Literature DB >> 31117063

The stability of imaging biomarkers in radiomics: a framework for evaluation.

H Y C Wang1, E M Donovan, A Nisbet, C P South, S Alobaidli, V Ezhil, I Phillips, V Prakash, M Ferreira, P Webster, P M Evans.   

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.

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Year:  2019        PMID: 31117063     DOI: 10.1088/1361-6560/ab23a7

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  2 in total

Review 1.  Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype.

Authors:  Isabella Fornacon-Wood; Corinne Faivre-Finn; James P B O'Connor; Gareth J Price
Journal:  Lung Cancer       Date:  2020-06-02       Impact factor: 5.705

2.  Reproducibility of radiomic features in CT images of NSCLC patients: an integrative analysis on the impact of acquisition and reconstruction parameters.

Authors:  Daniela Origgi; Francesca Botta; Lisa Rinaldi; Simone P De Angelis; Sara Raimondi; Stefania Rizzo; Cristiana Fanciullo; Cristiano Rampinelli; Manuel Mariani; Alessandro Lascialfari; Marta Cremonesi; Roberto Orecchia
Journal:  Eur Radiol Exp       Date:  2022-01-25
  2 in total

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