18F-grazy-02 PET/CT of Granzyme B Expression for Monitoring Immunotherapy
NCT07724535
Cancer, Neoplasms
Beijing, Beijing Municipality, China
View Trial DetailsNCT Number: NCT06217900
This study is a multi-center, case-control study aiming at developing and blinded testing machine learning-based multiple cancers early detection model by prospectively collecting blood samples from newly diagnosed cancer patients and individuals without confirmed cancer diagnosis.
Interested in participating?
Request InfoBlood samples from newly diagnosed cancer patients and individuals without confirmed cancer diagnosis will be prospectively collected to identify cancer-specific circulating signals through integrative multi-omic analysis. Based on the comprehensive molecular profiling, a machine learning-driven model will be trained and blinded validated independent through a two-stage approach in clinically annotated individuals. Approximately 10327 cancer patients will be enrolled in this study and early-stage cancer patients will be enriched to improve the model sensitivity on distinguishing cancers with favorable prognosis. Approximately 6339 age and sex matched controls will be included in model development, which are volunteers without a cancer diagnosis after routine cancer screening tests.
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
for Case Arm Participants:
Exclusion criteria
for Case Arm Participants:
Inclusion criteria
for Control Arm Participants:
Exclusion criteria
for Control Arm Participants:
Time frame: 12 months
The sensitivity, specificity and tissue origin accuracy of cfDNA methylation-based multiple cancers early detection model in detecting cancer or non-cancer at 95% confidence interval.
Time frame: 12 months
The sensitivity and tissue origin accuracy of cfDNA methylation-based multiple cancers early detection model in detecting stage I to II cancer at 95% confidence interval.
Time frame: 12 months
The sensitivity, specificity and tissue origin accuracy of multi-omic-based multiple cancers early detection model in detecting cancer or non-cancer at 95% confidence interval.
Time frame: 12 months
The sensitivity and specificity of cfDNA methylation-based or multi-omic-based multiple cancers early detection model in different subgroups of the population (such as age, gender, cancer pathological classification, and clinical stage) at 95% confidence interval.
Time frame: 12 months
To develop a questionnaire to evaluate the high-risk factors in the multi-cancer early screening, including lung cancer, gastrointestinal cancer, gynecological cancer, urogenital neoplasms, etc.
Time frame: 12 months
The sensitivity, specificity and tissue origin accuracy of multi-omic-based multiple cancers early detection model in in the population with suspected cancer at 95% confidence interval.
Time frame: 12 months
To simulate the positive predictive value and negative predictive value of different multi-cancer early detection models(cfDNA methylation-based or multi-omic-based),based on the sensitivity, specificity and tissue origin accuracy,according to multi cancer prevalence and staging data of individuals aged 40-75 years in China.
Time frame: 12 months
To simulate the stage-shift and incremental cost-effective ratio (ICER) benefit when compared to usual care (SOC screening) using Markov model based on MCED test performance
Time frame: 12 months
Exploring biomarkers in methylomics and fragmentomics,and constructing multimodal for multi-cancer early detection based on multiomics analysis
Contact information is provided by the study sponsor or research team.
Shanghai Weihe Medical Laboratory Co., Ltd.
Industry
PROFOUND Study: Development and Validation of a Multi-cancer Early Detection Model Based on Peripheral Blood Multi-omic Analysis and Machine Learning: a Multicenter, Prospective, Observational, Case-control Study
Acronym: PROFOUND
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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