The stability of imaging biomarkers in radiomics: a framework for evaluation. (21st August 2019)

• Record Type: Journal Article
• Authors: Wang, H Y C; Donovan, E M; Nisbet, A; South, C P; Alobaidli, S; Ezhil, V; Phillips, I; Prakash, V; Ferreira, M; Webster, P; Evans, P M
• Is Part Of: Physics in medicine & biology. Volume 64:Number 16(2019:Aug.)
• Publication Date: 2019-08-21
• Subjects: radiomics -- lung cancer -- CT imaging -- texture analysis; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ab23a7
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: This paper studies the sensitivity of a range of image texture parameters used in radiomics to: (i) the number of intensity levels, (ii) the method of quantisation to select the intensity levels and (iii) the use of an intensity threshold. 43 commonly used texture features were studied for the gross target volume outlined on the CT component of PET/CT scans of 50 patients with non-small cell lung carcinoma (NSCLC). All cases were quantised for all values between 4 and 128 intensity levels using four commonly used quantisation methods. All results were analysed with and without a threshold range of −200 HU to 300 HU. Cases were ranked for each texture feature and for all quantisation methods with the Spearman's rank correlation coefficient determined to evaluate stability. Results showed large fluctuations in ranking, particularly for low numbers of levels, differences between quantisation methods and with the use of a threshold, with values Spearman's Rank Correlation for many parameters below 0.2. Our results demonstrated the sensitivity of radiomics features to the parameters used during analysis and highlight the risk of low reproducibility comparing studies with slightly different parameters. In terms of the lung cancer CT datasets, this study supports the use of 128 intensity levels, the same uniform quantiser applied to all scans and thresholding of the data. It also supports several of the features recommended in the literature for such studies such as skewness and kurtosis. A recommended framework is presented for curation of the data analysis process to ensure stability of results.