Background: ARFI elastrography has been used as a noninvasive method to assess the severity of liver fibrosis in viral hepatitis, although with few studies in schistosomiasis mansoni. We aimed to evaluate the performance of point shear wave elastography (pSWE) for predicting significant periportal fibrosis (PPF) in schistosomotic patients and to determine its best cutoff point.
Methodology/Principal findings:This cross-sectional study included 358 adult schistosomotic patients subjected to US and pSWE on the right lobe. Two hundred two patients (62.0%) were women, with a median age of 54 (ranging 18-92) years.The pSWE measurements were compared to the US patterns of PPF, as gold standard, according to the Niamey classification. The performance of pSWE was calculated as the area under the ROC curve (AUC). Patients were further classified into two groups: 86 patients with mild PPF and 272 patients with significant PPF. The median pSWE of the significant fibrosis group was higher (1.40 m/s) than that of mild fibrosis group (1.14 m/s, p<0.001). AUC was 0.719 with ≤1.11 m/s as the best cutoff value for excluding significant PPF. Sensitivity and negative predictive values were 80.5% and 40.5%, respectively. Whereas, for confirming significant PPF, the best cutoff value was >1.39 m/s, with specificity of 86.1% and positive predictive value of 92.0%.Conclusions/Significance: pSWE was able to differentiate significant from mild PPF, with better performance to predict significant PPF.
Author summaryIn the developing world, over 207 million people are infected with parasitic Schistosoma worms. Among the species of Schistosoma that infect humans Schistosoma mansoni is one of the most common causes of illness. Here, we investigated the performance of point shear wave elastography (pSWE) for predicting significant periportal fibrosis (PPF) in schistosomotic patients and to determine its best cutoff point. We examined 358 people from northeast of Brazil for Schistosoma infections. The present study showed that pSWE was able to differentiate significant from mild PPF, with better performance to predict significant PPF.