Literature DB >> 26003389

Classification of usual interstitial pneumonia in patients with interstitial lung disease: assessment of a machine learning approach using high-dimensional transcriptional data.

Su Yeon Kim1, James Diggans1, Dan Pankratz1, Jing Huang1, Moraima Pagan1, Nicole Sindy1, Ed Tom1, Jessica Anderson1, Yoonha Choi1, David A Lynch2, Mark P Steele3, Kevin R Flaherty4, Kevin K Brown2, Humam Farah5, Michael J Bukstein5, Annie Pardo6, Moisés Selman7, Paul J Wolters8, Steven D Nathan9, Thomas V Colby10, Jeffrey L Myers4, Anna-Luise A Katzenstein11, Ganesh Raghu12, Giulia C Kennedy13.   

Abstract

BACKGROUND: Idiopathic pulmonary fibrosis is a progressive fibrotic lung disease that distorts pulmonary architecture, leading to hypoxia, respiratory failure, and death. Diagnosis is difficult because other interstitial lung diseases have similar radiological and histopathological characteristics. A usual interstitial pneumonia pattern is a hallmark of idiopathic pulmonary fibrosis and is essential for its diagnosis. We aimed to develop a molecular test that distinguishes usual interstitial pneumonia from other interstitial lung diseases in surgical lung biopsy samples. The eventual goal of this research is to develop a method to diagnose idiopathic pulmonary fibrosis without the patient having to undergo surgery.
METHODS: We collected surgical lung biopsy samples from patients with various interstitial lung diseases at 11 hospitals in North America. Pathology diagnoses were confirmed by an expert panel. We measured RNA expression levels for 33 297 transcripts on microarrays in all samples. A classifier algorithm was trained on one set of samples and tested in a second set. We subjected a subset of samples to next-generation RNA sequencing (RNAseq) generating expression levels on 55 097 transcripts, and assessed a classifier trained on RNAseq data by cross-validation.
FINDINGS: We took 125 surgical lung biopsies from 86 patients. 58 samples were identified by the expert panel as usual interstitial pneumonia, 23 as non-specific interstitial pneumonia, 16 as hypersensitivity pneumonitis, four as sarcoidosis, four as respiratory bronchiolitis, two as organising pneumonia, and 18 as subtypes other than usual interstitial pneumonia. The microarray classifier was trained on 77 samples and was assessed in a test set of 48 samples, for which it had a specificity of 92% (95% CI 81-100) and a sensitivity of 82% (64-95). Based on a subset of 36 samples, the RNAseq classifier had a specificity of 95% (84-100) and a sensitivity of 59% (35-82).
INTERPRETATION: Our results show that the development of a genomic signature that predicts usual interstitial pneumonia is feasible. These findings are an important first step towards the development of a molecular test that could be applied to bronchoscopy samples, thus avoiding surgery in the diagnosis of idiopathic pulmonary fibrosis. FUNDING: Veracyte.
Copyright © 2015 Elsevier Ltd. All rights reserved.

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Year:  2015        PMID: 26003389     DOI: 10.1016/S2213-2600(15)00140-X

Source DB:  PubMed          Journal:  Lancet Respir Med        ISSN: 2213-2600            Impact factor:   30.700


  27 in total

1.  AJRCCM: 100-Year Anniversary. Progress in Interstitial Lung Disease.

Authors:  Robert J Kaner; Kevin K Brown; Fernando J Martinez
Journal:  Am J Respir Crit Care Med       Date:  2017-05-01       Impact factor: 21.405

Review 2.  Molecular approach to the classification of chronic fibrosing lung disease-there and back again.

Authors:  Stijn E Verleden; Peter Braubach; Mark Kuehnel; Nicolas Dickgreber; Emily Brouwer; Pauline Tittmann; Florian Laenger; Danny Jonigk
Journal:  Virchows Arch       Date:  2020-11-09       Impact factor: 4.064

Review 3.  Progress in Understanding and Treating Idiopathic Pulmonary Fibrosis.

Authors:  Jonathan A Kropski; Timothy S Blackwell
Journal:  Annu Rev Med       Date:  2019-01-27       Impact factor: 13.739

Review 4.  Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging.

Authors:  Tara A Retson; Alexandra H Besser; Sean Sall; Daniel Golden; Albert Hsiao
Journal:  J Thorac Imaging       Date:  2019-05       Impact factor: 3.000

5.  Genomic Classifiers in Diagnosing Interstitial Lung Disease: Finding the Right Place at the Right Time.

Authors:  Gillian C Goobie; Daniel J Kass
Journal:  Ann Am Thorac Soc       Date:  2022-06

6.  Traction Bronchiectasis/Bronchiolectasis is Associated with Interstitial Lung Abnormality Mortality.

Authors:  Tomoyuki Hida; Mizuki Nishino; Takuya Hino; Junwei Lu; Rachel K Putman; Elias F Gudmundsson; Tetsuro Araki; Vladimir I Valtchinov; Osamu Honda; Masahiro Yanagawa; Yoshitake Yamada; Akinori Hata; Masahiro Jinzaki; Noriyuki Tomiyama; Hiroshi Honda; Raul San Jose Estepar; George R Washko; Takeshi Johkoh; David C Christiani; David A Lynch; Vilmundur Gudnason; Gunnar Gudmundsson; Gary M Hunninghake; Hiroto Hatabu
Journal:  Eur J Radiol       Date:  2020-05-18       Impact factor: 3.528

Review 7.  The diagnosis of idiopathic pulmonary fibrosis: current and future approaches.

Authors:  Fernando J Martinez; Alison Chisholm; Harold R Collard; Kevin R Flaherty; Jeffrey Myers; Ganesh Raghu; Simon L F Walsh; Eric S White; Luca Richeldi
Journal:  Lancet Respir Med       Date:  2016-12-06       Impact factor: 30.700

Review 8.  Molecular Markers and the Promise of Precision Medicine for Interstitial Lung Disease.

Authors:  Chad A Newton; Erica L Herzog
Journal:  Clin Chest Med       Date:  2021-06       Impact factor: 4.967

Review 9.  Idiopathic pulmonary fibrosis: Disease mechanisms and drug development.

Authors:  Paolo Spagnolo; Jonathan A Kropski; Mark G Jones; Joyce S Lee; Giulio Rossi; Theodoros Karampitsakos; Toby M Maher; Argyrios Tzouvelekis; Christopher J Ryerson
Journal:  Pharmacol Ther       Date:  2020-12-24       Impact factor: 13.400

Review 10.  Recent advances in understanding idiopathic pulmonary fibrosis.

Authors:  Cécile Daccord; Toby M Maher
Journal:  F1000Res       Date:  2016-05-31
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