Three-dimensional automatic artificial intelligence driven augmented-reality selective biopsy during nerve-sparing robot-assisted radical prostatectomy:A feasibility and accuracy study

Objective:To evaluate the accuracy of our new three-dimensional(3D)automatic augmented reality(AAR)system guided by artificial intelligence in the identification of tumour’s location at the level of the preserved neurovascular bundle(NVB)at the end of the extirpative phase of nerve-sparing robot-assisted radical prostatectomy.Methods:In this prospective study,we enrolled patients with prostate cancer(clinical stages cT1ce3,cN0,and cM0)with a positive index lesion at target biopsy,suspicious for capsular contact or extracapsular extension at preoperative multiparametric magnetic resonance imaging.Patients underwent robot-assisted radical prostatectomy at San Luigi Gonzaga Hospital(Orbassano,Turin,Italy),from December 2020 to December 2021.At the end of extirpative phase,thanks to our new AAR artificial intelligence driven system,the virtual prostate 3D model allowed to identify the tumour’s location at the level of the preserved NVB and to perform a selective excisional biopsy,sparing the remaining portion of the bundle.Perioperative and postoperative data were evaluated,especially focusing on the positive surgical margin(PSM)rates,potency,continence recovery,and biochemical recurrence.Results:Thirty-four patients were enrolled.In 15(44.1%)cases,the target lesion was in contact with the prostatic capsule at multiparametric magnetic resonance imaging(Wheeler grade L2)while in 19(55.9%)cases extracapsular extension was detected(Wheeler grade L3).3D AAR guided biopsies were negative in all pathological tumour stage 2(pT2)patients while they revealed the presence of cancer in 14 cases in the pT3 cohort(14/16;87.5%).PSM rates were 0%and 7.1%in the pathological stages pT2 and pT3(<3 mm,Gleason score 3),respectively.Conclusion:With the proposed 3D AAR system,it is possible to correctly identify the lesion’s location on the NVB in 87.5%of pT3 patients and perform a 3D-guided tailored nerve-sparing even in locally advanced diseases,without compromising the oncological safety in terms of PSM rates.

Three-dimensional imaging reconstruction of the kidney’s anatomy for a tailored minimally invasive partial nephrectomy:A pilot study

Objective:The aim of the study was to evaluate three-dimensional virtual models(3DVMs)usefulness in the intraoperative assistance of minimally-invasive partial nephrectomy in highly complex renal tumors.Methods:At our institution cT1-2N0M0 all renal masses with Preoperative Aspects and Dimensions Used for an Anatomical classification score≥10 treated with minimally-invasive partial nephrectomy were considered for the present study.For inclusion a baseline contrast-enhanced computed tomography in order to obtain 3DVMs,the baseline and postoperative serum creatinine as well as estimated glomerular filtration rate values were needed.These patients,in which 3DVMs were used to assist the surgeon in the planning and intraoperative guidance,were then compared with a control group of patients who underwent minimally-invasive partial nephrectomy with the same renal function assessments,but without 3DVMs.Multivariable logistic regression models were used to predict the margin,ischemia,and complication score achievement.Results:Overall,79 patients met the inclusion criteria and were compared with 143 complex renal masses without 3DVM assistance.The 3DVM group showed better postoperative outcomes in terms of baseline-weighted differential estimated glomerular filtration rate(-17.7%vs.-22.2%,p=0.03),postoperative complications(16.5%vs.23.1%,p=0.03),and major complications(Clavien Dindo>III,2.5%vs.5.6%,p=0.03).At multivariable logistic regression 3DVM assistance independently predicted higher rates of successful partial nephrectomy(odds ratio:1.42,p=0.03).Conclusion:3DVMs represent a useful tool to plan a tailored surgical approach in case of surgically complex masses.They can be used in different ways,matching the surgeon's needs from the planning phase to the demolitive and reconstructive phase,leading towards maximum safety and efficacy outcomes.

Artificial intelligence for renal cancer:From imaging to histology and beyond

Artificial intelligence(AI)has made considerable progress within the last decade and is the subject of contemporary literature.This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation.Renal cell carcinoma(RCC)has a rising incidence since most tumors are now detected at an earlier stage due to improved imaging.This creates considerable challenges as approximately 10%e17%of kidney tumors are designated as benign in histopathological evaluation;however,certain co-morbid populations(the obese and elderly)have an increased peri-interventional risk.AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions.The narrative review introduced basic principles and provide a comprehensive overview of current AI techniques for RCC.Currently,AI applications can be found in any aspect of RCC management including diagnostics,perioperative care,pathology,and follow-up.Most commonly applied models include neural networks,random forest,support vector machines,and regression.However,for implementation in daily practice,health care providers need to develop a basic understanding and establish interdisciplinary collaborations in order to standardize datasets,define meaningful endpoints,and unify interpretation.

New imaging technologies for robotic kidney cancer surgery

Objective:Kidney cancers account for approximately 2%of all newly diagnosed cancer in 2020.Among the primary treatment options for kidney cancer,urologist may choose between radical or partial nephrectomy,or ablative therapies.Nowadays,robotic-assisted partial nephrectomy(RAPN)for the management of renal cancers has gained popularity,up to being considered the gold standard.However,RAPN is a challenging procedure with a steep learning curve.Methods:In this narrative review,different imaging technologies used to guide and aid RAPN are discussed.Results:Three-dimensional visualization technology has been extensively discussed in RAPN,showing its value in enhancing robotic-surgery training,patient counseling,surgical planning,and intraoperative guidance.Intraoperative imaging technologies such as intracorporeal ultrasound,near-infrared fluorescent imaging,and intraoperative pathological examination can also be used to improve the outcomes following RAPN.Finally,artificial intelligence may play a role in the field of RAPN soon.Conclusion:RAPN is a complex surgery;however,many imaging technologies may play an important role in facilitating it.

A snapshot into the future of image-guided surgery for renal cancer

Robot-assisted partial nephrectomy(RAPN)is certainly one of the most fascinating and complex urological procedures.This is ascribable to its vast heterogeneity from one case to another,related to patient’s anatomical variability and tumour’s characteristics.Over the last years,with the aim to assist the surgeons in handling ever more difficult lesions(totally endophytic or large volume[1,2])suitable for RAPN,several technologies were proposed and tested,from preoperative planning to intraoperative assistance or navigation[3].