Lymph node-to-vein anastomosis (LNVA) is an emerging physiologic treatment for fluid-predominant lymphedema that combines the efficacy of lymphatic bypass with reduced technical complexity. Despite its advantages, LNVA is limited by challenges in identifying suitable lymph nodes and recipient veins. This study evaluated whether three-dimensional stereolithography (SLA) could improve surgical planning, intraoperative navigation, and efficiency in robotic LNVA. A retrospective comparative study was conducted of 29 patients who underwent robotic inguinal LNVA between November 2024 and September 2025. Thirteen procedures were performed using standard robotic LNVA (control group), and sixteen were performed with the addition of SLA-assisted planning and navigation (study group). Patient-specific SLA models were created from contrast-enhanced CT data, segmented into lymph nodes, veins, arteries, and bony landmarks, and printed at 1: 1 scale for incision planning and real-time intraoperative reference. Outcome measures included operative time, time to identification of target structures (TITS), surgeon-perceived operative difficulty (SPOD), and early patient-reported outcomes. Mean operative time was similar between groups (171 vs. 161 min), but TITS was significantly shorter with SLA (36 vs. 27 min; p = 0. 021). Double LNVA was achieved in 69% of SLA cases compared with 8% of controls, without prolonging operative duration. SPOD was significantly lower in the SLA group (p < 0. 001). All anastomoses were patent intraoperatively, and all patients reported symptom relief at one month. Model fabrication required approximately eight hours and averaged 270 per case. Stereolithography enhances robotic LNVA by providing a tangible three-dimensional roadmap that improves intraoperative orientation, reduces identification time, and enables multiple anastomoses without added operative burden. With modest cost and rapid production, SLA makes LNVA more precise, reproducible, and scalable—facilitating wider adoption and serving as a foundation for future outcome-based research.
Chen et al. (Sun,) studied this question.