Progress in Idiopathic Pulmonary Fibrosis

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1 Progress in Idiopathic Pulmonary Fibrosis David A. Lynch, MB Disclosures Progress in Idiopathic Pulmonary Fibrosis David A Lynch, MB Consultant: t Research support: Perceptive Imaging Boehringer Ingelheim Genentech Gilead Intermune Veracyte Pfizer Centocor, Inc Siemens Inc NHLBI Outline Diagnostic criteria i Advances in management Complications of IPF Atypical UIP Quantitative CT of lung fibrosis 2013 ATS-ERS Classification of idiopathic interstitial pneumonia Category Morphologic Clinical-radiologic-pathologic diagnosis Chronic fibrosing IP Usual IP Idiopathic pulmonary fibrosis Nonspecific IP Idiopathic NSIP Smoking-related IP Desquamative IP Desquamative IP Respiratory bronchiolitis Respiratory bronchiolitis ILD Acute/subacute IP Organizing pneumonia Cryptogenic organizing pneumonia Diffuse alveolar damage Acute IP Rare entities Lymphoid IP Idiopathic LIP Pleuroparenchymal fibroelastosis Idiopathic pleuroparenchymal fibroelastosis Unclassifiable idiopathic interstitial pneumonia UIP Honeycombing with or without traction bronchiectasis Diagnosis and Management. AJRCCM, 2011 Mar 15;183(6):

2 SUNDAY UIP Honeycombing with or without traction bronchiectasis Possible UIP Possible UIP Inconsistent with UIP Upper or midlung Peribronchovascular Extensive ground glass abnormality (>reticular) Profuse micronodules (bilateral, predominantly upper lobes) Discrete cysts (multiple, bilateral, away from areas of honeycombing) Diffuse mosaic attenuation (bilateral, in three or more lobes) Consolidation in segments or lobes Honeycombing in UIP Definition of honeycombing Present in 70-80% of cases of UIP Strongest indicator of UIP on CT Median survival UIP with honeycombing: 2.1 years UIP without honeycombing: 5.8 years Clustered cysts Row of subpleural cysts Usually < 5 mm in diameter Hunninghake GW, et al. Chest 2003;124: Elliot TL. J Comput Assist Tomogr 2005;29: Flaherty KR, et al. Thorax 2003;58:

3 Interobserver Variability in the Assessment of Honeycombing 80 single CT images from different patients Selected to include substantial numbers of cases with atypical honeycombing and non-honeycomb cystic spaces Weighted kappa values ranged from 0.40 to 0.58 Watadani et al. Radiology. 2013;266(3): (3) Study Predictive value of CT diagnosis of UIP Correctness of first choice diagnosis of UIP Correctness of confident first choice diagnosis Mathieson 89% 95% 72% Hunninghake 85% 96% 52% Flaherty 100% 100% 63% Tsubamoto 100% 91% 9% Elliot 88% 88% 50% Silva 84% 100% 67% % cases of UIP without confident CT diagnosis UIP: atypical features 55 subjects with biopsy- proven UIP 20 had CT diagnosis of UIP 34 had CT read as low probability for UIP. In these cases the CT diagnoses were NSIP (53%) Chronic HP (12%) Sarcoid (9%) Concordance between CT and pathologic diagnoses in IPFNET clinical trials Pathological diagnosis CT diagnosis Total Definite Probable Possible Not UIP UIP UIP UIP UIP (80%) 17 (17%) 1 (1%) 2 (2%) Possible UIP (80%) 9 (14%) 4 (6%) 0 Inconsistent (73%) 16 (21%) 4(5%) 0 with UIP Sverzellati et al. Radiology 2010: 254; Yagihashi et al. In preparation Does an additional HRCT category improve diagnostic specificity in IPF? UIP on Histology CT diagnosis n Probable/ definite Possible Not Considered d Definite (65%) 3 (12%) 6 (23%) Probable (82%) 5 (15%) 1 (3%) Indeterminate (54%) 12 (17%) 21 (29%) Inconsistent (51%) 11 (15%) 23 (33%) Acute exacerbation of IPF Baseline 5 months later Chung et al. In press, Chest

4 UIP: lung cancer UIP: lung cancer SUNDAY Baseline + 12 months + 18 months + 18 months UIP: lung cancer Chronic hypersensitivity pneumonitis, UIP, NSIP Prevalence ranges from 5-15% Often peripheral, lower lobe May be multifocal Kishi et al., JCAT, 2006;30:95-9 CHP UIP NSIP n p Ground-glass opacities 36 (100) 44 (96) 50 (100) 0.15 Reticulation 36 (100) 46 (100) 50 (100) N/A Honeycombing 23 (64) 31 (67) 4 (8) <0.001 Lobular decreased 29 (81) 20 (43) 17 (34) < attenuation Centrilobular nodules 20 (56) 7 (15) 7 (14) <0.001 Subpleural sparing 4 (11) 2 (4) 32 (64*) <0.001 Lower lung 11 (31) 38 (83) 47 (94) <0.001 Silva et al. Radiology 2008;246: Outcomes of clinical trials in IPF: IPFNET Outcomes of clinical trials in IPF: IPFNET Warfarin vs placebo: time to Sildenafil vs placebo: time to death or hospitalization death Prednisone-azathioprine-n-Acetylcysteine y vs placebo: time to death or hospitalization n-acetylcysteine y vs placebo: change in FVC Warfarin Placebo Placebo Sildenafil

5 Perfenidone vs placebo: progression-free survival Outcomes of clinical trials in IPF Nintedanib vs placebo: change in FVC Perfenidone Definite UIP on CT Possible UIP on CT, with biopsy confirmation of UIP CT Entry criteria Nintedanib Absence of atypical features on CT Definite honeycombing with basal and peripheral or and traction bronchiectasis with basal and peripheral Richeldi et al. Respir Med. 2014;108(7): King et al. N Engl J Med. 2014;370(22): Multi-disciplinary approach to IIP diagnosis 58 cases with suspected IIP Independent review followed by clinical- radiology-pathology consensus After CRP consensus, changes occurred in: 53% of radiologist diagnoses 34% %of clinician ca diagnoses dag 19% of pathologist diagnoses Flaherty et al. Am J Respir Crit Care Med. 2004; 170: ATS workshop: CT changes pathologic diagnosis of NSIP 104 cases had probable or definite NSIP on pathology review 38 had diagnosis changed 21 based on CT alone 14 based on CT and clinical 3 based on clinical alone Alternate diagnoses HP 21 COP 11 UIP 3 Other 4 Pathologist is not always right! Clinical-radiologic-pathologic review is very important Travis WD, et al. Am J Respir Crit Care Med Jun 15;177(12): Quantification of lung fibrosis S Semiquantitative Densitometry/CT histogram Texture-based methods Semiquantitative assessment in lung fibrosis Extent t of fibrotic abnormality Extent of ground glass abnormality Extent of emphysema 5-point scale: 0, 1-25%, 26-50% point scale: 0, 1-10%, 11-20% point scale: 0, 5%, 10%...

6 Relationship between semiquantitative assessment and mortality: Multivariate SUNDAY Baseline Variable Hazard ratio 95% Confidence Interval p Value HRCT features Overall extent of fibrosis , 4.55 < ? % predicted DLCO , Treatment , assignment to IFN- 1b Lynch DA, et al. Am J Respir Crit Care Med2005;172: CT-based quantification of lung fibrosis Histogram-based quantification Densitometry/CT t /CThistogram Local histogram methods Texture-based methods Histogram-based quantification Mean lung attenuation Skewness Degree of leftward or rightward deviation of histogram, compared with Gaussian Kurtosis Degree of peakedness of histogram, compared with Gaussian Multivariate analysis of predictors of mortality in IPF (n=167, 35 deaths) Effect Odds Ratio Estimates 95% Confidence Limits Wald Chi- Pr > Square ChiSq Kurtosis at Baseline to Mean Fibrosis to at Baseline

7 Texture-based quantification Advantages of image-based quantification in IPF Provides morphologic characterization Reticular Honeycomb Ground glass Includes functional information Lung volumes Provides regional and lobar information Sensitive, and increasingly sophisticated, quantitative tools are available Emerging information on reproducibility, longitudinal change and normal values Textural analysis in IPF: limitations?s Sources of variation Level of inspiration Scanner make/model/reconstruction algorithm CT dose Analysis technique Radiation dose More validation is needed in longitudinal datasets Key points Honeycombing remains important in making a Honeycombing remains important in making a confident diagnosis of UIP, despite observer variation Radiologist is pivotal in recognizing complications of UIP (acute exacerbation and lung cancer) Increasing recognition of atypical UIP/IPF may require revision of diagnostic guidelines Quantification will provide a powerful tool for assessing disease response