Abstract / Summary
Background: This study reports the development and initial face and content validation of high-fidelity 3D hybrid simulators for training in complex pediatric oncologic surgery. Pediatric solid tumor surgery is technically demanding; case volumes are low, and procedural errors carry lifelong oncologic or functional consequences. High-fidelity simulation offers a route to structured training without patient risk. Methods: Patient-derived hybrid simulators were developed by combining 3D-printed anatomical molds with multi-hardness platinum silicone casting. Three oncologic scenarios were reproduced: nephron-sparing surgery (NSS) for Wilms tumor, neuroblastoma L1, and neuroblastoma L2 with major vascular involvement. Retrospective imaging datasets were segmented using Philips IntelliSpace Portal to generate patient-derived STL files. Bony components (simulator box, thoracic wall) were fabricated by selective laser sintering (SLS) in PA12 polyamide. Silicone casting molds were produced by fused filament fabrication (FFF) in basic PLA. Silicone selection (Smooth-On Ecoflex and Dragon Skin series) was based on our team’s expertise in the mechanical characteristics of human organs, matching manufacturer-provided Shore hardness datasheet values to the closest available reference for each tissue type. All components were assembled in anatomically accurate sequence within a patient-sized housing box. Validation was conducted through structured workshops with fifteen pediatric surgical oncologists using a 5-point Likert scale. Results: Mean Likert scores, calculated from participant-level composite domain scores (n = 27 model evaluations from 15 participants), were high across all domains: anatomical accuracy 4.14 ± 0.53 (median 4.00, IQR 3.67–4.67), dissection realism 4.29 ± 0.45 (median 4.33, IQR 4.00–4.50), and tactile fidelity 4.37 ± 0.56 (median 4.00, IQR 4.00–5.00). The L2 neuroblastoma model was rated particularly valuable for rehearsing synchronized two-surgeon workflows and anticipatory communication during high-risk vascular dissection. Conclusions: These hybrid simulators provide a reproducible, high-fidelity platform with strong expert-perceived realism, supporting rehearsal of technical maneuvers and coordinated two-surgeon workflows; formal evaluation of skill acquisition and clinical transfer remains warranted.