Quantification of lung function on CT images based on pulmonary radiomic filtering.

Autor: Yang Z; Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA.; Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China., Lafata KJ; Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA.; Department of Radiology, Duke University, Durham, North Carolina, USA.; Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA., Chen X; Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China., Bowsher J; Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China., Chang Y; Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA., Wang C; Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA., Yin FF; Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA.; Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China.
Jazyk: angličtina
Zdroj: Medical physics [Med Phys] 2022 Nov; Vol. 49 (11), pp. 7278-7286. Date of Electronic Publication: 2022 Jul 10.
DOI: 10.1002/mp.15837
Abstrakt: 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.
(© 2022 American Association of Physicists in Medicine.)
Databáze: MEDLINE
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