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The main analysis, including clinical data values, will be descriptive and will be focused to inform future prospective studies. For continuous variables, the mean and standard deviation will be presented, together with the mean between-group difference, and 95% confidence interval. For binary outcomes, the percentage and frequency of patients in the outcome category of interest will be presented. When necessary intracluster correlation coefficients will be reported, together with 95% confidence interval. Where appropriate p-values will be presented.
For most laboratory data analysis, the known relevance of a positive detection through a clinical biomarker shall not be known prior to the completion of data analysis, however for PRS analysis this would theoretically be able to provide information on 10-year and lifetime cancer risk for breast, colon, endometrial, melanoma, ovarian, pancreas, and prostate cancers. The level of risk will be determined as a risk-ratio of 2 or ≥3 compared to the general population for moderate- and high-risk individuals respectively.
Inclusion of radiomics data will be assessed for subsets of patients where robust imaging data and appropriate techniques for analysis exist, noting the predominance evidence available for certain tumour types such as lung cancer.
The study will explore the role of multi-parametric predictors by optimising convolutional neural networks (CNN) or other deep learning tool to classify patients into outcome classes.
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