ARRS 2022 Abstracts

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E1967. Structuring Anatomy: Advanced Digital Fabrication of Renal Cell Carcinoma for Presurgical Planning of Partial Nephrectomies
Authors
  1. Nicholas Jacobson; University of Colorado Anschutz
  2. Erik Carrera; University of Colorado Anschutz
  3. Alison Sheridan; University of Colorado Anschutz
Objective:
There has been increasing utilization of 3D printing from medical images for presurgical planning. Recent studies have shown that when such models are used in preoperative planning, there can be decreased operative time and improved margin rates; however, the current method for 3D printing requires segmentation, where valuable volumetric information from source diagnostic images is lost. In addition, these segmentation workflows are time-consuming, and the resulting models only coarsely approximate the original scanned data, resulting ultimately in loss of visual fidelity. We utilize a novel method of 3D printing that derives the material composition of the 3D-printed model directly from the scanned data and has improved spatial fidelity. In conjunction with radiology and urology, we have developed a 3D-printed model of the kidney that highlights a renal mass to be used for preoperative planning. We hypothesize that our model will decrease positive margin rates and decrease operative time in partial nephrectomies and improve upon the current ~50% positive surgical margin rate and reduce operating time. We present here the first 3D-printed model of the kidney utilizing our novel 3D printing method.

Materials and Methods:
We used a novel image-based 3D printing method that directly translates CT images of renal masses to a 3D-printed model at the spatial fidelity of the source data. We first imported a renal mass protocol 3 phase CT scan and converted the corticomedullary phase imaging into a volume rendering. Window width and window level adjustments were made to highlight the vessels, kidney, and tumor.

Results:
The resulting model clearly displays the morphology of the tumor, supporting vessels, cortex, and medulla. These models will be used in a clinical trial (n = 80) at UCHealth University of Colorado preoperatively for planning and brought into the operating room as guidance during the procedure. Additionally, the model will be used during consultation with the patient to assist with communication regarding the procedure and utilization from imaging during the preparation for the surgery. Surveys will be given to both the surgeon and the patient before and after the procedure in addition to gathering procedural metrics relating to resection time and complications. Finally, we will gather information relating to margin rates from pathological examinations of the resections.

Conclusion:
We have successfully created a 3D-printed model of the kidney and an associated renal mass using our novel method of 3D printing. These models will be used prospectively in a clinical trial for partial nephrectomy pre-surgical planning to evaluate our hypothesis of reducing operating time and improving the rate of negative surgical margins (NSM). Our method seeks to improve upon the current state of the art in hopes of further reducing operating time and complications.