OBJECTIVE: Emphysema is a disease of the lung characterized by the destruction of the alveolar sac walls. Several quantitative densitometric measures of emphysema from wholelung CT have been proposed for evaluating disease severity and progression. However, a concern with these quantitative measures has been the large interscan variability observed during longitudinal studies of emphysema. To account for and reduce inherent measure variability, this work implements and evaluates the use of a multivariate random-effects model for correcting longitudinal variation in densitometric scores of emphysema due to inspiration. MATERIALS AND METHODS: The method of multivariate compensation was validated on three of the most commonly reported densitometric measures of emphysema: the emphysema index, histogram percentile, and fractal dimension. Two short-interval, zero-change datasets, one for model development (n = 105) and one for validation (n = 106), were retrospectively identified and used to ensure that all variation was caused by inherent measure variability. RESULTS: A statistically significant (F test, p < 0.001) reduction of 42.40% in measurement limits of agreement could be obtained after model application, with compensated emphysema metric differences showing 31-33% of the variance compared with uncompensated metric variance. CONCLUSION: Compensation was still effective when the trained model was applied to the second validation dataset. Multivariate compensation was found to be useful in reducing interscan measurement variability and should be applied to future longitudinal studies of emphysema.
OBJECTIVE:Emphysema is a disease of the lung characterized by the destruction of the alveolar sac walls. Several quantitative densitometric measures of emphysema from wholelung CT have been proposed for evaluating disease severity and progression. However, a concern with these quantitative measures has been the large interscan variability observed during longitudinal studies of emphysema. To account for and reduce inherent measure variability, this work implements and evaluates the use of a multivariate random-effects model for correcting longitudinal variation in densitometric scores of emphysema due to inspiration. MATERIALS AND METHODS: The method of multivariate compensation was validated on three of the most commonly reported densitometric measures of emphysema: the emphysema index, histogram percentile, and fractal dimension. Two short-interval, zero-change datasets, one for model development (n = 105) and one for validation (n = 106), were retrospectively identified and used to ensure that all variation was caused by inherent measure variability. RESULTS: A statistically significant (F test, p < 0.001) reduction of 42.40% in measurement limits of agreement could be obtained after model application, with compensated emphysema metric differences showing 31-33% of the variance compared with uncompensated metric variance. CONCLUSION: Compensation was still effective when the trained model was applied to the second validation dataset. Multivariate compensation was found to be useful in reducing interscan measurement variability and should be applied to future longitudinal studies of emphysema.
Authors: Huaiyu Heather Chen-Mayer; Matthew K Fuld; Bernice Hoppel; Philip F Judy; Jered P Sieren; Junfeng Guo; David A Lynch; Antonio Possolo; Sean B Fain Journal: Med Phys Date: 2017-02-21 Impact factor: 4.071
Authors: Rowena Yip; Artit Jirapatnakul; Minxia Hu; Xiangmeng Chen; Dan Han; Teng Ma; Yeqing Zhu; Mary M Salvatore; Laurie R Margolies; David F Yankelevitz; Claudia I Henschke Journal: Transl Lung Cancer Res Date: 2021-02
Authors: Jennifer L Boes; Maria Bule; Benjamin A Hoff; Ryan Chamberlain; David A Lynch; Jadranka Stojanovska; Fernando J Martinez; Meilan K Han; Ella A Kazerooni; Brian D Ross; Craig J Galbán Journal: Tomography Date: 2015-09