Isolated cystic lung disease: An algorithmic approach to distinguishing birthogg-dubé syndrome, lymphangioleiomyomatosis, and lymphocytic interstitial pneumonia Academic Article Article uri icon

Overview

MeSH Major

  • Electric Stimulation Therapy
  • Obesity, Morbid

abstract

  • © American Roentgen Ray Society. OBJECTIVE. Birt-Hogg-Dubé (BHD) syndrome, lymphangioleiomyomatosis (LAM), and lymphocytic interstitial pneumonia (LIP) frequently present as isolated cystic lung disease and can be challenging to distinguish. If imaging findings are otherwise unremarkable, the radiologist is unaided by ancillary CT findings in narrowing the diagnosis. We hypothesized that the distribution and morphologic features of lung cysts could be used to differentiate BHD syndrome, LAM, and LIP. Therefore, the purpose of this study was to characterize the CT appearances of these conditions and create a practical CT-based algorithm to differentiate among them. MATERIALS AND METHODS. The study was a retrospective review of the CT images of 16 patients with BHD syndrome, 17 patients with LAM, and 14 patients with LIP. On the basis of the data collected, a CT-based algorithm was created, and the CT images were reviewed again. RESULTS. Lower lung-predominant cysts were significantly more likely to be found in patients with BHD syndrome (100% of patients) or LIP (71-93% of patients) than in patients with LAM (6-12% of patients), who were more likely to have diffuse cysts. Compared with patients with LIP or LAM, patients with BHD syndrome were significantly more likely to have elliptical (floppy) paramediastinal cysts (88-94% of patients with BHD syndrome, 36- 43% of patients with LIP, and 6-12% of patients with LAM) or a disproportionate number of paramediastinal cysts (69-88% of patients with BHD syndrome, 0-14% of patients with LIP, and 0-6% of patients with LAM). Our algorithm enabled differentiation of BHD syndrome, LAM, and LIP with a high level of accuracy and high interreader agreement (κ = 0.809). CONCLUSION. Radiologists can use the proposed CT-based algorithm to prospectively and confidently suggest one of these disorders as the favored diagnosis. Of importance, this will allow diagnosing the disorder early and accurately, screening for comorbidities, and prevention of potential complications.

publication date

  • June 2019

Research

keywords

  • Academic Article

Identity

Digital Object Identifier (DOI)

  • 10.2214/AJR.18.20920

PubMed ID

  • 30888864

Additional Document Info

start page

  • 1260

end page

  • 1264

volume

  • 212

number

  • 6