Objective: To quantify how surgeons translate 2-dimensional (2D) computed tomography (CT) or magnetic resonance imaging (MRI) data to a 3-dimensional (3D) model and evaluate if 3D printed models improve tumor localization. Materials and Methods: Twenty patients with renal masses were randomly selected from our institutional review board approved prospective 3D modeling study. Three surgeons reviewed the clinically available CT or MRI data; and using computer-aided design software, translated the renal tumor to the position on the kidney that corresponded with the image interpretation. The renal tumor location determined by each surgeon was compared to the true renal mass location determined by the segmented imaging data and the Dice Similarity Coefficient (DSC) was calculated to evaluate the spatial overlap accuracy. The exercise was repeated for a subset of patients with a 3D printed model. Results: The mean DSC was 0.243 ± 0.236 for the entire cohort (n = 60). There was no overlap between the actual renal tumor and renal tumor identified by the surgeons in 16 of 60 cases (26.67%). Seven cases were reviewed again by 2 surgeons in a different setting with a 3D printed renal cancer model. For these cases, the DSC improved from 0.277 ± 0.248 using imaging only to 0.796 ± 0.090 with the 3D printed model (P < .01). Conclusion: In this study, cognitive renal tumor localization based on CT and MRI data was poor. This study demonstrates that experienced surgeons cannot always translate 2D imaging studies into 3D. Furthermore, 3D printed models can improve tumor localization and potentially assist with appropriate surgical approach.
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