Real-time Augmented Reality Three-dimensional Guided Robotic Radical Prostatectomy: Preliminary Experience and Evaluation of the Impact on Surgical Planning

Schiavina, Riccardo; Bianchi, Lorenzo; Lodi, Simone; Cercenelli, Laura; Chessa, F.; Bortolani, Barbara; Gaudiano, Caterina; Casablanca, Carlo; Droghetti, Matteo; Porreca, Angelo; Romagnoli, Daniele; Golfieri, Rita; Giunchi, Francesca; Fiorentino, Michelangelo; Marcelli, Emanuela; Diciotti, Stefano; Brunocilla, Eugenio

doi:10.1016/j.euf.2020.08.004

Cited by 53 publications

(37 citation statements)

References 25 publications

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“…Schiavina et al reported that the AR-3D guided surgery can be used to improve intraoperative “real time navigation” to identify the index lesion in robot-assisted radical prostatectomy. Moreover, nerve-sparing surgery approach during robot-assisted radical prostatectomy can be regulated through AR-3D guidance [ 32 ]. Additionally, surgical robots have the potential to provide haptic feedback to the surgeon [ 33 ].…”

Section: Resultsmentioning

confidence: 99%

Mixed reality applications in urology: Requirements and future potential

Reis

Yılmaz

Rambach

et al. 2021

Annals of Medicine &Amp; Surgery

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Background Mixed reality (MR), the computer-supported augmentation of a real environment with virtual elements, becomes ever more relevant in the medical domain, especially in urology, ranging from education and training over surgeries. We aimed to review existing MR technologies and their applications in urology. Methods A non-systematic review of current literature was performed using the PubMed-Medline database using the medical subject headings (MeSH) term “mixed reality”, combined with one of the following terms: “virtual reality”, “augmented reality”, ‘’urology’’ and “augmented virtuality”. The relevant studies were utilized. Results MR applications such as MR guided systems, immersive VR headsets, AR models, MR-simulated ureteroscopy and smart glasses have enormous potential in education, training and surgical interventions of urology. Medical students, urology residents and inexperienced urologists can gain experience thanks to MR technologies. MR applications are also used in patient education before interventions. Conclusions For surgical support, the achievable accuracy is often not sufficient. The main challenges are the non-rigid nature of the genitourinary organs, intraoperative data acquisition, online and multimodal registration and calibration of devices. However, the progress made in recent years is tremendous in all respects and the gap is constantly shrinking.

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Section: Resultsmentioning

confidence: 99%

Mixed reality applications in urology: Requirements and future potential

Reis

Yılmaz

Rambach

et al. 2021

Annals of Medicine &Amp; Surgery

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“…The surgical plan consisted of RP with PLND with open, laparoscopic or robotic approach at the Division of Urology (IRCCS, AOU di Bologna) by experienced surgeons, as previously described [ 32 , 33 ], following standard recommendations [ 1 ], decided according to clinical and conventional imaging staging work-up and not altered by the results of the investigational scan.…”

Section: Methodsmentioning

confidence: 99%

The Role of [18F]Fluciclovine PET/CT in the Characterization of High-Risk Primary Prostate Cancer: Comparison with [11C]Choline PET/CT and Histopathological Analysis

Zanoni

Mei

Bianchi

et al. 2021

Cancers

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The primary aim of the study was to evaluate the role of [18F]Fluciclovine PET/CT in the characterization of intra-prostatic lesions in high-risk primary PCa patients eligible for radical prostatectomy, in comparison with conventional [11C]Choline PET/CT and validated by prostatectomy pathologic examination. Secondary aims were to determine the performance of PET semi-quantitative parameters (SUVmax; target-to-background ratios [TBRs], using abdominal aorta, bone marrow and liver as backgrounds) for malignant lesion detection (and best cut-off values) and to search predictive factors of malignancy. A six sextants prostate template was created and used by PET readers and pathologists for data comparison and validation. PET visual and semi-quantitative analyses were performed: for instance, patient-based, blinded to histopathology; subsequently lesion-based, un-blinded, according to the pathology reference template. Among 19 patients included (mean age 63 years, 89% high and 11% very-high-risk, mean PSA 9.15 ng/mL), 45 malignant and 31 benign lesions were found and 19 healthy areas were selected (n = 95). For both tracers, the location of the “blinded” prostate SUVmax matched with the lobe of the lesion with the highest pGS in 17/19 cases (89%). There was direct correlation between [18F]Fluciclovine uptake values and pISUP. Overall, lesion-based (n = 95), the performance of PET semiquantitative parameters, with either [18F]Fluciclovine or [11C]Choline, in detecting either malignant/ISUP2-5/ISUP4-5 PCa lesions, was moderate and similar (AUCs ≥ 0.70) but still inadequate (AUCs ≤ 0.81) as a standalone staging procedure. A [18F]Fluciclovine TBR-L3 ≥ 1.5 would depict a clinical significant lesion with a sensitivity and specificity of 85% and 68% respectively; whereas a SUVmax cut-off value of 4 would be able to identify a ISUP 4-5 lesion in all cases (sensitivity 100%), although with low specificity (52%). TBRs (especially with threshold significantly higher than aorta and slightly higher than bone marrow), may be complementary to implement malignancy targeting.

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“…Here we follow the common framework for which AI includes methods such as Machine Learning and Deep Learning. AI tools can help PCa diagnosis in many aspects, such as in prostate gland volume segmentation, lesion segmentation, detection, and characterization [12,13], and it has also been developed for applications in robotic surgery both for prostate cancer [14] and kidney cancer [15], digital pathology, and genomics [16]. However, one of the most challenging tasks is to accurately stage lesions aggressiveness, in particular classifying between not clinically significant tumors and clinically significant PCa.…”

Section: Ai In Pca Characterizationmentioning

confidence: 99%

“…Only a few studies comment on this score giving possible explanations for false negative such as the presence of small and subcapsular lesions [35]. As far as the specificity is concerned, the results are much less controversial, with similar results obtained in [26] and better AI performances achieved in [14,19,28], where the specificities on the test set of PI-RADS and AI are 0.81 vs. 0.89, 0.48 vs. 0.8, and 0.28 vs. 0.57, respectively.…”

Section: Figurementioning

confidence: 99%

Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data

Corradini

Brizi

Gaudiano

et al. 2021

Cancers

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Many efforts have been carried out for the standardization of multiparametric Magnetic Resonance (mp-MR) images evaluation to detect Prostate Cancer (PCa), and specifically to differentiate levels of aggressiveness, a crucial aspect for clinical decision-making. Prostate Imaging—Reporting and Data System (PI-RADS) has contributed noteworthily to this aim. Nevertheless, as pointed out by the European Association of Urology (EAU 2020), the PI-RADS still has limitations mainly due to the moderate inter-reader reproducibility of mp-MRI. In recent years, many aspects in the diagnosis of cancer have taken advantage of the use of Artificial Intelligence (AI) such as detection, segmentation of organs and/or lesions, and characterization. Here a focus on AI as a potentially important tool for the aim of standardization and reproducibility in the characterization of PCa by mp-MRI is reported. AI includes methods such as Machine Learning and Deep learning techniques that have shown to be successful in classifying mp-MR images, with similar performances obtained by radiologists. Nevertheless, they perform differently depending on the acquisition system and protocol used. Besides, these methods need a large number of samples that cover most of the variability of the lesion aspect and zone to avoid overfitting. The use of publicly available datasets could improve AI performance to achieve a higher level of generalizability, exploiting large numbers of cases and a big range of variability in the images. Here we explore the promise and the advantages, as well as emphasizing the pitfall and the warnings, outlined in some recent studies that attempted to classify clinically significant PCa and indolent lesions using AI methods. Specifically, we focus on the overfitting issue due to the scarcity of data and the lack of standardization and reproducibility in every step of the mp-MR image acquisition and the classifier implementation. In the end, we point out that a solution can be found in the use of publicly available datasets, whose usage has already been promoted by some important initiatives. Our future perspective is that AI models may become reliable tools for clinicians in PCa diagnosis, reducing inter-observer variability and evaluation time.

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Real-time Augmented Reality Three-dimensional Guided Robotic Radical Prostatectomy: Preliminary Experience and Evaluation of the Impact on Surgical Planning

Cited by 53 publications

References 25 publications

Mixed reality applications in urology: Requirements and future potential

Mixed reality applications in urology: Requirements and future potential

The Role of [18F]Fluciclovine PET/CT in the Characterization of High-Risk Primary Prostate Cancer: Comparison with [11C]Choline PET/CT and Histopathological Analysis

Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data

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