Journal article
Pulmonary Hypertension in Association with Lung Disease: Quantitative CT and Artificial Intelligence to the Rescue? State-of-the-Art Review
Diagnostics (Basel), Vol.11(4), p.679
04/01/2021
DOI: 10.3390/diagnostics11040679
PMCID: PMC8070579
PMID: 33918838
Abstract
Accurate phenotyping of patients with pulmonary hypertension (PH) is an integral part of informing disease classification, treatment, and prognosis. The impact of lung disease on PH outcomes and response to treatment remains a challenging area with limited progress. Imaging with computed tomography (CT) plays an important role in patients with suspected PH when assessing for parenchymal lung disease, however, current assessments are limited by their semi-qualitative nature. Quantitative chest-CT (QCT) allows numerical quantification of lung parenchymal disease beyond subjective visual assessment. This has facilitated advances in radiological assessment and clinical correlation of a range of lung diseases including emphysema, interstitial lung disease, and coronavirus disease 2019 (COVID-19). Artificial Intelligence approaches have the potential to facilitate rapid quantitative assessments. Benefits of cross-sectional imaging include ease and speed of scan acquisition, repeatability and the potential for novel insights beyond visual assessment alone. Potential clinical benefits include improved phenotyping and prediction of treatment response and survival. Artificial intelligence approaches also have the potential to aid more focused study of pulmonary arterial hypertension (PAH) therapies by identifying more homogeneous subgroups of patients with lung disease. This state-of-the-art review summarizes recent QCT developments and potential applications in patients with PH with a focus on lung disease.
Details
- Title: Subtitle
- Pulmonary Hypertension in Association with Lung Disease: Quantitative CT and Artificial Intelligence to the Rescue? State-of-the-Art Review
- Creators
- Krit Dwivedi - University of SheffieldMichael Sharkey - Sheffield Teaching Hospitals NHS Foundation TrustRobin Condliffe - University of SheffieldJohanna M. Uthoff - University of SheffieldSamer Alabed - University of SheffieldPeter Metherall - Sheffield Teaching Hospitals NHS Foundation TrustHaiping Lu - InsigneoJim M. Wild - InsigneoEric A. Hoffman - University of IowaAndrew J. Swift - Sheffield Teaching Hospitals NHS Foundation TrustDavid G. Kiely - Insigneo
- Resource Type
- Journal article
- Publication Details
- Diagnostics (Basel), Vol.11(4), p.679
- DOI
- 10.3390/diagnostics11040679
- PMID
- 33918838
- PMCID
- PMC8070579
- NLM abbreviation
- Diagnostics (Basel)
- ISSN
- 2075-4418
- eISSN
- 2075-4418
- Publisher
- Mdpi
- Number of pages
- 20
- Grant note
- 222930/Z/21/Z; 203914/Z/16/; 205188/Z/16/Z / Wellcome Trust
- Language
- English
- Date published
- 04/01/2021
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Internal Medicine
- Record Identifier
- 9984318823002771
Metrics
6 Record Views