Can Augmented Reality Be Helpful in Pelvic Bone Cancer Surgery? An In Vitro Study, by Cho et al. CORR (2018), published ahead of print.
Background Application of surgical navigation for pelvic bone cancer surgery may prove useful, but in addition to the fact that research supporting its adoption remains relatively preliminary, the actual navigation devices are physically large, occupying considerable space in already crowded operating rooms. To address this issue, we developed and tested a navigation system for pelvic bone cancer surgery assimilating augmented reality (AR) technology to simplify the system by embedding the navigation software into a tablet personal computer (PC).
Questions/purposes Using simulated tumors and resections in a pig pelvic model, we asked: Can AR-assisted resection reduce errors in terms of planned bone cuts and improve ability to achieve the planned margin around a tumor in pelvic bone cancer surgery?
Methods We developed an AR-based navigation system for pelvic bone tumor surgery, which could be operated on a tablet PC. We created 36 bone tumor models for simulation of tumor resection in pig pelves and assigned 18 each to the AR-assisted resection group and conventional resection group. To simulate a bone tumor, bone cement was inserted into the acetabular dome of the pig pelvis. Tumor resection was simulated in two scenarios. The first was AR-assisted resection by an orthopaedic resident and the second was resection using conventional methods by an orthopaedic oncologist. For both groups, resection was planned with a 1-cm safety margin around the bone cement. Resection margins were evaluated by an independent orthopaedic surgeon who was blinded as to the type of resection. All specimens were sectioned twice: first through a plane parallel to the medial wall of the acetabulum and second through a plane perpendicular to the first. The distance from the resection margin to the bone cement was measured at four different locations for each plane. The largest of the four errors on a plane was adopted for evaluation. Therefore, each specimen had two values of error, which were collected from two perpendicular planes. The resection errors were classified into four grades: ≤ 3 mm; 3 to 6 mm; 6 to 9 mm; and > 9 mm or any tumor violation. Student’s t-test was used for statistical comparison of the mean resection errors of the two groups.
Results The mean of 36 resection errors of 18 pelves in the AR-assisted resection group was 1.59 mm (SD, 4.13 mm; 95% confidence interval [CI], 0.24-2.94 mm) and the mean error of the conventional resection group was 4.55 mm (SD, 9.7 mm; 95% CI, 1.38-7.72 mm; p < 0.001). All specimens in the AR-assisted resection group had errors < 6 mm, whereas 78% (28 of 36) of errors in the conventional group were < 6 mm. Conclusions In this in vitro simulated tumor model, we demonstrated that AR assistance could help to achieve the planned margin. Our model was designed as a proof of concept; although our findings do not justify a clinical trial in humans, they do support continued investigation of this system in a live animal model, which will be our next experiment. Clinical Relevance The AR-based navigation system provides additional information of the tumor extent and may help surgeons during pelvic bone cancer surgery without the need for more complex and cumbersome conventional navigation systems.