Diagnostic Accuracy of Ultrasound for Localizing Lymphovenous Anastomosis Sites in Lymphedema Surgery
Year: 2026
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Preoperative vessel mapping is critical for successful lymphovenous anastomosis (LVA) in supermicrosurgical lymphedema treatment. Indocyanine green lymphography is common but limited by poor depth penetration and reduced visibility in advanced disease. This study aimed to evaluate the diagnostic accuracy, measurement agreement with intraoperative findings, and procedural efficiency of preoperative ultrasound in identifying surgically compatible LVA sites. METHODS: This study retrospectively analyzed prospectively collected data between April 2019 and January 2024 at a secondary care center. Consecutive patients with scintigraphically confirmed limb lymphedema underwent preoperative ultrasound to localize LVA sites, with intraoperative findings as the reference standard. Diagnostic accuracy was assessed using positive predictive value (PPV), Bland-Altman analysis, multivariable logistic regression, and subgroup testing. RESULTS: Eighty-seven patients (mean age, 55.8 ± 12.8 years; 75 women) underwent 109 ultrasound examinations. Of 470 ultrasound-localized sites, 285 were selected for surgery; 272 were intraoperatively confirmed (PPV, 95.4%; 95% CI: 92.3, 97.5%). The 13 false positives were independent of tissue thickness, vessel size, depth, and distance (all p > 0.17). Ultrasound diameters showed minimal bias compared with intraoperative measurements [+0.13 mm (95% CI: 0.09, 0.17) for lymphatics; -0.10 mm (95% CI: -0.17, -0.04) for veins]. Mean total examination time was 34.7 ± 9.6 minutes; per-image analysis averaged 7.2 ± 2.3 minutes and was unrelated to diagnostic accuracy (p = 0.74). CONCLUSIONS: Preoperative ultrasound enabled accurate, reproducible localization of LVA sites with strong anatomical agreement and consistent performance across participant and anatomical variables.
Source: Plastic & Reconstructive Surgery
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7222097754
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