Search

BACKGROUND. Adrenal masses are often indeterminate on single-phase postcontrast CT. Dual-energy CT (DECT) with three-material decomposition algorithms may aid characterization. OBJECTIVE. The purpose of this study was to compare the diagnostic performance of metrics derived from portal venous phase DECT, including virtual noncontrast (VNC) attenuation, fat fraction, iodine density, and relative enhancement ratio, for characterizing adrenal masses. METHODS. This retrospective study included 128 patients (82 women, 46 men; mean age, 64.6 ± 12.7 [SD] years) who between January 2016 and December 2019 underwent portal venous phase abdominopelvic DECT that showed a total of 139 adrenal lesions with an available reference standard based on all imaging, clinical, and pathologic records (87 adenomas, 52 nonadenomas [48 metastases, two adrenal cortical carcinomas, one ganglioneuroma, one hematoma]). Two radiologists placed ROIs to determine the following characteristics of the masses: VNC attenuation, fat fraction, iodine density normalized to portal vein, and for masses with VNC greater than 10 HU, relative enhancement ratio (ratio of portal venous phase attenuation to VNC attenuation). Readers' mean measurements were used for ROC analyses, and clinically optimal thresholds were derived as thresholds yielding the highest sensitivity at 100% specificity. RESULTS. Adenomas and nonadenomas were significantly different (all p < .001) in VNC attenuation (mean ± SD, 18.5 ± 12.9 vs 34.1 ± 8.9 HU), fat fraction (mean ± SD, 24.3% ± 8.2% vs 14.2% ± 5.6%), normalized iodine density (mean ± SD, 0.34 ± 0.15 vs 0.17 ± 0.17), and relative enhancement ratio (mean ± SD, 186% ± 96% vs 58% ± 59%). AUCs for all metrics ranged from 0.81 through 0.91. The metric with highest sensitivity for adenoma at the clinically optimal threshold (i.e., 100% specificity) was fat fraction (threshold, ≥ 23.8%; sensitivity, 59% [95% CI, 48-69%]) followed by VNC attenuation (≤ 15.2 HU; sensitivity, 39% [95% CI, 29-50%]), relative enhancement ratio (≥ 214%; sensitivity, 37% [95% CI, 25-50%]), and normalized iodine density (≥ 0.90; sensitivity, 1% (95% CI, 0-60%]). VNC attenuation at the traditional true noncontrast attenuation threshold of 10 HU or lower had sensitivity of 28% (95% CI, 19-38%) and 100% specificity. Presence of fat fraction 23.8% or greater or relative enhancement ratio 214% or greater yielded sensitivity of 68% (95% CI, 57-77%) with 100% specificity. CONCLUSION. For adrenal lesions evaluated with single-phase DECT, fat fraction had higher sensitivity than VNC attenuation at both the clinically optimal threshold and the traditional threshold of 10 HU or lower. CLINICAL IMPACT. By helping to definitively diagnose adenomas, DECT-derived metrics can help avoid downstream imaging for incidental adrenal lesions.

BACKGROUND. CT-based body composition (BC) measurements have historically been too resource intensive to analyze for widespread use and have lacked robust comparison with traditional weight metrics for predicting cardiovascular risk. OBJECTIVE. The aim of this study was to determine whether BC measurements obtained from routine CT scans by use of a fully automated deep learning algorithm could predict subsequent cardiovascular events independently from weight, BMI, and additional cardiovascular risk factors. METHODS. This retrospective study included 9752 outpatients (5519 women and 4233 men; mean age, 53.2 years; 890 patients self-reported their race as Black and 8862 self-reported their race as White) who underwent routine abdominal CT at a single health system from January 2012 through December 2012 and who were given no major cardiovascular or oncologic diagnosis within 3 months of undergoing CT. Using publicly available code, fully automated deep learning BC analysis was performed at the L3 vertebral body level to determine three BC areas (skeletal muscle area [SMA], visceral fat area [VFA], and subcutaneous fat area [SFA]). Age-, sex-, and race-normalized reference curves were used to generate z scores for the three BC areas. Subsequent myocardial infarction (MI) or stroke was determined from the electronic medical record. Multivariable-adjusted Cox proportional hazards models were used to determine hazard ratios (HRs) for MI or stroke within 5 years after CT for the three BC area z scores, with adjustment for normalized weight, normalized BMI, and additional cardiovascular risk factors (smoking status, diabetes diagnosis, and systolic blood pressure). RESULTS. In multivariable models, age-, race-, and sex-normalized VFA was associated with subsequent MI risk (HR of highest quartile compared with lowest quartile, 1.31 [95% CI, 1.03-1.67], p = .04 for overall effect) and stroke risk (HR of highest compared with lowest quartile, 1.46 [95% CI, 1.07-2.00], p = .04 for overall effect). In multivariable models, normalized SMA, SFA, weight, and BMI were not associated with subsequent MI or stroke risk. CONCLUSION. VFA derived from fully automated and normalized analysis of abdominal CT examinations predicts subsequent MI or stroke in Black and White patients, independent of traditional weight metrics, and should be considered an adjunct to BMI in risk models. CLINICAL IMPACT. Fully automated and normalized BC analysis of abdominal CT has promise to augment traditional cardiovascular risk prediction models.