Computed Tomographic Biomarkers in Idiopathic Pulmonary Fibrosis. The Future of Quantitative Analysis

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.

[1]  N. Müller,et al.  Fibrosing alveolitis: CT-pathologic correlation. , 1986, Radiology.

[2]  P. Paré,et al.  Differential diagnosis of bronchiolitis obliterans with organizing pneumonia and usual interstitial pneumonia: clinical, functional, and radiologic findings. , 1987, Radiology.

[3]  N. Müller,et al.  Disease activity in idiopathic pulmonary fibrosis: CT and pathologic correlation. , 1987, Radiology.

[4]  R R Miller,et al.  Hypersensitivity pneumonitis: evaluation with CT. , 1989, Radiology.

[5]  D. Hansell,et al.  The predictive value of appearances on thin-section computed tomography in fibrosing alveolitis. , 1993, The American review of respiratory disease.

[6]  J A Merchant,et al.  High-resolution CT-derived measures of lung density are valid indexes of interstitial lung disease. , 1994, Journal of applied physiology.

[7]  U Raff,et al.  Automated discrimination and quantification of idiopathic pulmonary fibrosis from normal lung parenchyma using generalized fractal dimensions in high-resolution computed tomography images. , 1995, Academic radiology.

[8]  E. Hoffman,et al.  Quantification of pulmonary emphysema from lung computed tomography images. , 1997, American journal of respiratory and critical care medicine.

[9]  M A Schork,et al.  Idiopathic pulmonary fibrosis: predicting response to therapy and survival. , 1998, American journal of respiratory and critical care medicine.

[10]  E. Hoffman,et al.  Computer recognition of regional lung disease patterns. , 1999, American journal of respiratory and critical care medicine.

[11]  R. Temple,et al.  Are surrogate markers adequate to assess cardiovascular disease drugs? , 1999, JAMA.

[12]  E. Hoffman,et al.  Interstitial lung disease: A quantitative study using the adaptive multiple feature method. , 1999, American journal of respiratory and critical care medicine.

[13]  D. DeMets,et al.  Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework , 2001, Clinical pharmacology and therapeutics.

[14]  J. Egan,et al.  Pulmonary function in idiopathic pulmonary fibrosis and referral for lung transplantation. , 2001, American journal of respiratory and critical care medicine.

[15]  Gary K Grunwald,et al.  Quantitative CT indexes in idiopathic pulmonary fibrosis: relationship with physiologic impairment. , 2003, Radiology.

[16]  F. Martinez,et al.  Radiological versus histological diagnosis in UIP and NSIP: survival implications , 2003, Thorax.

[17]  F. Martinez,et al.  Prognostic implications of physiologic and radiographic changes in idiopathic interstitial pneumonia. , 2003, American journal of respiratory and critical care medicine.

[18]  David A Lynch,et al.  High-resolution computed tomography in idiopathic pulmonary fibrosis: diagnosis and prognosis. , 2005, American journal of respiratory and critical care medicine.

[19]  Ye Xu,et al.  MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies , 2006, IEEE Transactions on Medical Imaging.

[20]  E. V. van Beek,et al.  Computer-aided classification of interstitial lung diseases via MDCT: 3D adaptive multiple feature method (3D AMFM). , 2006, Academic radiology.

[21]  Sumit K. Shah,et al.  CAD in clinical trials: Current role and architectural requirements , 2007, Comput. Medical Imaging Graph..

[22]  Richard A. Robb,et al.  High resolution multidetector CT-aided tissue analysis and quantification of lung fibrosis. , 2007 .

[23]  D. Hansell,et al.  Interstitial lung disease in systemic sclerosis: a simple staging system. , 2008, American journal of respiratory and critical care medicine.

[24]  Gary K Grunwald,et al.  Idiopathic pulmonary fibrosis: physiologic tests, quantitative CT indexes, and CT visual scores as predictors of mortality. , 2008, Radiology.

[25]  N. Müller,et al.  Fleischner Society: glossary of terms for thoracic imaging. , 2008, Radiology.

[26]  F. Martinez,et al.  Accuracy of high-resolution CT in the diagnosis of diffuse lung disease: effect of predominance and distribution of findings. , 2008, AJR. American journal of roentgenology.

[27]  N. Müller,et al.  Reader accuracy and confidence in diagnosing diffuse lung disease on high-resolution computed tomography of the lungs: impact of sampling frequency , 2008, Acta radiologica.

[28]  N. Müller,et al.  Computed tomography findings in pathological usual interstitial pneumonia: relationship to survival. , 2008, American journal of respiratory and critical care medicine.

[29]  T. E. King,et al.  Usual interstitial pneumonia in rheumatoid arthritis-associated interstitial lung disease , 2009, European Respiratory Journal.

[30]  R. Elashoff,et al.  Treatment of scleroderma-interstitial lung disease with cyclophosphamide is associated with less progressive fibrosis on serial thoracic high-resolution CT scan than placebo: findings from the scleroderma lung study. , 2009, Chest.

[31]  E. Baraldi,et al.  Endpoints in respiratory diseases , 2011, European Journal of Clinical Pharmacology.

[32]  D. Schroeder,et al.  Incidence, prevalence, and clinical course of idiopathic pulmonary fibrosis: a population-based study. , 2010, Chest.

[33]  Wilfried De Backer,et al.  Validation of computational fluid dynamics in CT-based airway models with SPECT/CT. , 2010, Radiology.

[34]  Raúl San José Estépar,et al.  Identification of early interstitial lung disease in smokers from the COPDGene Study. , 2010, Academic radiology.

[35]  Yutaka Kawata,et al.  Long-term follow-up high-resolution CT findings in non-specific interstitial pneumonia , 2010, Thorax.

[36]  H. Collard,et al.  Clinical course and prediction of survival in idiopathic pulmonary fibrosis. , 2011, American journal of respiratory and critical care medicine.

[37]  W. De Backer,et al.  The acute effect of budesonide/formoterol in COPD: a multi-slice computed tomography and lung function study , 2011, European Respiratory Journal.

[38]  R. Katz,et al.  Biomarkers and surrogate markers: An FDA perspective , 2004, NeuroRX.

[39]  N. Müller,et al.  Acute exacerbation of idiopathic pulmonary fibrosis: high-resolution CT scores predict mortality , 2011, European Radiology.

[40]  R. Robb,et al.  Can Progression of Fibrosis as Assessed by Computer-Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER) Predict Outcomes in Patients With Idiopathic Pulmonary Fibrosis? , 2011 .

[41]  A. Italiano,et al.  Prognostic or predictive? It's time to get back to definitions! , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[42]  A. Nicholson,et al.  Fibrotic idiopathic interstitial pneumonias: HRCT findings that predict mortality , 2011, European Radiology.

[43]  Andrew Y. Ng,et al.  Learning Feature Representations with K-Means , 2012, Neural Networks: Tricks of the Trade.

[44]  Joyce S Lee,et al.  A Multidimensional Index and Staging System for Idiopathic Pulmonary Fibrosis , 2012, Annals of Internal Medicine.

[45]  A. Wells Forced vital capacity as a primary end point in idiopathic pulmonary fibrosis treatment trials: making a silk purse from a sow's ear , 2012, Thorax.

[46]  Eric A Hoffman,et al.  Systems for lung volume standardization during static and dynamic MDCT-based quantitative assessment of pulmonary structure and function. , 2012, Academic radiology.

[47]  H. Collard,et al.  Idiopathic pulmonary fibrosis: clinically meaningful primary endpoints in phase 3 clinical trials. , 2012, American journal of respiratory and critical care medicine.

[48]  P F Judy,et al.  Reference standard and statistical model for intersite and temporal comparisons of CT attenuation in a multicenter quantitative lung study. , 2012, Medical physics.

[49]  T. Hartman,et al.  Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis , 2013, European Respiratory Journal.

[50]  Richard A. Robb,et al.  Quantitative Computed Tomography Imaging of Interstitial Lung Diseases , 2013, Journal of thoracic imaging.

[51]  Eric A Hoffman,et al.  Development of Quantitative Computed Tomography Lung Protocols , 2013, Journal of thoracic imaging.

[52]  Richard A. Robb,et al.  Landscaping the effect of CT reconstruction parameters: Robust Interstitial Pulmonary Fibrosis quantitation , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[53]  S. Sahn,et al.  All-cause mortality rate in patients with idiopathic pulmonary fibrosis. Implications for the design and execution of clinical trials. , 2014, American journal of respiratory and critical care medicine.

[54]  A. Nicholson,et al.  An integrated clinicoradiological staging system for pulmonary sarcoidosis: a case-cohort study. , 2014, The Lancet. Respiratory medicine.

[55]  R. Robb,et al.  Quantitative Stratification of Diffuse Parenchymal Lung Diseases , 2014, PloS one.

[56]  Philip F. Judy,et al.  Normalizing computed tomography data reconstructed with different filter kernels: effect on emphysema quantification , 2015, European Radiology.

[57]  D. Lynch,et al.  CT staging and monitoring of fibrotic interstitial lung diseases in clinical practice and treatment trials: a position paper from the Fleischner Society. , 2015, The Lancet. Respiratory medicine.

[58]  Kyle J Myers,et al.  Quantitative imaging biomarkers: A review of statistical methods for computer algorithm comparisons , 2014, Statistical methods in medical research.

[59]  B. Chowdhury,et al.  Forced vital capacity in idiopathic pulmonary fibrosis--FDA review of pirfenidone and nintedanib. , 2015, The New England journal of medicine.

[60]  J. Goldin,et al.  Comparison of the quantitative CT imaging biomarkers of idiopathic pulmonary fibrosis at baseline and early change with an interval of 7 months. , 2015, Academic radiology.

[61]  W. De Backer,et al.  International Journal of Copd Dovepress Pulmonary Vascular Effects of Pulsed Inhaled Nitric Oxide in Copd Patients with Pulmonary Hypertension , 2022 .

[62]  W. De Backer,et al.  Functional respiratory imaging (FRI) for optimizing therapy development and patient care , 2016, Expert review of respiratory medicine.

[63]  Michael F. McNitt-Gray,et al.  Robustness-Driven Feature Selection in Classification of Fibrotic Interstitial Lung Disease Patterns in Computed Tomography Using 3D Texture Features , 2016, IEEE Transactions on Medical Imaging.

[64]  P. Beddy,et al.  Recurrent lower limb venous thrombosis associated with a congenitally absent infrarenal inferior vena cava. , 2016, QJM : monthly journal of the Association of Physicians.

[65]  Joseph Jacob,et al.  Automated Quantitative Computed Tomography Versus Visual Computed Tomography Scoring in Idiopathic Pulmonary Fibrosis: Validation Against Pulmonary Function , 2016, Journal of thoracic imaging.

[66]  David Couper,et al.  SPIROMICS Protocol for Multicenter Quantitative Computed Tomography to Phenotype the Lungs. , 2016, American journal of respiratory and critical care medicine.

[67]  Raúl San José Estépar,et al.  Association Between Interstitial Lung Abnormalities and All-Cause Mortality. , 2016, JAMA.

[68]  P. Dorinsky,et al.  Assessment of lung deposition and analysis of the effect of fluticasone/salmeterol hydrofluoroalkane (HFA) pressurized metered dose inhaler (pMDI) in stable persistent asthma patients using functional respiratory imaging , 2016, Expert review of respiratory medicine.

[69]  U. Tateishi,et al.  Chronic Hypersensitivity Pneumonitis With a Usual Interstitial Pneumonia-Like Pattern: Correlation Between Histopathologic and Clinical Findings. , 2016, Chest.

[70]  Ruth Hartley,et al.  Comparison of CT-based Lobar Ventilation with 3He MR Imaging Ventilation Measurements. , 2016, Radiology.

[71]  M. Wijsenbeek,et al.  Recombinant human pentraxin-2 therapy in patients with idiopathic pulmonary fibrosis: safety, pharmacokinetics and exploratory efficacy , 2016, European Respiratory Journal.

[72]  G. Raghu,et al.  FG-3019 anti-connective tissue growth factor monoclonal antibody: results of an open-label clinical trial in idiopathic pulmonary fibrosis , 2016, European Respiratory Journal.

[73]  D. Hansell,et al.  Evaluation of computer-based computer tomography stratification against outcome models in connective tissue disease-related interstitial lung disease: a patient outcome study , 2016, BMC Medicine.

[74]  B. Ley Clarity on Endpoints for Clinical Trials in Idiopathic Pulmonary Fibrosis. , 2017, Annals of the American Thoracic Society.

[75]  J. Goldin,et al.  Late Breaking Abstract - Responder phenotyping using functional respiratory imaging (FRI) in IPF patients treated with anti-CGTG monoclonal antibody FG3019 , 2017 .

[76]  Raúl San José Estépar,et al.  Clinical and Genetic Associations of Objectively Identified Interstitial Changes in Smokers , 2017, Chest.

[77]  J. Goldin,et al.  Late Breaking Abstract - Assessment of disease progression in IPF patients using Functional Respiratory Imaging (FRI) , 2017 .

[78]  D. Hansell,et al.  Chronic hypersensitivity pneumonitis: identification of key prognostic determinants using automated CT analysis , 2017, BMC Pulmonary Medicine.

[79]  D. Lynch,et al.  Idiopathic Pulmonary Fibrosis: The Association between the Adaptive Multiple Features Method and Fibrosis Outcomes , 2017, American journal of respiratory and critical care medicine.

[80]  Edwin J R van Beek,et al.  Idiopathic Pulmonary Fibrosis: Data-driven Textural Analysis of Extent of Fibrosis at Baseline and 15-Month Follow-up. , 2017, Radiology.

[81]  D. Hansell,et al.  Functional and prognostic effects when emphysema complicates idiopathic pulmonary fibrosis , 2017, European Respiratory Journal.

[82]  Joseph Jacob,et al.  Serial automated quantitative CT analysis in idiopathic pulmonary fibrosis: functional correlations and comparison with changes in visual CT scores , 2018, European Radiology.

[83]  D. Hansell,et al.  Mortality prediction in idiopathic pulmonary fibrosis: evaluation of computer-based CT analysis with conventional severity measures , 2017, European Respiratory Journal.

[84]  A. Matsumoto,et al.  Comparison of Total Lung Capacity Determined by Plethysmography With Computed Tomographic Segmentation Using CALIPER , 2017, Journal of thoracic imaging.

[85]  S. Walsh,et al.  Likelihood of pulmonary hypertension in patients with idiopathic pulmonary fibrosis and emphysema , 2018, Respirology.

[86]  S. Raghunath,et al.  Short-term Automated Quantification of Radiologic Changes in the Characterization of Idiopathic Pulmonary Fibrosis Versus Nonspecific Interstitial Pneumonia and Prediction of Long-term Survival , 2017, Journal of thoracic imaging.

[87]  S. Dupont,et al.  Safety, tolerability, pharmacokinetics, and pharmacodynamics of GLPG1690, a novel autotaxin inhibitor, to treat idiopathic pulmonary fibrosis (FLORA): a phase 2a randomised placebo-controlled trial. , 2018, The Lancet. Respiratory medicine.