Joseph Jacob1, Brian J Bartholmai2, Srinivasan Rajagopalan2, Maria Kokosi3, Ryoko Egashira4, Anne Laure Brun5, Arjun Nair6, Simon L F Walsh7, Ronald Karwoski8, Athol U Wells3. 1. Division of Radiology, Mayo Clinic Rochester, Rochester, MN, USA. joseph.jacob@nhs.net. 2. Division of Radiology, Mayo Clinic Rochester, Rochester, MN, USA. 3. Interstitial Lung Disease Unit, Royal Brompton Hospital, Royal Brompton and Harefield NHS Foundation Trust, London, UK. 4. Department of Radiology, Saga Daigaku, City, Saga, Japan. 5. Department of Radiology, Whittington Hospital, London, UK. 6. Department of Radiology, Guys and St Thomas' NHS Foundation Trust, London, UK. 7. Department of Radiology, Kings College Hospital NHS Foundation Trust, London, UK. 8. Department of Physiology and Biomedical Engineering, Mayo Clinic Rochester, Rochester, Minnesota, USA.
Abstract
OBJECTIVES: To determine whether computer-based CT quantitation of change can improve on visual change quantification of parenchymal features in IPF. METHODS: Sixty-six IPF patients with serial CT imaging (6-24 months apart) had CT features scored visually and with a computer software tool: ground glass opacity, reticulation and honeycombing (all three variables summed as interstitial lung disease extent [ILD]) and emphysema. Pulmonary vessel volume (PVV) was estimated by computer only. Relationships between changes in CT features and forced vital capacity (FVC) were examined using univariate and multivariate linear regression analyses. RESULTS: On univariate analysis, changes in computer variables demonstrated stronger linkages to FVC change than changes in visual scores (CALIPER ILD:R2=0.53, p<0.0001; Visual ILD:R2=0.16, p=0.001). PVV increase correlated most strongly with relative FVC change (R2=0.57). When PVV constituents (vessel size and location) were examined, an increase in middle zone vessels linked most strongly to FVC decline (R2=0.57) and was independent of baseline disease severity (characterised by CT fibrosis extent, FVC, or DLco). CONCLUSIONS: An increase in PVV, specifically an increase in middle zone lung vessels, was the strongest CT determinant of FVC decline in IPF and was independent of baseline disease severity. KEY POINTS: • Computer analysis improves on visual CT scoring in evaluating deterioration on CT • Increasing pulmonary vessel volume is the strongest CT predictor of functional deterioration • Increasing pulmonary vessel volume predicts functional decline independent of baseline disease severity.
OBJECTIVES: To determine whether computer-based CT quantitation of change can improve on visual change quantification of parenchymal features in IPF. METHODS: Sixty-six IPF patients with serial CT imaging (6-24 months apart) had CT features scored visually and with a computer software tool: ground glass opacity, reticulation and honeycombing (all three variables summed as interstitial lung disease extent [ILD]) and emphysema. Pulmonary vessel volume (PVV) was estimated by computer only. Relationships between changes in CT features and forced vital capacity (FVC) were examined using univariate and multivariate linear regression analyses. RESULTS: On univariate analysis, changes in computer variables demonstrated stronger linkages to FVC change than changes in visual scores (CALIPER ILD:R2=0.53, p<0.0001; Visual ILD:R2=0.16, p=0.001). PVV increase correlated most strongly with relative FVC change (R2=0.57). When PVV constituents (vessel size and location) were examined, an increase in middle zone vessels linked most strongly to FVC decline (R2=0.57) and was independent of baseline disease severity (characterised by CT fibrosis extent, FVC, or DLco). CONCLUSIONS: An increase in PVV, specifically an increase in middle zone lung vessels, was the strongest CT determinant of FVC decline in IPF and was independent of baseline disease severity. KEY POINTS: • Computer analysis improves on visual CT scoring in evaluating deterioration on CT • Increasing pulmonary vessel volume is the strongest CT predictor of functional deterioration • Increasing pulmonary vessel volume predicts functional decline independent of baseline disease severity.
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