emphasis will be placed on evaluating the models efficacy in detecting early-stage resectable tumors The overarching aim is to determine whether the implementation of this AI-assisted screening approach could potentially lead to improved overall survival rates through earlier detection and intervention
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