Quantification of lung function on CT images based on pulmonary radiomic filtering
© 2022 American Association of Physicists in Medicine..
PURPOSE: To develop a radiomics filtering technique for characterizing spatial-encoded regional pulmonary ventilation information on lung computed tomography (CT).
METHODS: The lung volume was segmented on 46 CT images, and a 3D sliding window kernel was implemented across the lung volume to capture the spatial-encoded image information. Fifty-three radiomic features were extracted within the kernel, resulting in a fourth-order tensor object. As such, each voxel coordinate of the original lung was represented as a 53-dimensional feature vector, such that radiomic features could be viewed as feature maps within the lungs. To test the technique as a potential pulmonary ventilation biomarker, the radiomic feature maps were compared to paired functional images (Galligas PET or DTPA-SPECT) based on the Spearman correlation (ρ) analysis.
RESULTS: The radiomic feature maps GLRLM-based Run-Length Non-Uniformity and GLCOM-based Sum Average are found to be highly correlated with the functional imaging. The achieved ρ (median [range]) for the two features are 0.46 [0.05, 0.67] and 0.45 [0.21, 0.65] across 46 patients and 2 functional imaging modalities, respectively.
CONCLUSIONS: The results provide evidence that local regions of sparsely encoded heterogeneous lung parenchyma on CT are associated with diminished radiotracer uptake and measured lung ventilation defects on PET/SPECT imaging. These findings demonstrate the potential of radiomics to serve as a complementary tool to the current lung quantification techniques and provide hypothesis-generating data for future studies.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:49 |
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| Enthalten in: |
Medical physics - 49(2022), 11 vom: 30. Nov., Seite 7278-7286 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Yang, Zhenyu [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 15.12.2022 Date Revised 15.12.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/mp.15837 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM342918028 |
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| 100 | 1 | |a Yang, Zhenyu |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Quantification of lung function on CT images based on pulmonary radiomic filtering |
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| 500 | |a Date Revised 15.12.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2022 American Association of Physicists in Medicine. | ||
| 520 | |a PURPOSE: To develop a radiomics filtering technique for characterizing spatial-encoded regional pulmonary ventilation information on lung computed tomography (CT) | ||
| 520 | |a METHODS: The lung volume was segmented on 46 CT images, and a 3D sliding window kernel was implemented across the lung volume to capture the spatial-encoded image information. Fifty-three radiomic features were extracted within the kernel, resulting in a fourth-order tensor object. As such, each voxel coordinate of the original lung was represented as a 53-dimensional feature vector, such that radiomic features could be viewed as feature maps within the lungs. To test the technique as a potential pulmonary ventilation biomarker, the radiomic feature maps were compared to paired functional images (Galligas PET or DTPA-SPECT) based on the Spearman correlation (ρ) analysis | ||
| 520 | |a RESULTS: The radiomic feature maps GLRLM-based Run-Length Non-Uniformity and GLCOM-based Sum Average are found to be highly correlated with the functional imaging. The achieved ρ (median [range]) for the two features are 0.46 [0.05, 0.67] and 0.45 [0.21, 0.65] across 46 patients and 2 functional imaging modalities, respectively | ||
| 520 | |a CONCLUSIONS: The results provide evidence that local regions of sparsely encoded heterogeneous lung parenchyma on CT are associated with diminished radiotracer uptake and measured lung ventilation defects on PET/SPECT imaging. These findings demonstrate the potential of radiomics to serve as a complementary tool to the current lung quantification techniques and provide hypothesis-generating data for future studies | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a pulmonary ventilation | |
| 650 | 4 | |a radiomic | |
| 650 | 4 | |a radiomic filtering | |
| 700 | 1 | |a Lafata, Kyle J |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Xinru |e verfasserin |4 aut | |
| 700 | 1 | |a Bowsher, James |e verfasserin |4 aut | |
| 700 | 1 | |a Chang, Yushi |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Chunhao |e verfasserin |4 aut | |
| 700 | 1 | |a Yin, Fang-Fang |e verfasserin |4 aut | |
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