OBJECTIVE: The aim of this study was to determine whether a CT-based method shows lung lesions, grades disease severity, and evaluates lung tissue in areas adjacent to or remote from cysts in patients with lymphangioleiomyomatosis (LAM), a cystic lung disease that may cause respiratory failure and death. MATERIALS AND METHODS: Three hundred twenty-six CT examinations of 52 patients with LAM were studied. After the lungs had been divided into segments and images had been subdivided into texture blocks, a multidimensional feature vector was used to differentiate and group each texture block. Cysts were outlined, and texture around and away from cysts was analyzed. Sequential CT scans and pulmonary function test results were evaluated to assess the trend of change. Histopathologic examinations were performed of biopsy specimens from 45 patients. RESULTS: Differences in texture features between areas adjacent to and areas remote from the cysts were observed. The cyst score and sum entropy in areas around the cysts correlated with lung function (p<0.0001). Emphysematouslike changes in noncystic areas were identified in lung tissue of 31 of 45 patients. CONCLUSION: A computational method that uses texture analysis and feature correlation can identify and quantify cystic areas where LAM exists and can detect abnormalities in areas near cysts. Pathologic data also show lung damage in areas adjacent to cysts. Several texture features correlate with lung function. Declines in lung function paralleled changes in texture features. In LAM, cystic changes alone may not define the extent of lung destruction.
OBJECTIVE: The aim of this study was to determine whether a CT-based method shows lung lesions, grades disease severity, and evaluates lung tissue in areas adjacent to or remote from cysts in patients with lymphangioleiomyomatosis (LAM), a cystic lung disease that may cause respiratory failure and death. MATERIALS AND METHODS: Three hundred twenty-six CT examinations of 52 patients with LAM were studied. After the lungs had been divided into segments and images had been subdivided into texture blocks, a multidimensional feature vector was used to differentiate and group each texture block. Cysts were outlined, and texture around and away from cysts was analyzed. Sequential CT scans and pulmonary function test results were evaluated to assess the trend of change. Histopathologic examinations were performed of biopsy specimens from 45 patients. RESULTS: Differences in texture features between areas adjacent to and areas remote from the cysts were observed. The cyst score and sum entropy in areas around the cysts correlated with lung function (p<0.0001). Emphysematouslike changes in noncystic areas were identified in lung tissue of 31 of 45 patients. CONCLUSION: A computational method that uses texture analysis and feature correlation can identify and quantify cystic areas where LAM exists and can detect abnormalities in areas near cysts. Pathologic data also show lung damage in areas adjacent to cysts. Several texture features correlate with lung function. Declines in lung function paralleled changes in texture features. In LAM, cystic changes alone may not define the extent of lung destruction.
Authors: Gustavo G Pacheco; Amanda M Jones; Jianhua Yao; David E Kleiner; Angelo M Taveira-DaSilva; Joel Moss Journal: Chest Date: 2018-02 Impact factor: 9.410
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Authors: Jianhua Yao; Angelo M Taveira-DaSilva; Amanda M Jones; Patricia Julien-Williams; Mario Stylianou; Joel Moss Journal: Am J Respir Crit Care Med Date: 2014-12-01 Impact factor: 21.405
Authors: Eileen Hu-Wang; John L Schuzer; Shirley Rollison; Eric S Leifer; Chloe Steveson; Vissaagan Gopalakrishnan; Jianhua Yao; Tania Machado; Amanda M Jones; Patricia Julien-Williams; Joel Moss; Marcus Y Chen Journal: Chest Date: 2018-10-03 Impact factor: 9.410