Quantitative analysis of contrast-enhanced ultrasonography of the bowel wall can predict disease activity in inflammatory bowel disease

PurposeTo evaluate the accuracy of quantitative analysis of bowel wall enhancement in inflammatory bowel disease (IBD) with contrast enhanced ultrasound (CEUS) by comparing the results with vascular density in a biopsy sample from the same area of the intestinal tract, and to determine the usefulness of this analysis for the prediction of disease activity.Materials and methodsThis prospective study was approved by our institute's ethics committee and all patients gave written informed consent. We enrolled 33 consecutive adult patients undergoing colonoscopy and biopsy for IBD. All patients underwent CEUS and the results were quantitatively analyzed. Vessel count per high-power field on biopsy specimens was compared with colonoscopy, baseline ultrasonography, and CEUS findings, and with analysis of peak intensity, time to peak, regional blood volume, mean transit time, and regional blood flow. Results in patients with high and low vascular density were compared using Fisher's test, t-test, Pearson's correlation test, and receiver operating characteristic curve (ROC) analysis. Cutoff values were determined using ROC analysis, and sensitivity and specificity were calculated.ResultsHigh vascular density (>265 vessels per field) on histological examination was significantly correlated with active disease on colonoscopy, baseline ultrasonography, and CEUS (p < .0001). Quantitative analysis showed a higher enhancement peak, a shorter time to peak enhancement, a higher regional blood flow and regional blood volume in patients with high vascular density than in those with low vascular density. Cutoff values to distinguish between active and inactive disease were identified for peak enhancement (>40.5%), and regional blood flow (>54.8 ml/min).ConclusionQuantitative analysis of CEUS data correlates with disease activity as determined by vascular density. Quantitative parameters of CEUS can be used to predict active disease with high sensitivity and specificity.