Bridging the gap between micro- and macro-scales in medical imaging with textural analysis – A biological basis for CT radiomics classifiers?
| Autor: | Neesha C. Dhani, C. Geady, I. Siddiqui, J. Bilkey, David A. Jaffray, Harald Keller |
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| Rok vydání: | 2020 |
| Předmět: |
Point spread function
Pixel business.industry Computer science Attenuation Biophysics General Physics and Astronomy Digital pathology Pattern recognition Image processing General Medicine 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Feature (computer vision) 030220 oncology & carcinogenesis Image Processing Computer-Assisted Medical imaging Radiology Nuclear Medicine and imaging Spatial frequency Artificial intelligence Tomography X-Ray Computed business |
| Zdroj: | Physica Medica. 72:142-151 |
| ISSN: | 1120-1797 |
| Popis: | Introduction Studies suggest there is utility in computed tomography (CT) radiomics for pancreatic disease; however, the precise biological interpretation of its features is unclear. In this manuscript, we present a novel approach towards this interpretation by investigating sub-micron tissue structure using digital pathology. Methods A classification-to attenuation (CAT) function was developed and applied to digital pathology images to create sub-micron linear attenuation maps. From these maps, grey level co-occurrence matrix (GLCM) features were extracted and compared to pathology features. To simulate the spatial frequency loss in a CT scanner, the attenuation maps were convolved with a point spread function (PSF) and subsequently down-sampled. GLCM features were extracted from these down-sampled maps to assess feature stability as a function of spatial frequency loss. Results Two GLCM features were shown to be strongly and positively correlated (r = 0.8) with underlying characteristics of the tumor microenvironment, namely percent pimonidazole staining in the tumor. All features underwent marked change as a function of spatial frequency loss; progressively larger spatial frequency losses resulted in progressively larger inter-tumor standard deviations; two GLCM features exhibited stability up to a 100 µm pixel size. Conclusion This work represents a necessary step towards understanding the biological significance of radiomics. Our preliminary results suggest that cellular metrics of pimonidazole-detectable hypoxia correlate with sub-micron attenuation coefficient texture; however, the consistency of these textures in face of spatial frequency loss is detrimental for robust radiomics. Further study in larger data sets may elucidate additional, potentially more robust features of biologic and clinical relevance. |
| Databáze: | OpenAIRE |
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