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

Autor:	Huang YH; Department of Health Technology and Informatics, The Hong Kong Polytechnic University., Teng X; Department of Health Technology and Informatics, The Hong Kong Polytechnic University., Zhang J; Department of Health Technology and Informatics, The Hong Kong Polytechnic University., Chen Z; Department of Health Technology and Informatics, The Hong Kong Polytechnic University., Ma Z; Department of Health Technology and Informatics, The Hong Kong Polytechnic University., Ren G; Department of Health Technology and Informatics, The Hong Kong Polytechnic University., Kong FS; Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR.; Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen., Ge H; Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China., Cai J; Department of Health Technology and Informatics, The Hong Kong Polytechnic University.
Jazyk:	angličtina
Zdroj:	Journal of thoracic imaging [J Thorac Imaging] 2023 Sep 01; Vol. 38 (5), pp. 286-296. Date of Electronic Publication: 2023 May 29.
DOI:	10.1097/RTI.0000000000000717
Abstrakt:	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. Competing Interests: The authors declare no conflict of interest. (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)
Databáze:	MEDLINE
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