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Quantitative Stratification of Diffuse Parenchymal Lung Diseases

Diffuse parenchymal lung diseases (DPLDs) are characterized by widespread pathological changes within the pulmonary tissue that impair the elasticity and gas exchange properties of the lungs. Clinical-radiological diagnosis of these diseases remains challenging and their clinical course is character...

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Autores principales: Raghunath, Sushravya, Rajagopalan, Srinivasan, Karwoski, Ronald A., Maldonado, Fabien, Peikert, Tobias, Moua, Teng, Ryu, Jay H., Bartholmai, Brian J., Robb, Richard A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3968138/
https://www.ncbi.nlm.nih.gov/pubmed/24676019
http://dx.doi.org/10.1371/journal.pone.0093229
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author Raghunath, Sushravya
Rajagopalan, Srinivasan
Karwoski, Ronald A.
Maldonado, Fabien
Peikert, Tobias
Moua, Teng
Ryu, Jay H.
Bartholmai, Brian J.
Robb, Richard A.
author_facet Raghunath, Sushravya
Rajagopalan, Srinivasan
Karwoski, Ronald A.
Maldonado, Fabien
Peikert, Tobias
Moua, Teng
Ryu, Jay H.
Bartholmai, Brian J.
Robb, Richard A.
author_sort Raghunath, Sushravya
collection PubMed
description Diffuse parenchymal lung diseases (DPLDs) are characterized by widespread pathological changes within the pulmonary tissue that impair the elasticity and gas exchange properties of the lungs. Clinical-radiological diagnosis of these diseases remains challenging and their clinical course is characterized by variable disease progression. These challenges have hindered the introduction of robust objective biomarkers for patient-specific prediction based on specific phenotypes in clinical practice for patients with DPLD. Therefore, strategies facilitating individualized clinical management, staging and identification of specific phenotypes linked to clinical disease outcomes or therapeutic responses are urgently needed. A classification schema consistently reflecting the radiological, clinical (lung function and clinical outcomes) and pathological features of a disease represents a critical need in modern pulmonary medicine. Herein, we report a quantitative stratification paradigm to identify subsets of DPLD patients with characteristic radiologic patterns in an unsupervised manner and demonstrate significant correlation of these self-organized disease groups with clinically accepted surrogate endpoints. The proposed consistent and reproducible technique could potentially transform diagnostic staging, clinical management and prognostication of DPLD patients as well as facilitate patient selection for clinical trials beyond the ability of current radiological tools. In addition, the sequential quantitative stratification of the type and extent of parenchymal process may allow standardized and objective monitoring of disease, early assessment of treatment response and mortality prediction for DPLD patients.
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spelling pubmed-39681382014-04-01 Quantitative Stratification of Diffuse Parenchymal Lung Diseases Raghunath, Sushravya Rajagopalan, Srinivasan Karwoski, Ronald A. Maldonado, Fabien Peikert, Tobias Moua, Teng Ryu, Jay H. Bartholmai, Brian J. Robb, Richard A. PLoS One Research Article Diffuse parenchymal lung diseases (DPLDs) are characterized by widespread pathological changes within the pulmonary tissue that impair the elasticity and gas exchange properties of the lungs. Clinical-radiological diagnosis of these diseases remains challenging and their clinical course is characterized by variable disease progression. These challenges have hindered the introduction of robust objective biomarkers for patient-specific prediction based on specific phenotypes in clinical practice for patients with DPLD. Therefore, strategies facilitating individualized clinical management, staging and identification of specific phenotypes linked to clinical disease outcomes or therapeutic responses are urgently needed. A classification schema consistently reflecting the radiological, clinical (lung function and clinical outcomes) and pathological features of a disease represents a critical need in modern pulmonary medicine. Herein, we report a quantitative stratification paradigm to identify subsets of DPLD patients with characteristic radiologic patterns in an unsupervised manner and demonstrate significant correlation of these self-organized disease groups with clinically accepted surrogate endpoints. The proposed consistent and reproducible technique could potentially transform diagnostic staging, clinical management and prognostication of DPLD patients as well as facilitate patient selection for clinical trials beyond the ability of current radiological tools. In addition, the sequential quantitative stratification of the type and extent of parenchymal process may allow standardized and objective monitoring of disease, early assessment of treatment response and mortality prediction for DPLD patients. Public Library of Science 2014-03-27 /pmc/articles/PMC3968138/ /pubmed/24676019 http://dx.doi.org/10.1371/journal.pone.0093229 Text en © 2014 Raghunath et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Raghunath, Sushravya
Rajagopalan, Srinivasan
Karwoski, Ronald A.
Maldonado, Fabien
Peikert, Tobias
Moua, Teng
Ryu, Jay H.
Bartholmai, Brian J.
Robb, Richard A.
Quantitative Stratification of Diffuse Parenchymal Lung Diseases
title Quantitative Stratification of Diffuse Parenchymal Lung Diseases
title_full Quantitative Stratification of Diffuse Parenchymal Lung Diseases
title_fullStr Quantitative Stratification of Diffuse Parenchymal Lung Diseases
title_full_unstemmed Quantitative Stratification of Diffuse Parenchymal Lung Diseases
title_short Quantitative Stratification of Diffuse Parenchymal Lung Diseases
title_sort quantitative stratification of diffuse parenchymal lung diseases
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3968138/
https://www.ncbi.nlm.nih.gov/pubmed/24676019
http://dx.doi.org/10.1371/journal.pone.0093229
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