Cyst‐based measurements for assessing lymphangioleiomyomatosis in computed tomography. Issue 5 (15th April 2015)

• Record Type: Journal Article
• Authors: Lo, P.; Brown, M. S.; Kim, H.; Kim, H.; Argula, R.; Strange, C.; Goldin, J. G.
• Is Part Of: Medical physics. Volume 42:Issue 5(2015)
• Page Start: 2287
• Page End: 2295
• Publication Date: 2015-04-15
• Subjects: biological tissues -- computerised tomography -- diseases -- feature extraction -- feature selection -- image segmentation -- lung -- medical image processing -- pneumodynamics -- regression analysis; Computed tomography -- Pneumodyamics, respiration -- Diseases; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; lymphangioleiomyomatosis -- cyst segmentation -- cystic lung disease -- quantitative CT -- chest CT; Computed tomography -- Lungs -- Medical image segmentation -- Linear regression -- Electric measurements -- Medical image smoothing -- Medical image noise -- Density measurement -- Image analysis; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4916655
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: To investigate the efficacy of a new family of measurements made on individual pulmonary cysts extracted from computed tomography (CT) for assessing the severity of lymphangioleiomyomatosis (LAM). Methods: CT images were analyzed using thresholding to identify a cystic region of interest from chest CT of LAM patients. Individual cysts were then extracted from the cystic region by the watershed algorithm, which separates individual cysts based on subtle edges within the cystic regions. A family of measurements were then computed, which quantify the amount, distribution, and boundary appearance of the cysts. Sequential floating feature selection was used to select a small subset of features for quantification of the severity of LAM. Adjusted R 2 from multiple linear regression and R 2 from linear regression against measurements from spirometry were used to compare the performance of our proposed measurements with currently used density based CT measurements in the literature, namely, the relative area measure and the D measure. Results: Volumetric CT data, performed at total lung capacity and residual volume, from a total of 49 subjects enrolled in the MILES trial were used in our study. Our proposed measures had adjusted R 2 ranging from 0.42 to 0.59 when regressing against the spirometry measures, with p < 0.05. For previously used density based CT measurements in the literature, the best R 2 was 0.46 (for only one instance), with the majority being lower than 0.3 or p > 0.05. Conclusions: The proposed family of CT‐based cyst measurements have better correlation with spirometric measures than previously used density based CT measurements. They show potential as a sensitive tool for quantitatively assessing the severity of LAM.