Quantitative CT Analysis of Diffuse Lung Disease.
Alicia ChenRonald A KarwoskiDavid S GieradaBrian J BartholmaiChi Wan KooPublished in: Radiographics : a review publication of the Radiological Society of North America, Inc (2019)
Quantitative analysis of thin-section CT of the chest has a growing role in the clinical evaluation and management of diffuse lung diseases. This heterogeneous group includes diseases with markedly different prognoses and treatment options. Quantitative tools can assist in both accurate diagnosis and longitudinal management by improving characterization and quantification of disease and increasing the reproducibility of disease severity assessment. Furthermore, a quantitative index of disease severity may serve as a useful tool or surrogate endpoint in evaluating treatment efficacy. The authors explore the role of quantitative imaging tools in the evaluation and management of diffuse lung diseases. Lung parenchymal features can be classified with threshold, histogram, morphologic, and texture-analysis-based methods. Quantitative CT analysis has been applied in obstructive, infiltrative, and restrictive pulmonary diseases including emphysema, cystic fibrosis, asthma, idiopathic pulmonary fibrosis, hypersensitivity pneumonitis, connective tissue-related interstitial lung disease, and combined pulmonary fibrosis and emphysema. Some challenges limiting the development and practical application of current quantitative analysis tools include the quality of training data, lack of standard criteria to validate the accuracy of the results, and lack of real-world assessments of the impact on outcomes. Artifacts such as patient motion or metallic beam hardening, variation in inspiratory effort, differences in image acquisition and reconstruction techniques, or inaccurate preprocessing steps such as segmentation of anatomic structures may lead to inaccurate classification. Despite these challenges, as new techniques emerge, quantitative analysis is developing into a viable tool to supplement the traditional visual assessment of diffuse lung diseases and to provide decision support regarding diagnosis, prognosis, and longitudinal evaluation of disease. ©RSNA, 2019.
Keyphrases
- idiopathic pulmonary fibrosis
- interstitial lung disease
- high resolution
- pulmonary fibrosis
- contrast enhanced
- image quality
- systemic sclerosis
- clinical evaluation
- cystic fibrosis
- deep learning
- low grade
- chronic obstructive pulmonary disease
- computed tomography
- lung function
- dual energy
- machine learning
- rheumatoid arthritis
- pulmonary hypertension
- cross sectional
- magnetic resonance imaging
- metabolic syndrome
- adipose tissue
- high grade
- electronic health record
- air pollution
- case report
- artificial intelligence
- big data
- smoking cessation
- pet ct
- combination therapy
- weight loss