| Title |
Computed Tomographic Biomarkers in Idiopathic Pulmonary Fibrosis. The Future of Quantitative Analysis
|
|---|---|
| Published in |
American Journal of Respiratory & Critical Care Medicine, January 2019
|
| DOI | 10.1164/rccm.201803-0444pp |
| Pubmed ID | |
| Authors |
Xiaoping Wu, Grace H Kim, Margaret L Salisbury, David Barber, Brian J Bartholmai, Kevin K Brown, Craig S Conoscenti, Jan De Backer, Kevin R Flaherty, James F Gruden, Eric A Hoffman, Stephen M Humphries, Joseph Jacob, Toby M Maher, Ganesh Raghu, Luca Richeldi, Brian D Ross, Rozsa Schlenker-Herceg, Nicola Sverzellati, Athol U Wells, Fernando J Martinez, David A Lynch, Jonathan Goldin, Simon L F Walsh |
| Abstract |
Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease with great variability in disease severity and rate of progression. The need for a reliable, sensitive, and objective biomarker to track disease progression and response to therapy remains a great challenge in IPF clinical trials. Over the past decade, quantitative computed tomography (QCT) has emerged as an area of intensive research to address this need. We have gathered a group of pulmonologists, radiologists and scientists with expertise in this area to define the current status and future promise of this imaging technique in the evaluation and management of IPF. In this Pulmonary Perspective, we review the development and validation of six computer-based QCT methods and offer insight into the optimal use of an imaging-based biomarker as a tool for prognostication, prediction of response to therapy, and potential surrogate endpoint in future therapeutic trials. |
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X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Venezuela, Bolivarian Republic of | 4 | 44% |
| Lebanon | 1 | 11% |
| Unknown | 4 | 44% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 89% |
| Science communicators (journalists, bloggers, editors) | 1 | 11% |
Mendeley demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 113 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 19 | 17% |
| Other | 18 | 16% |
| Student > Ph. D. Student | 9 | 8% |
| Student > Postgraduate | 7 | 6% |
| Student > Master | 6 | 5% |
| Other | 16 | 14% |
| Unknown | 38 | 34% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 39 | 35% |
| Computer Science | 5 | 4% |
| Engineering | 5 | 4% |
| Agricultural and Biological Sciences | 4 | 4% |
| Physics and Astronomy | 3 | 3% |
| Other | 10 | 9% |
| Unknown | 47 | 42% |