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Respiratory Invariant Textures From Static Computed Tomography Scans for Explainable Lung Function Characterization.

Publication ,  Journal Article
Huang, Y-H; Teng, X; Zhang, J; Chen, Z; Ma, Z; Ren, G; Kong, F-MS; Ge, H; Cai, J
Published in: J Thorac Imaging
September 1, 2023

PURPOSE: The inherent characteristics of lung tissue independent of breathing maneuvers may provide fundamental information for function assessment. This paper attempted to correlate textural signatures from computed tomography (CT) with pulmonary function measurements. MATERIALS AND METHODS: Twenty-one lung cancer patients with thoracic 4-dimensional CT, DTPA-single-photon emission CT ventilation ( VNM ) scans, and available spirometry measurements (forced expiratory volume in 1 s, FEV 1 ; forced vital capacity, FVC; and FEV 1 /FVC) were collected. In subregional feature discovery, function-correlated candidates were identified from 79 radiomic features based on the statistical strength to differentiate defected/nondefected lung regions. Feature maps (FMs) of selected candidates were generated on 4-dimensional CT phases for a voxel-wise feature distribution study. Quantitative metrics were applied for validations, including the Spearman correlation coefficient (SCC) and the Dice similarity coefficient for FM- VNM spatial agreement assessments, intraclass correlation coefficient for FM interphase robustness evaluations, and FM-spirometry comparisons. RESULTS: At the subregion level, 8 function-correlated features were identified (effect size>0.330). The FMs of candidates yielded moderate-to-strong voxel-wise correlations with the reference VNM . The FMs of gray level dependence matrix dependence nonuniformity showed the highest robust (intraclass correlation coefficient=0.96 and P <0.0001) spatial correlation, with median SCCs ranging from 0.54 to 0.59 throughout the 10 breathing phases. Its phase-averaged FM achieved a median SCC of 0.60, a median Dice similarity coefficient of 0.60 (0.65) for high (low) functional lung volumes, and a correlation of 0.565 (0.646) between the spatially averaged feature values and FEV 1 (FEV 1 /FVC). CONCLUSIONS: The results provide further insight into the underlying association of specific pulmonary textures with both local ( VNM ) and global (FEV 1 /FVC, FEV 1 ) functions. Further validations of the FM generalizability and the standardization of implementation protocols are warranted before clinically relevant investigations.

Duke Scholars

Published In

J Thorac Imaging

DOI

EISSN

1536-0237

Publication Date

September 1, 2023

Volume

38

Issue

5

Start / End Page

286 / 296

Location

United States

Related Subject Headings

  • Tomography Scanners, X-Ray Computed
  • Respiratory Function Tests
  • Nuclear Medicine & Medical Imaging
  • Lung Neoplasms
  • Lung
  • Humans
  • 3202 Clinical sciences
  • 3201 Cardiovascular medicine and haematology
 

Citation

APA
Chicago
ICMJE
MLA
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Huang, Y.-H., Teng, X., Zhang, J., Chen, Z., Ma, Z., Ren, G., … Cai, J. (2023). Respiratory Invariant Textures From Static Computed Tomography Scans for Explainable Lung Function Characterization. J Thorac Imaging, 38(5), 286–296. https://doi.org/10.1097/RTI.0000000000000717
Huang, Yu-Hua, Xinzhi Teng, Jiang Zhang, Zhi Chen, Zongrui Ma, Ge Ren, Feng-Ming Spring Kong, Hong Ge, and Jing Cai. “Respiratory Invariant Textures From Static Computed Tomography Scans for Explainable Lung Function Characterization.J Thorac Imaging 38, no. 5 (September 1, 2023): 286–96. https://doi.org/10.1097/RTI.0000000000000717.
Huang Y-H, Teng X, Zhang J, Chen Z, Ma Z, Ren G, et al. Respiratory Invariant Textures From Static Computed Tomography Scans for Explainable Lung Function Characterization. J Thorac Imaging. 2023 Sep 1;38(5):286–96.
Huang, Yu-Hua, et al. “Respiratory Invariant Textures From Static Computed Tomography Scans for Explainable Lung Function Characterization.J Thorac Imaging, vol. 38, no. 5, Sept. 2023, pp. 286–96. Pubmed, doi:10.1097/RTI.0000000000000717.
Huang Y-H, Teng X, Zhang J, Chen Z, Ma Z, Ren G, Kong F-MS, Ge H, Cai J. Respiratory Invariant Textures From Static Computed Tomography Scans for Explainable Lung Function Characterization. J Thorac Imaging. 2023 Sep 1;38(5):286–296.

Published In

J Thorac Imaging

DOI

EISSN

1536-0237

Publication Date

September 1, 2023

Volume

38

Issue

5

Start / End Page

286 / 296

Location

United States

Related Subject Headings

  • Tomography Scanners, X-Ray Computed
  • Respiratory Function Tests
  • Nuclear Medicine & Medical Imaging
  • Lung Neoplasms
  • Lung
  • Humans
  • 3202 Clinical sciences
  • 3201 Cardiovascular medicine and haematology