Ganesh Raghu1, Kevin R Flaherty2, David J Lederer3, David A Lynch4, Thomas V Colby5, Jeffrey L Myers6, Steve D Groshong7, Brandon T Larsen5, Jonathan H Chung8, Mark P Steele9, Sadia Benzaquen10, Karel Calero11, Amy H Case12, Gerard J Criner13, Steven D Nathan14, Navdeep S Rai15, Murali Ramaswamy16, Lars Hagmeyer17, J Russell Davis18, Umair A Gauhar19, Daniel G Pankratz20, Yoonha Choi20, Jing Huang20, P Sean Walsh20, Hannah Neville20, Lori R Lofaro20, Neil M Barth20, Giulia C Kennedy20, Kevin K Brown6, Fernando J Martinez21. 1. Center for Interstitial Lung Diseases, Department of Medicine and Laboratory Medicine, University of Washington Medical Center, University of Washington, Seattle, WA, USA. Electronic address: graghu@uw.edu. 2. Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA. 3. Department of Medicine and Epidemiology, Columbia University, New York, NY, USA. 4. Department of Radiology, National Jewish Health, Denver, CO, USA. 5. Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, AZ, USA. 6. Department of Pathology, University of Michigan School of Medicine, Ann Arbor, MI, USA. 7. Department of Medicine, National Jewish Health, Denver, CO, USA. 8. Department of Radiology, University of Chicago Medicine, Chicago, IL, USA. 9. Division of Pulmonary Sciences, University of Colorado School of Medicine, Anschutz Medical Center, Denver, CO, USA. 10. Division of Pulmonary and Critical Care Medicine, University of Cincinnati, Cincinnati, OH, USA. 11. Department of Internal Medicine, University of South Florida, Tampa, FL, USA. 12. Division of Pulmonary, Critical Care, and Sleep Medicine, Piedmont Healthcare, Atlanta, GA, USA. 13. Department of Thoracic Medicine and Surgery, Temple University, Philadelphia, PA, USA. 14. Department of Medicine, Inova Fairfax Hospital, Falls Church, VA, USA. 15. Pulmonary, Critical Care and Sleep Medicine, Pulmonary Consultants, PLLC, Tacoma, WA, USA. 16. Pulmonary and Critical Care Medicine, Pulmonlx, Greensboro, NC, USA; Cone Health, Greensboro, NC, USA. 17. Clinic of Pneumology and Allergology, Centre of Sleep Medicine and Respiratory Care, Hospital Bethanien Hospital Solingen, Germany. 18. Department of Cardiothoracic Surgery, Saint Luke's Hospital, Kansas City, MO, USA. 19. Division of Pulmonary, Critical Care, and Sleep Disorders Medicine, University of Louisville, Louisville, KY, USA. 20. Veracyte, San Francisco, CA, USA. 21. Department of Medicine, Weill Cornell Medical Center, New York, NY, USA.
Abstract
BACKGROUND: In the appropriate clinical setting, the diagnosis of idiopathic pulmonary fibrosis (IPF) requires a pattern of usual interstitial pneumonia to be present on high-resolution chest CT (HRCT) or surgical lung biopsy. A molecular usual interstitial pneumonia signature can be identified by a machine learning algorithm in less-invasive transbronchial lung biopsy samples. We report prospective findings for the clinical validity and utility of this molecular test. METHODS: We prospectively recruited 237 patients for this study from those enrolled in the Bronchial Sample Collection for a Novel Genomic Test (BRAVE) study in 29 US and European sites. Patients were undergoing evaluation for interstitial lung disease and had had samples obtained by clinically indicated surgical or transbronchial biopsy or cryobiopsy for pathology. Histopathological diagnoses were made by experienced pathologists. Available HRCT scans were reviewed centrally. Three to five transbronchial lung biopsy samples were collected from all patients specifically for this study, pooled by patient, and extracted for transcriptomic sequencing. After exclusions, diagnostic histopathology and RNA sequence data from 90 patients were used to train a machine learning algorithm (Envisia Genomic Classifier, Veracyte, San Francisco, CA, USA) to identify a usual interstitial pneumonia pattern. The primary study endpoint was validation of the classifier in 49 patients by comparison with diagnostic histopathology. To assess clinical utility, we compared the agreement and confidence level of diagnosis made by central multidisciplinary teams based on anonymised clinical information and radiology results plus either molecular classifier or histopathology results. FINDINGS: The classifier identified usual interstitial pneumonia in transbronchial lung biopsy samples from 49 patients with 88% specificity (95% CI 70-98) and 70% sensitivity (47-87). Among 42 of these patients who had possible or inconsistent usual interstitial pneumonia on HRCT, the classifier showed 81% positive predictive value (95% CI 54-96) for underlying biopsy-proven usual interstitial pneumonia. In the clinical utility analysis, we found 86% agreement (95% CI 78-92) between clinical diagnoses using classifier results and those using histopathology data. Diagnostic confidence was improved by the molecular classifier results compared with histopathology results in 18 with IPF diagnoses (proportion of diagnoses that were confident or provisional with high confidence 89% vs 56%, p=0·0339) and in all 48 patients with non-diagnostic pathology or non-classifiable fibrosis histopathology (63% vs 42%, p=0·0412). INTERPRETATION: The molecular test provided an objective method to aid clinicians and multidisciplinary teams in ascertaining a diagnosis of IPF, particularly for patients without a clear radiological diagnosis, in samples that can be obtained by a less invasive method. Further prospective clinical validation and utility studies are planned. FUNDING: Veracyte.
BACKGROUND: In the appropriate clinical setting, the diagnosis of idiopathic pulmonary fibrosis (IPF) requires a pattern of usual interstitial pneumonia to be present on high-resolution chest CT (HRCT) or surgical lung biopsy. A molecular usual interstitial pneumonia signature can be identified by a machine learning algorithm in less-invasive transbronchial lung biopsy samples. We report prospective findings for the clinical validity and utility of this molecular test. METHODS: We prospectively recruited 237 patients for this study from those enrolled in the Bronchial Sample Collection for a Novel Genomic Test (BRAVE) study in 29 US and European sites. Patients were undergoing evaluation for interstitial lung disease and had had samples obtained by clinically indicated surgical or transbronchial biopsy or cryobiopsy for pathology. Histopathological diagnoses were made by experienced pathologists. Available HRCT scans were reviewed centrally. Three to five transbronchial lung biopsy samples were collected from all patients specifically for this study, pooled by patient, and extracted for transcriptomic sequencing. After exclusions, diagnostic histopathology and RNA sequence data from 90 patients were used to train a machine learning algorithm (Envisia Genomic Classifier, Veracyte, San Francisco, CA, USA) to identify a usual interstitial pneumonia pattern. The primary study endpoint was validation of the classifier in 49 patients by comparison with diagnostic histopathology. To assess clinical utility, we compared the agreement and confidence level of diagnosis made by central multidisciplinary teams based on anonymised clinical information and radiology results plus either molecular classifier or histopathology results. FINDINGS: The classifier identified usual interstitial pneumonia in transbronchial lung biopsy samples from 49 patients with 88% specificity (95% CI 70-98) and 70% sensitivity (47-87). Among 42 of these patients who had possible or inconsistent usual interstitial pneumonia on HRCT, the classifier showed 81% positive predictive value (95% CI 54-96) for underlying biopsy-proven usual interstitial pneumonia. In the clinical utility analysis, we found 86% agreement (95% CI 78-92) between clinical diagnoses using classifier results and those using histopathology data. Diagnostic confidence was improved by the molecular classifier results compared with histopathology results in 18 with IPF diagnoses (proportion of diagnoses that were confident or provisional with high confidence 89% vs 56%, p=0·0339) and in all 48 patients with non-diagnostic pathology or non-classifiable fibrosis histopathology (63% vs 42%, p=0·0412). INTERPRETATION: The molecular test provided an objective method to aid clinicians and multidisciplinary teams in ascertaining a diagnosis of IPF, particularly for patients without a clear radiological diagnosis, in samples that can be obtained by a less invasive method. Further prospective clinical validation and utility studies are planned. FUNDING: Veracyte.
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