Dataset Information

Effect of 3-Dimensional Virtual Reality Models for Surgical Planning of Robotic-Assisted Partial Nephrectomy on Surgical Outcomes: A Randomized Clinical Trial.

ABSTRACT:

Importance

Planning complex operations such as robotic-assisted partial nephrectomy requires surgeons to review 2-dimensional computed tomography or magnetic resonance images to understand 3-dimensional (3-D), patient-specific anatomy.

Objective

To determine surgical outcomes for robotic-assisted partial nephrectomy when surgeons reviewed 3-D virtual reality (VR) models during operative planning.

Design, setting, and participants

A single-blind randomized clinical trial was performed. Ninety-two patients undergoing robotic-assisted partial nephrectomy performed by 1 of 11 surgeons at 6 large teaching hospitals were prospectively enrolled and randomized. Enrollment and data collection occurred from October 2017 through December 2018, and data analysis was performed from December 2018 through March 2019.

Interventions

Patients were assigned to either a control group undergoing usual preoperative planning with computed tomography and/or magnetic resonance imaging only or an intervention group where imaging was supplemented with a 3-D VR model. This model was viewed on the surgeon's smartphone in regular 3-D format and in VR using a VR headset.

Main outcomes and measures

The primary outcome measure was operative time. It was hypothesized that the operations performed using the 3-D VR models would have shorter operative time than those performed without the models. Secondary outcomes included clamp time, estimated blood loss, and length of hospital stay.

Results

Ninety-two patients (58 men [63%]) with a mean (SD) age of 60.9 (11.6) years were analyzed. The analysis included 48 patients randomized to the control group and 44 randomized to the intervention group. When controlling for case complexity and other covariates, patients whose surgical planning involved 3-D VR models showed differences in operative time (odds ratio [OR], 1.00; 95% CI, 0.37-2.70; estimated OR, 2.47), estimated blood loss (OR, 1.98; 95% CI, 1.04-3.78; estimated OR, 4.56), clamp time (OR, 1.60; 95% CI, 0.79-3.23; estimated OR, 11.22), and length of hospital stay (OR, 2.86; 95% CI, 1.59-5.14; estimated OR, 5.43). Estimated ORs were calculated using the parameter estimates from the generalized estimating equation model. Referent group values for each covariate and the corresponding nephrometry score were summed across the covariates and nephrometry score, and the sum was exponentiated to obtain the OR. A mean of the estimated OR weighted by sample size for each nephrometry score strata was then calculated.

Conclusions and relevance

This large, randomized clinical trial demonstrated that patients whose surgical planning involved 3-D VR models had reduced operative time, estimated blood loss, clamp time, and length of hospital stay.

Trial registration

ClinicalTrials.gov identifiers (1 registration per site): NCT03334344, NCT03421418, NCT03534206, NCT03542565, NCT03556943, and NCT03666104.

SUBMITTER: Shirk JD

PROVIDER: S-EPMC6751754 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Objective: To report a case of robot-assisted partial nephrectomy (RAPN) for two highly complex renal tumors in a patient with a Horseshoe kidney (HSK), focusing on the utility of hyperaccuracy three-dimensional (HA3D) virtual models for accurate preoperative and intraoperative planning of the procedure. Methods: A 74-year-old Caucasian male patient was referred to our Unit for incidental detection of two complex renal masses in the left portion of a HSK. The 50 × 55 mm, larger, predominantly exophytic renal mass was located at the middle-lower pole of the left-sided kidney (PADUA score 9). The 16 × 17 mm, smaller, hilar renal mass was located at the middle-higher pole of the left-sided kidney (PADUA score 9). Contrast-enhanced CT scan images in DICOM format were processed using a dedicated software to achieve a HA3D virtual reconstructions. RAPN was performed by a highly experienced surgeon using the da Vinci Si robotic platform with a three-arm configuration. A selective delayed clamping strategy was adopted for resection of the larger renal mass while a clampless strategy was adopted for the smaller renal mass. An enucleative resection strategy was pursued for both tumors. Results: The overall operative time was 150 min, with a warm ischemia time of 21 min. No intraoperative or postoperative complications were recorded. Final resection technique according to the SIB score was pure enucleation for both masses. At histopathological analysis, both renal masses were clear cell renal cell carcinoma (ccRCC) (stage pT1bNxMx and pT3aNxMx for the larger and smaller mass, respectively). At a follow-up of 7 months, there was no evidence of local or systemic recurrence. Conclusions: Surgical management of complex renal masses in patients with HSKs is challenging and decision-making is highly nuanced. To optimize postoperative outcomes, proper surgical experience and careful preoperative planning are key. In this regard, 3D models can play a crucial role to refine patient counseling, surgical decision-making, and pre- and intraoperative planning during RAPN, tailoring surgical strategies and techniques according to the single patient's anatomy.

Project description:BackgroundThere has been an increasing trend toward pulmonary segmentectomies to treat early-stage lung cancer, small intrapulmonary metastases, and localized benign pathology. A complete preoperative understanding of pulmonary anatomy is essential for accurate surgical planning and case selection. Identifying intersegmental divisions is extremely difficult when performed on computed tomography. For the preoperative planning of segmentectomies, virtual reality (VR) and artificial intelligence could allow 3-dimensional visualization of the complex anatomy of pulmonary segmental divisions, vascular arborization, and bronchial anatomy. This technology can be applied by surgeons preoperatively to gain better insight into a patient's anatomy for planning segmentectomy.MethodsIn this prospective observational pilot study, we aim to assess and demonstrate the technical feasibility and clinical applicability of the first dedicated artificial intelligence-based and immersive 3-dimensional-VR platform (PulmoVR; jointly developed and manufactured by Department of Cardiothoracic Surgery [Erasmus Medical Center, Rotterdam, The Netherlands], MedicalVR [Amsterdam, The Netherlands], EVOCS Medical Image Communication [Fysicon BV, Oss, The Netherlands], and Thirona [Nijmegen, The Netherlands]) for preoperative planning of video-assisted thoracoscopic segmentectomies.ResultsA total of 10 eligible patients for segmentectomy were included in this study after referral through the institutional thoracic oncology multidisciplinary team. PulmoVR was successfully applied as a supplementary imaging tool to perform video-assisted thoracoscopic segmentectomies. In 40% of the cases, the surgical strategy was adjusted due to the 3-dimensional-VR-based evaluation of anatomy. This underlines the potential benefit of additional VR-guided planning of segmentectomy for both surgeon and patient.ConclusionsOur study demonstrates the successful development and clinical application of the first dedicated artificial intelligence and VR platform for the planning of pulmonary segmentectomy. This is the first study that shows an immersive virtual reality-based application for preoperative planning of segmentectomy to the best of our knowledge.