NEW YORK (Reuters Health), Sep 3 - Esophageal dilatation visible on a high-resolution CT scan of the lungs may be a sign of scleroderma, according to findings published in the September issue of the Annals of the Rheumatic Diseases.
There is evidence that immunosuppressive treatment in the early stages of systemic sclerosis (SSc) may improve survival, "enhancing the need for early diagnosis and regular evaluation of organ involvement," wrote Dr. M. C. Vonk, of Radboud University Nijmegen Medical Center, the Netherlands, and colleagues.
The researchers examined the predictive value of esophageal dilatation on the high-resolution CT (HRCT) scan for the diagnosis of SSc. Included in the study were 105 consecutive patients with scleroderma and 107 consecutive control subjects. Two independent radiologists, who were blinded for the diagnosis of the patients, evaluated the scans for esophageal dilatation and interstitial lung disease.
Infra-aortic esophageal dilatation was observed in 62% of SSc patients and 12% of controls. The positive predictive value of esophageal dilatation for the diagnosis of SSc was 83% and the negative predictive value was 69%.
There was no difference observed in the prevalence of esophageal dilatation in patients with early disease and those with a longer duration of disease. No correlation was found between esophageal dilatation and interstitial lung disease in cases and controls.
"The presence of an esophageal dilatation on an HRCT scan does not completely predict the presence of SSc, and its absence does not rule out SSc," Vonk's team writes. "However, it could be worthwhile if a radiologist, on noticing an esophageal dilatation on an HRCT scan of the chest, reports this finding and perhaps even suggests the possibility of a diagnosis of SSc, enabling the referring specialist to start further diagnostic workup for SSc."
Ann Rheum Dis 2008;67:1317-1321.
Last Updated: 2008-09-02 17:35:44 -0400 (Reuters Health)
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![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
