The Influence of Variability and Uncertainty in the Clinical Reference on MRI Radiomics Modelling and Performance

Abstract

Radiomics uses quantitative analysis of medical imaging based on machine learning techniques and has shown its potentials of aiding personalized clinical decisions. A high standard of clinical reference (or ground truth, endpoint) is vital in radiomics feature selection and modeling, but is commonly overlooked, and assumed to be perfect. However, in reality, there are uncertainties and variability in these clinical references due to many factors. We aim to quantitatively assess the influence of clinical reference uncertainty and variability on MRI Radiomics modeling via endpoint annotation permutation with different levels.

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