On dose cube pixel spacing pre-processing for features extraction stability in dosiomic studies |
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| Institution: | 1. School of Medicine, University of Auckland, Auckland 1023, New Zealand;2. School of Medical Sciences, University of Auckland, Auckland 1023, New Zealand;1. Department of Radiology, NYU Langone Health, New York, NY, United States of America;2. Center for Advanced Imaging Innovation and Research, NYU Grossman School of Medicine, New York, NY, United States of America;1. Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Ave/JJN3, Cleveland, OH 44195;2. Center for Devices and Radiological Health, US Food and Drug Administration Division of Imaging, Diagnostic and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland;3. Division of Radiotherapy and Imaging, The Institute of Cancer Research and Royal Marsden Hospital, London, United Kingdom; European Imaging Biomarkers Alliance, European Society of Radiology, London, UK;4. Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, Maryland;5. Euclid Bioimaging, Inc., Boston, Massachusetts;6. Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina;7. Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland;8. Data Science Institute, Statistical and Quantitative Sciences, Takeda Pharmaceuticals America Inc, Lexington, Massachusetts;9. Department of Diagnostic Radiology, Oregon Health & Sciences University, Portland, Oregon;10. Department of Medical Physics, University of Wisconsin, Madison, Wisconsin |
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| Abstract: | PurposeDosiomics allows to parameterize regions of interest (ROIs) and to produce quantitative dose features encoding the spatial and statistical distribution of radiotherapy dose. The stability of dosiomics features extraction on dose cube pixel spacing variation has been investigated in this study.Material and MethodsBased on 17 clinical delivered dose distributions (Pn), dataset has been generated considering all the possible combinations of four dose grid resolutions and two calculation algorithms. Each dose voxel cube has been post-processed considering 4 different dose cube pixel spacing values: 1x1x1, 2x2x2, 3x3x3 mm3 and the one equal to the planning CT. Dosiomics features extraction has been performed from four different ROIs. The stability of each extracted dosiomic feature has been analyzed in terms of coefficient of variation (CV) intraclass correlation coefficient (ICC).ResultsThe highest CV mean values were observed for PTV ROI and for the grey level size zone matrix features family. On the other hand, the lowest CV mean values have been found for RING ROI for the grey level co-occurrence matrix features family. P3 showed the highest percentage of CV >1 (1.14%) followed by P15 (0.41%), P1 (0.29%) and P13 (0.19%). ICC analysis leads to identify features with an ICC >0.95 that could be considered stable to use in dosiomic studies when different dose cube pixel spacing are considered, especially the features in common among the seventeen plans.ConclusionConsidering the observed variability, dosiomic studies should always provide a report not only on grid resolution and algorithm dose calculation, but also on dose cube pixel spacing. |
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| Keywords: | Dosiomics Dose distribution texture analysis Features stability Dose cube pixel spacing |
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