Body surface area–adjusted median nerve cross-sectional area and multimodal ultrasound improve diagnosis of carpal tunnel syndrome

Background To evaluate the diagnostic performance of high-frequency ultrasound combined with Superb Microvascular Imaging (SMI) and Shear Wave Elastography (SWE) for carpal tunnel syndrome (CTS), and to develop an individualized diagnostic approach using a body surface area (BSA)–adjusted median nerve CSA at the pisiform level. Materials and methods This retrospective study included 47 wrists with carpal tunnel syndrome (CTS) and 94 control wrists. Median nerve cross-sectional area (CSA) was measured at four anatomical sites. Superb Microvascular Imaging (SMI) and Shear Wave Elastography (SWE) were used to assess intraneural vascularity and stiffness, respectively. A linear regression model was developed to estimate the expected CSA at the pisiform level based on body surface area (BSA), and a BSA-based Z-score was calculated accordingly. Receiver operating characteristic (ROC) analyses were performed to compare the diagnostic performance of (i) a fixed CSA cutoff at the pisiform level, (ii) the BSA-based Z-score, and (iii) a combined SMI + SWE logistic regression model. Results Ultrasound parameters differed significantly between the CTS and control groups ( P < 0.05). The BSA-based Z-score derived from the CSA at the pisiform level yielded an AUC of 0.924 (95% CI 0.879–0.969) and improved specificity (83%; 95% CI 0.738–0.899) compared with the fixed CSA cutoff (75%; 95% CI 0.644–0.829). In multivariable analysis, SMI- and SWE-derived parameters remained independent predictors of CTS ( P < 0.001). The combined SMI + SWE logistic regression model demonstrated the best diagnostic performance (AUC 0.944; 95% CI 0.906–0.982), with 83% sensitivity (95% CI 0.692–0.924) and 90% specificity (95% CI 0.826–0.955). Conclusion High-frequency ultrasound combined with Superb Microvascular Imaging (SMI) and Shear Wave Elastography (SWE) enables accurate, noninvasive evaluation of CTS. A BSA-based CSA Z-score improves specificity in CSA-based diagnosis, and integrating SMI and SWE further enhances overall diagnostic performance.