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

TitleRespiratory Invariant Textures From Static Computed Tomography Scans for Explainable Lung Function Characterization
Authors
Keywordscomputed tomography
functional imaging
lung cancer
radiomics
ventilation
Issue Date1-Sep-2023
PublisherLippincott, Williams & Wilkins
Citation
Journal of Thoracic Imaging, 2023, v. 38, n. 5, p. 286-296 How to Cite?
Abstract

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, FEV1; forced vital capacity, FVC; and FEV1/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 FEV1 (FEV1/FVC).

Conclusions: 

The results provide further insight into the underlying association of specific pulmonary textures with both local (VNM) and global (FEV1/FVC, FEV1) functions. Further validations of the FM generalizability and the standardization of implementation protocols are warranted before clinically relevant investigations.


Persistent Identifierhttp://hdl.handle.net/10722/343802
ISSN
2022 Impact Factor: 3.3
2020 SCImago Journal Rankings: 0.836

 

DC FieldValueLanguage
dc.contributor.authorHuang, Yu-Hua-
dc.contributor.authorTeng, Xinzhi-
dc.contributor.authorZhang, Jiang-
dc.contributor.authorChen, Zhi-
dc.contributor.authorMa, Zongrui-
dc.contributor.authorRen, Ge-
dc.contributor.authorKong, Feng-Ming Spring-
dc.contributor.authorGe, Hong-
dc.contributor.authorCai, Jing-
dc.date.accessioned2024-06-11T07:51:43Z-
dc.date.available2024-06-11T07:51:43Z-
dc.date.issued2023-09-01-
dc.identifier.citationJournal of Thoracic Imaging, 2023, v. 38, n. 5, p. 286-296-
dc.identifier.issn0883-5993-
dc.identifier.urihttp://hdl.handle.net/10722/343802-
dc.description.abstract<h3>Purpose: </h3><p>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.</p><h3>Materials and Methods: </h3><p>Twenty-one lung cancer patients with thoracic 4-dimensional CT, DTPA-single-photon emission CT ventilation (<em>V</em><sub>NM</sub>) scans, and available spirometry measurements (forced expiratory volume in 1 s, FEV<sub>1</sub>; forced vital capacity, FVC; and FEV<sub>1</sub>/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-<em>V</em><sub>NM</sub> spatial agreement assessments, intraclass correlation coefficient for FM interphase robustness evaluations, and FM-spirometry comparisons.</p><h3>Results: </h3><p>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 <em>V</em><sub>NM</sub>. The FMs of gray level dependence matrix dependence nonuniformity showed the highest robust (intraclass correlation coefficient=0.96 and <em>P</em><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<sub>1</sub> (FEV<sub>1</sub>/FVC).</p><h3>Conclusions: </h3><p>The results provide further insight into the underlying association of specific pulmonary textures with both local (<em>V</em><sub>NM</sub>) and global (FEV<sub>1</sub>/FVC, FEV<sub>1</sub>) functions. Further validations of the FM generalizability and the standardization of implementation protocols are warranted before clinically relevant investigations.</p>-
dc.languageeng-
dc.publisherLippincott, Williams & Wilkins-
dc.relation.ispartofJournal of Thoracic Imaging-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectcomputed tomography-
dc.subjectfunctional imaging-
dc.subjectlung cancer-
dc.subjectradiomics-
dc.subjectventilation-
dc.titleRespiratory Invariant Textures From Static Computed Tomography Scans for Explainable Lung Function Characterization-
dc.typeArticle-
dc.identifier.doi10.1097/RTI.0000000000000717-
dc.identifier.scopuseid_2-s2.0-85168428553-
dc.identifier.volume38-
dc.identifier.issue5-
dc.identifier.spage286-
dc.identifier.epage296-
dc.identifier.eissn1536-0237-
dc.identifier.issnl0883-5993-

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