TITLE:
A Validated Model for the Imaging Diagnosis of Cystic Lung Disease
AUTHORS:
Wallace T. Miller, Karen C. Patterson, Shweta Sood, James E. Schmitt, Arshad A. Wani, Robert Borden, Maya Galperin-Aisenberg, Mary K. Porteus, Michelle L. Hershman, Michael Hewitt, Jennifer Levy, Victor D. Babatunde, Tetiana Glushko, Timothy J. Niesen, Sergey Leshchinskiy, Karine Sahakyan, Keyur Desai, Jennifer A. Gillman, Sandeep Reddy, Michael Shriver, Nathaniel B. Linna, Abass M. Noor, Aysenur Buz, Matthew E. Biron, Scott Simpson
KEYWORDS:
Lymphangioleiomyomatosis, Histiocytosis, Langerhans-Cell, Idiopathic Interstitial Pneumonias, Birt-Hogg-Dube Syndrome, Lung Diseases, Interstitial, Diagnoses, Differential
JOURNAL NAME:
Open Journal of Radiology,
Vol.13 No.1,
March
7,
2023
ABSTRACT: Rationale and Objectives: Cystic lung disease may be accurately diagnosed by imaging interpretation of specialist radiologists, without other information. We hypothesized that with minimal training non-specialists could perform similarly to specialist physicians in the diagnosis of cystic lung disease. Methods: 72 cystic lung disease cases and 25 cystic lung disease mimics were obtained from three sources: 1) a prospective acquired diffuse lung disease registry, 2) a retrospective search of medical records and 3) teaching files. Cases were anonymized, randomized and interpreted by 7 diffuse lung disease specialists and 15 non-specialist radiologists and pulmonologists. Clinical information other than age and sex was not provided. Prior to interpretation, non-specialists viewed a short PDF training document explaining cystic lung disease interpretation. Results: Correct first choice diagnosis of 85%-88% may be achieved by high-performing specialist readers and 71%-80% by non-specialists and lower-performing specialists, with mean accuracies in the diagnosis of LAM (91%, p Conclusion: With specific but limited training, non-specialist physicians can diagnose cystic lung diseases from CT appearance alone with similar accuracy to specialists, correctly identifying approximately 75% of cases.