A self organizing map for exploratory analysis of PET radiomic features
| Autor: | Emiliano Spezi, Emad Alsyed, Stephen J. Paisey, Rhodri L. Smith, Christopher Marshall |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Self-organizing map
Dependency (UML) Computer science business.industry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Image segmentation Texture (geology) Feature (computer vision) Redundancy (engineering) Segmentation Artificial intelligence business |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|