View clinical trials related to Predictive Cancer Model.
Filter by:The goal of this prospective observational cohort study is to validate previously developed Hepatocellular Carcinoma (HCC) risk prediction algorithms, the Liver Risk Computation (LIRIC) models, which are based on electronic health records. The main questions it aims to answer are: - Will our retrospectively developed general population LIRIC models, developed on routine EHR data, perform similarly when prospectively validated, and reliably and accurately predict HCC in real-time? - What is the average time from model deployment and risk prediction, to the date of HCC development and what is the stage of HCC at diagnosis? The risk model will be deployed on data from individuals eligible for the study. Each individual will be assigned a risk score and tracked over time to assess the model's discriminatory performance and calibration.
1. Establish a predictive model for the efficacy of immune checkpoint inhibitors (ICI) in Chinese patients with biliary tract cancers. By analyzing the dynamic changes of circulating tumor DNA (ctDNA) and other clinical and pathological features before and after ICI treatment in a cohort of patients with biliary tract tumors, a predictive model can be established to evaluate the efficacy of ICI treatment in the early stages or even before treatment, serving as a reliable tool for selecting patients who are likely to benefit from ICI treatment. 2. Investigate the clinical features of populations that benefit from different immune combination therapies. By comparing the differences and enrichment of mutations between patients receiving different treatment regimens, and if patients have sufficient pre-treatment tissue, further comparisons of differentially expressed genes and pathways may be made.
The goal of this prospective observational cohort study is to validate a previously developed pancreatic cancer risk prediction algorith (the PRISM model) using electronic health records from the general population. The main questions it aims to answer are: - Will a pancreatic cancer risk model, developed on routine EHR data, reliably and accurately predict pancreatic cancer in real-time? - What is the average time from model deployment and risk prediction, to the date of pancreatic cancer development and what is the stage of pancreatic cancer at diagnosis? The risk model will be deployed on data from individuals eligible for the study. Each individual will be assigned a risk score and tracked over time to assess the model's discriminatory performance and calibration.