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      Virtual reality and artificial intelligence for 3-dimensional planning of lung segmentectomies

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          Abstract

          Background

          There 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.

          Methods

          In 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.

          Results

          A 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.

          Conclusions

          Our 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.

          Graphical abstract

          The identification of segmental anatomy for surgical planning of lung segmentectomy is quite challenging with conventional imaging. In the current study, a novel method is presented to create artificial intelligence based virtual reality reconstructions to prepare for thoracoscopic segmentectomies in 10 consecutive patients. VR, Virtual reality; CT, computed tomography.

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          Most cited references18

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          Anatomic thoracoscopic pulmonary segmentectomy under 3-dimensional multidetector computed tomography simulation: a report of 52 consecutive cases.

          The purpose of this retrospective study was to evaluate the efficacy of anatomic thoracoscopic pulmonary segmentectomy performed under the guidance of 3-dimensional multidetector computed tomography simulation.
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            Video-Assisted Thoracoscopic Surgery Is a Safe and Effective Alternative to Thoracotomy for Anatomical Segmentectomy in Patients With Clinical Stage I Non-Small Cell Lung Cancer.

            There is rising interest among thoracic surgeons in anatomical segmental resection for early-stage non-small cell lung cancer (NSCLC). In the current study we compared video-assisted thoracoscopic surgery (VATS) and thoracotomy approaches for segmentectomy to explore the safety and oncologic efficacy of VATS for stage I NSCLC.
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              Effect of 3-Dimensional Virtual Reality Models for Surgical Planning of Robotic-Assisted Partial Nephrectomy on Surgical Outcomes : A Randomized Clinical Trial

              Question Does the use of 3-dimensional, virtual reality models for planning robotic-assisted partial nephrectomy improve surgical outcomes? Findings In this single-blind randomized clinical trial involving 92 patients, the use of 3-dimensional virtual reality models reduced the operative time, estimated blood loss, clamp time, and length of hospital stay. Meaning This randomized clinical trial demonstrates key outcomes improvements when using 3-dimensional, virtual reality models to plan robotic-assisted partial nephrectomy. This single-blind randomized clinical trial examines whether 3-dimensional (3-D) virtual reality models used to plan robotic-assisted partial nephrectomy can reduce the operative time, estimated blood loss, clamp time, and length of hospital stay. 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
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                Author and article information

                Contributors
                Journal
                JTCVS Tech
                JTCVS Tech
                JTCVS Techniques
                Elsevier
                2666-2507
                16 March 2021
                June 2021
                16 March 2021
                : 7
                : 309-321
                Affiliations
                [a ]Department of Cardiothoracic Surgery, Thoraxcenter, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands
                [b ]Department of Pulmonary Medicine, Erasmus Medical Center Cancer Institute, Rotterdam, The Netherlands
                Author notes
                []Address for reprints: Amir H. Sadeghi, MD, MSc, Department of Cardiothoracic Surgery, Erasmus University Medical Center, Room Rg-635, PO Box 2040, 3015 GD Rotterdam, The Netherlands. h.sadeghi@ 123456erasmusmc.nl
                Article
                S2666-2507(21)00253-4
                10.1016/j.xjtc.2021.03.016
                8312141
                34318279
                a69dde80-c054-44ac-86ef-a5d34e30b13f
                © 2021 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 10 March 2021
                : 10 March 2021
                Categories
                Thoracic: Lung Cancer: Evolving Technology

                virtual reality,preoperative planning,segmentectomy,video-assisted thoracoscopic surgery,lung cancer,2d, 2 dimensional,3d, 3 dimensional,ai, artificial intelligence,ct, computed tomography,dicom, digital imaging and communication in medicine,nsclc, non–small cell lung cancer,s, segment,vats, video assisted thoracoscopic surgery,vr, virtual reality

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