Stability of radiomics features in apparent diffusion coefficient maps from a multi centre test retest trial
Quantitative radiomics features, extracted from medical images, characterize tumour phenotypes and have been shown to provide prognostic value in predicting clinical outcomes. Stability of radiomics features extracted from apparent diffusion coefficient (ADC) maps is essential for reliable correlation with the underlying pathology and its clinical applications. Within a multicentre, multi vendor trial we established a method to analyse radiomics features from ADC maps of ovarian (n 12) lung (n 19) and colorectal liver metastasis (n 30) cancer patients who underwent repeated <7 days) diffusion weighted imaging at 1.5 T and 3 T. From these ADC maps, 1322 features describing tumour shape, texture and intensity were retrospectively extracted and stable features were selected using the concordance correlation coefficient (CCC 0.85) Although some features were tissue and/or respiratory motion specific, 122 features were stable for all tumour entities. A large proportion of features were stable across different vendors and field strengths. By extracting stable phenotypic features, fitting dimensionality is reduced and reliable prognostic models can be created, paving the way for clinical implementation of ADC based radiomics.
Scientific Reports