Three-dimensional Elastic Augmented-reality Robot-assisted Radical Prostatectomy Using Hyperaccuracy Three-dimensional Reconstruction Technology: A Step Further in the Identification of Capsular Involvement

Porpiglia, Francesco; Checcucci, Enrico; Amparore, Daniele; Manfredi, Matteo; Massa, Federica; Piazzolla, Pietro; Manfrin, Diego; Piana, Alberto; Tota, Donatella; Bollito, Enrico; Fiori, Cristian

doi:10.1016/j.eururo.2019.03.037

Cited by 94 publications

(40 citation statements)

References 27 publications

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“…The accuracy of the NeuroSAFE technique can be increased by reducing the slice thickness at the postero‐lateral margin to 3 mm (from 5 mm), but we have demonstrated excellent concordance with our current process and so it remains unchanged in the protocol in order to manage resources well with no compromise on detail. The emergence of promising fast digital imaging techniques [28] and the ongoing development of new platforms to incorporate preoperative mpMRI information intra‐operatively via augmented reality also hold considerable promise for future strategies to reduce the risk of PSMs [29].…”

Section: Discussionmentioning

confidence: 99%

NeuroSAFE frozen section during robot‐assisted radical prostatectomy: peri‐operative and histopathological outcomes from the NeuroSAFE PROOF feasibility randomized controlled trial

et al. 2021

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Objectives To report on the methods, peri‐operative outcomes and histopathological concordance between frozen and final section from the NeuroSAFE PROOF feasibility study (NCT03317990). Patients and Methods Between May 2018 and March 2019, 49 patients at two UK centres underwent robot‐assisted radical prostatectomy (RARP). Twenty‐five patient were randomized to NeuroSAFE RARP (intervention arm) and 24 to standard RARP (control arm). Frozen section was compared to final paraffin section margin assessment in the 25 patients in the NeuroSAFE arm. Operation timings and complications were collected prospectively in both arms. Results Fifty neurovascular bundles (NVBs) from 25 patients in the NeuroSAFE arm were analysed. When analysed by each pathological section (n = 250, average five per side), we noted a sensitivity of 100%, a specificity of 99.2%, and an area under the curve (AUC) of 0.994 (95% confidence interval [CI] 0.985 to 1; P ≤0.001). On an NVB basis (n = 50), sensitivity was 100%, specificity was 92.7%, and the AUC was 0.963 (95% CI 0.914 to 1; P ≤0.001). NeuroSAFE RARP lasted a mean of 3 h 16 min (knife to skin to off table, 95% CI 3 h 2 min–3 h 30 min) compared to 2 h 4 min (95% CI 2 h 2 min–2 h 25 min; P ≤0.001) for standard RARP. There was no morbidity associated with the additional length of operating time on in the NeuroSAFE arm. Conclusion This feasibility study demonstrates the safety, reproducibility and excellent histopathological concordance of the NeuroSAFE technique in the NeuroSAFE PROOF trial. Although the technique increases the duration of RARP, this does not cause short‐term harm. Confirmation of feasibility has led to the opening of the fully powered NeuroSAFE PROOF randomized controlled trial, which is currently under way at four sites in the UK.

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Section: Discussionmentioning

confidence: 99%

NeuroSAFE frozen section during robot‐assisted radical prostatectomy: peri‐operative and histopathological outcomes from the NeuroSAFE PROOF feasibility randomized controlled trial

et al. 2021

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“…We finally implement a pipeline to map a 3D model to the 2D video stream starting from the output of the segmentation, and compare it with the previous approach-based solely on CV techniques. The former approach was also tested and validated during in-vivo surgery, and the results obtained in the improving of the quality of the biopsy are documented in [ 17 ] and [ 35 ]. This demonstrated that the post prostatectomy biopsy’s precision increases with the application of our general approach.…”

Section: Discussionmentioning

confidence: 99%

Real-time deep learning semantic segmentation during intra-operative surgery for 3D augmented reality assistance

et al. 2021

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Purpose The current study aimed to propose a Deep Learning (DL) and Augmented Reality (AR) based solution for a in-vivo robot-assisted radical prostatectomy (RARP), to improve the precision of a published work from our group. We implemented a two-steps automatic system to align a 3D virtual ad-hoc model of a patient’s organ with its 2D endoscopic image, to assist surgeons during the procedure. Methods This approach was carried out using a Convolutional Neural Network (CNN) based structure for semantic segmentation and a subsequent elaboration of the obtained output, which produced the needed parameters for attaching the 3D model. We used a dataset obtained from 5 endoscopic videos (A, B, C, D, E), selected and tagged by our team’s specialists. We then evaluated the most performing couple of segmentation architecture and neural network and tested the overlay performances. Results U-Net stood out as the most effecting architectures for segmentation. ResNet and MobileNet obtained similar Intersection over Unit (IoU) results but MobileNet was able to elaborate almost twice operations per seconds. This segmentation technique outperformed the results from the former work, obtaining an average IoU for the catheter of 0.894 (σ = 0.076) compared to 0.339 (σ = 0.195). This modifications lead to an improvement also in the 3D overlay performances, in particular in the Euclidean Distance between the predicted and actual model’s anchor point, from 12.569 (σ= 4.456) to 4.160 (σ = 1.448) and in the Geodesic Distance between the predicted and actual model’s rotations, from 0.266 (σ = 0.131) to 0.169 (σ = 0.073). Conclusion This work is a further step through the adoption of DL and AR in the surgery domain. In future works, we will overcome the limits of this approach and finally improve every step of the surgical procedure.

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“…Porpiglia et al recently published their pioneering work on three-dimensional elastic augmented reality when performing RALRP (36). These virtual models are constructed using images from high-resolution mpMRI.…”

Section: Augmented-reality Radical Prostatectomymentioning

confidence: 99%

Techniques of robotic radical prostatectomy for the management of prostate cancer: which one, when and why

Liu

Hemal

2020

Transl Androl Urol

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The advent of robotic assistance in surgery has completely revolutionized the surgical management of prostate cancer. It enables precise dissection and reconstruction in order to maximize oncological and functional outcomes. In many parts of the world, robot-assisted laparoscopic radical prostatectomy has evolved to become the surgical standard of care for localized disease, including in appropriately selected patients with high risk prostate cancer. Its role has also been expanded to encompass cytoreductive prostatectomy and salvage radical prostatectomy. As surgical expertise grows with robotic assistance, several novel and non-radical approaches have been developed to further mitigate treatment side effects. Patient characteristics, disease factors and surgeon expertise are important metrics for consideration when selecting the most appropriate technique for any given patient.

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Three-dimensional Elastic Augmented-reality Robot-assisted Radical Prostatectomy Using Hyperaccuracy Three-dimensional Reconstruction Technology: A Step Further in the Identification of Capsular Involvement

Cited by 94 publications

References 27 publications

NeuroSAFE frozen section during robot‐assisted radical prostatectomy: peri‐operative and histopathological outcomes from the NeuroSAFE PROOF feasibility randomized controlled trial

NeuroSAFE frozen section during robot‐assisted radical prostatectomy: peri‐operative and histopathological outcomes from the NeuroSAFE PROOF feasibility randomized controlled trial

Real-time deep learning semantic segmentation during intra-operative surgery for 3D augmented reality assistance

Techniques of robotic radical prostatectomy for the management of prostate cancer: which one, when and why

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