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A self organizing map for exploratory analysis of PET radiomic features

Alsyed, Emad, Smith, Rhodri, Paisey, Stephen ORCID:, Marshall, Christopher ORCID: and Spezi, Emiliano ORCID: 2021. A self organizing map for exploratory analysis of PET radiomic features. Presented at: 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Boston, MA, USA, 31 October -7 November 2020. 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE, 10.1109/NSS/MIC42677.2020.9507846

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Texture analysis for quantification of intratumor uptake heterogeneity in PET/CT images has received increasing attention. This allows the extraction of a large number of ‘radiomic’ features to be correlated with end point information such as tumor type, therapy response, prognosis. The conventional complex workflow for calculation of texture features introduces numerous confounding variables. This non exhaustively includes, imaging time post administration of radiopharmaceutical and the method and extent of functional volume segmentation. A lack of understanding on the dependency of texture features with these variables serves as a detriment to the urgent need to standardize texture measurements to pool results from different imaging centers. The utilization of machine learning techniques for feature (and their combinations) selection serves as a promising method to alleviate redundancy in radiomics. To this avail, we introduce for the first time the application of a Kohonen self-organizing feature map to identify the emergent properties present when performing texture analysis. The application of the self-organizing map to radiomic analysis serves as a powerful general-purpose exploratory instrument to reveal the statistical indicators of texture distributions. For this purpose, texture features from PET-CT images of 8 pre-clinical mice with mammary carcinoma xenografts were analyzed with varying post injection imaging time and tumor segmentation contour size. This varying distribution of texture parameters were interpreted by the self-organizing map to reveal two distinct clusters of texture features which are dependent on contour size, providing additional evidence that contour size and hence segmentation method is a confounding variable when performing texture analysis. Furthermore, the self-organizing map can be utilized as a method to incorporate this revealed dependency in a prediction model in the presence of end point information, which will be an area of future work.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Engineering
Publisher: IEEE
ISBN: 978-1-7281-7694-9
Date of First Compliant Deposit: 4 October 2021
Date of Acceptance: 21 July 2021
Last Modified: 30 Dec 2022 07:23

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