Maarten K Ninaber1, Jan Stolk2, Jasper Smit2, Ernest J Le Roy2, Lucia J M Kroft3, M Els Bakker4, Jeska K de Vries Bouwstra5, Anne A Schouffoer5, Marius Staring4, Berend C Stoel4. 1. Department of Pulmonology, Leiden University Medical Center, Albinusdreef 2, 2333ZA Leiden, The Netherlands. Electronic address: m.k.ninaber@lumc.nl. 2. Department of Pulmonology, Leiden University Medical Center, Albinusdreef 2, 2333ZA Leiden, The Netherlands. 3. Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333ZA Leiden, The Netherlands. 4. Division of Image Processing, Radiology, Leiden University Medical Center, Albinusdreef 2, 2333ZA Leiden, The Netherlands. 5. Department of Rheumatology, Leiden University Medical Center, Albinusdreef 2, 2333ZA Leiden, The Netherlands.
Abstract
INTRODUCTION: Interstitial lung disease occurs frequently in patients with systemic sclerosis (SSc). Quantitative computed tomography (CT) densitometry using the percentile density method may provide a sensitive assessment of lung structure for monitoring parenchymal damage. Therefore, we aimed to evaluate the optimal percentile density score in SSc by quantitative CT densitometry, against pulmonary function. MATERIAL AND METHODS: We investigated 41 SSc patients by chest CT scan, spirometry and gas transfer tests. Lung volumes and the nth percentile density (between 1 and 99%) of the entire lungs were calculated from CT histograms. The nth percentile density is defined as the threshold value of densities expressed in Hounsfield units. A prerequisite for an optimal percentage was its correlation with baseline DLCO %predicted. Two patients showed distinct changes in lung function 2 years after baseline. We obtained CT scans from these patients and performed progression analysis. RESULTS: Regression analysis for the relation between DLCO %predicted and the nth percentile density was optimal at 85% (Perc85). There was significant agreement between Perc85 and DLCO %predicted (R=-0.49, P=0.001) and FVC %predicted (R=-0.64, P<0.001). Two patients showed a marked change in Perc85 over a 2 year period, but the localization of change differed clearly. CONCLUSIONS: We identified Perc85 as optimal lung density parameter, which correlated significantly with DLCO and FVC, confirming a lung parenchymal structure-function relation in SSc. This provides support for future studies to determine whether structural changes do precede lung function decline.
INTRODUCTION:Interstitial lung disease occurs frequently in patients with systemic sclerosis (SSc). Quantitative computed tomography (CT) densitometry using the percentile density method may provide a sensitive assessment of lung structure for monitoring parenchymal damage. Therefore, we aimed to evaluate the optimal percentile density score in SSc by quantitative CT densitometry, against pulmonary function. MATERIAL AND METHODS: We investigated 41 SSc patients by chest CT scan, spirometry and gas transfer tests. Lung volumes and the nth percentile density (between 1 and 99%) of the entire lungs were calculated from CT histograms. The nth percentile density is defined as the threshold value of densities expressed in Hounsfield units. A prerequisite for an optimal percentage was its correlation with baseline DLCO %predicted. Two patients showed distinct changes in lung function 2 years after baseline. We obtained CT scans from these patients and performed progression analysis. RESULTS: Regression analysis for the relation between DLCO %predicted and the nth percentile density was optimal at 85% (Perc85). There was significant agreement between Perc85 and DLCO %predicted (R=-0.49, P=0.001) and FVC %predicted (R=-0.64, P<0.001). Two patients showed a marked change in Perc85 over a 2 year period, but the localization of change differed clearly. CONCLUSIONS: We identified Perc85 as optimal lung density parameter, which correlated significantly with DLCO and FVC, confirming a lung parenchymal structure-function relation in SSc. This provides support for future studies to determine whether structural changes do precede lung function decline.
Authors: Barbara Ruaro; Elisa Baratella; Paola Confalonieri; Marco Confalonieri; Fabio Giuseppe Vassallo; Barbara Wade; Pietro Geri; Riccardo Pozzan; Gaetano Caforio; Cristina Marrocchio; Maria Assunta Cova; Francesco Salton Journal: Diagnostics (Basel) Date: 2021-12-07