Noncontrast Chest Computed Tomographic Imaging of Obesity and the Metabolic Syndrome: Part II Noncardiovascular Findings

Novel measures for the diagnosis of hepatic steatosis using contrast-enhanced computer tomography images

PURPOSE

Hepatic steatosis is often diagnosed non-invasively. Various measures and accompanying diagnostic thresholds based on contrast-enhanced CT and virtual non-contrast images have been proposed. We compare these established criteria to novel and fully automated measures.

METHOD

CT data sets of 197 patients were analyzed. Regions of interest (ROIs) were manually drawn for the liver, spleen, portal vein, and aorta to calculate four established measures of liver-fat. Two novel measures capturing the deviation between the empirical distributions of HU measurements across all voxels within the liver and spleen were calculated. These measures were calculated with both manual ROIs and using fully automated organ segmentations. Agreement between the different measures was evaluated using correlational analysis, as well as their ability to discriminate between fatty and healthy liver.

RESULTS

Established and novel measures of fatty liver were at a high level of agreement. Novel methods were statistically indistinguishable from the established ones when taking established diagnostic thresholds or physicians' diagnoses as ground truth and this high performance level persisted for automatically selected ROIs.

CONCLUSION

Automatically generated organ segmentations led to comparable results as manual ROIs, suggesting that the implementation of automated methods can prove to be a valuable tool for incidental diagnosis. Differences in the distribution of HU measurements across voxels between liver and spleen can serve as surrogate markers for the liver-fat-content. Novel measures do not exhibit a measurable disadvantage over established methods based on simpler measures such as across-voxel averages in a population with low incidence of fatty liver.

Skeletal Muscle Adiposity and Lung Function Trajectory in the Severe Asthma Research Program

Rationale: Extrapulmonary manifestations of asthma, including fatty infiltration in tissues, may reflect systemic inflammation and influence lung function and disease severity. Objectives: To determine if skeletal muscle adiposity predicts lung function trajectory in asthma. Methods: Adult SARP III (Severe Asthma Research Program III) participants with baseline computed tomography imaging and longitudinal postbronchodilator FEV1% predicted (median follow-up 5 years [1,132 person-years]) were evaluated. The mean of left and right paraspinous muscle density (PSMD) at the 12th thoracic vertebral body was calculated (Hounsfield units [HU]). Lower PSMD reflects higher muscle adiposity. We derived PSMD reference ranges from healthy control subjects without asthma. A linear multivariable mixed-effects model was constructed to evaluate associations of baseline PSMD and lung function trajectory stratified by sex. Measurements and Main Results: Participants included 219 with asthma (67% women; mean [SD] body mass index, 32.3 [8.8] kg/m2) and 37 control subjects (51% women; mean [SD] body mass index, 26.3 [4.7] kg/m2). Participants with asthma had lower adjusted PSMD than control subjects (42.2 vs. 55.8 HU; P < 0.001). In adjusted models, PSMD predicted lung function trajectory in women with asthma (β = -0.47 Δ slope per 10-HU decrease; P = 0.03) but not men (β = 0.11 Δ slope per 10-HU decrease; P = 0.77). The highest PSMD tertile predicted a 2.9% improvement whereas the lowest tertile predicted a 1.8% decline in FEV1% predicted among women with asthma over 5 years. Conclusions: Participants with asthma have lower PSMD, reflecting greater muscle fat infiltration. Baseline PSMD predicted lung function decline among women with asthma but not men. These data support an important role of metabolic dysfunction in lung function decline.

Usefulness of Body Composition CT Analysis in Patients with Idiopathic Pulmonary Fibrosis: A Pilot Study

PURPOSE

To evaluate the feasibility of a chest CT-based body composition analysis in idiopathic pulmonary fibrosis (IPF), and to investigate the respective contribution of lung and muscle CT quantitative analyses to the prognosis of IPF.

METHOD

A total of 71 IPF patients were recruited at diagnosis. All patients underwent a standard chest CT-scan and a bioelectrical impedance analysis considered as reference standard for estimating malnutrition through the use of the fat-free mass index (FFMI). The skeletal muscle index (SMI) was measured on chest-CT at the level of the first lumbar vertebra by two radiologists. Lung fibrosis extent was quantified by three radiologists in consensus. The extent of emphysema, the pulmonary artery to aorta (PA/AO) diameter ratio and lymph node enlargement were also reported. Mortality and hospitalization over a 14-month follow-up were recorded.

RESULTS

A low FFMI defining malnutrition was identified in 26.8% of patients. SMI was significantly lower in these patients (p<0.001) and was correlated with FFMI (r=0.637, p<0.001). Interobserver agreement of SMI measurement was very good (ICC=0.91). For diagnosing malnutrition, SMI showed a 0.79 sensitivity, a 0.69 specificity, a 0.48 PPV and a 0.90 NPV. In univariate analysis, fibrosis extent was significantly associated with death, while SMI did not reach significance. In multivariate analysis, fibrosis extent and PA/AO ratio were independently associated with hospitalization.

CONCLUSIONS

SMI measured on chest CT could be a reliable tool to exclude malnutrition in IPF. A quantitative analysis of both fibrosis and skeletal muscle may allow holistic management of IPF patients.

Imaging Biomarkers of Hypothyroidism on Lung Cancer Screening CT

PURPOSE

To assess the feasibility of attenuation and size measurement of the thyroid gland as an imaging biomarker for hypothyroidism in patients undergoing lung cancer screening (LCS) with low dose CT.

MATERIALS AND METHODS

With institutional review board (IRB) approval, we retrospectively reviewed all patients with LCS CT between September 1, 2016 and March 31, 2020, who had at least 1 thyroid-stimulating hormone (TSH) test within 90 days of the patient's most recent screening CT. Hypothyroid patients were identified through billing diagnosis and/or elevated TSH or those on treatment with thyroxine; normal patients were identified as those without a diagnosis of hypothyroidism and normal TSH. For each hypothyroid patient, an age- and sex-matched normal control was included. The diameters and attenuation of both lobes of the thyroid gland were measured for each case; patients in whom the thyroid gland could not be seen to measure were excluded.

RESULTS

A total of 304 patients were included. The areas under the receiver operating characteristic curve for size and attenuation of the left lobe were 0.774 (95% confidence interval [CI] 0.714-0.825) and 0.812 (95% CI 0.759-0.861), respectively; and for the right lobe were 0.776 (95% CI 0.719-0.827) and 0.794 (95% CI 0.740-0.847), respectively. We developed a decision tree algorithm to predict hypothyroidism combining the minimum size and attenuation of either lobe of the thyroid gland, with sensitivity, specificity, and accuracy of 76%, 87%, and 82%, respectively.

CONCLUSION

Size and attenuation of the thyroid gland can be used to identify potential hypothyroid patients undergoing LCS.