Augmented Reality to Guide Selective Clamping and Tumor Dissection During Robot-assisted Partial Nephrectomy: A Preliminary Experience

From Diagnosis to Precision Surgery: the Transformative Role of Artificial Intelligence in Urologic Imaging

The multidisciplinary nature of artificial intelligence (AI) has allowed for rapid growth of its application in medical imaging. Artificial intelligence algorithms can augment various imaging modalities such as X-rays, CT, and MRI to improve image quality and generate high-resolution three-dimensional images. AI reconstruction of three-dimensional models of patient anatomy from CT or MRI scans can better enable urologists to visualize structures and accurately plan surgical approaches. AI can also be optimized to create virtual reality simulations of surgical procedures based on patient-specific data, giving urologists more hands-on experience and preparation. Recent development of artificial intelligence modalities such as TeraRecon and Ceevra offer rapid and efficient medical imaging analyses aimed at enhancing the provision of urologic care, notably for intra-operative guidance during robotic-assisted radical prostatectomy and partial nephrectomy. Notably, use of 3-D VR models has been linked to improved operative times, shorter hospital stay, reduced clamp time, and minimized blood loss in patients undergoing robotic assisted laparoscopic partial nephrectomy when compared to standard operative approaches that do not utilize VR technologies.

Visual extended reality tools in image-guided surgery in urology: a systematic review

PURPOSE

The aim of this systematic review is to assess the clinical implications of employing various Extended Reality (XR) tools for image guidance in urological surgery.

METHODS

In June 2023, a systematic electronic literature search was conducted using the Medline database (via PubMed), Embase (via Ovid), Scopus, and Web of Science. The search strategy was designed based on the PICO (Patients, Intervention, Comparison, Outcome) criteria. Study protocol was registered on PROSPERO (registry number CRD42023449025). We incorporated retrospective and prospective comparative studies, along with single-arm studies, which provided information on the use of XR, Mixed Reality (MR), Augmented Reality (AR), and Virtual Reality (VR) in urological surgical procedures. Studies that were not written in English, non-original investigations, and those involving experimental research on animals or cadavers were excluded from our analysis. The quality assessment of comparative and cohort studies was conducted utilizing the Newcastle-Ottawa scale, whilst for randomized controlled trials (RCTs), the Jadad scale was adopted. The level of evidence for each study was determined based on the guidelines provided by the Oxford Centre for Evidence-Based Medicine.

RESULTS

The initial electronic search yielded 1,803 papers after removing duplicates. Among these, 58 publications underwent a comprehensive review, leading to the inclusion of 40 studies that met the specified criteria for analysis. 11, 20 and 9 studies tested XR on prostate cancer, kidney cancer and miscellaneous, including bladder cancer and lithiasis surgeries, respectively. Focusing on the different technologies 20, 15 and 5 explored the potential of VR, AR and MR. The majority of the included studies (i.e., 22) were prospective non-randomized, whilst 7 and 11 were RCT and retrospective studies respectively. The included studies that revealed how these new tools can be useful both in preoperative and intraoperative setting for a tailored surgical approach.

CONCLUSIONS

AR, VR and MR techniques have emerged as highly effective new tools for image-guided surgery, especially for urologic oncology. Nevertheless, the complete clinical advantages of these innovations are still in the process of evaluation.

Current Application of Navigation Systems in Robotic-Assisted and Laparoscopic Partial Nephrectomy: Focus on the Improvement of Surgical Performance and Outcomes

Kidney cancer represents the third most prevalent malignancy among all types of genitourinary cancer worldwide. Currently, there is a growing trend of employing partial nephrectomy for the management of large and complex tumors. Surgical outcomes are associated with some amendable surgical factors, including warm ischemic time, pedicle clamping, preserved volume of renal parenchyma, appropriate surgical strategy, and precise resection of the tumor. Improving surgical performance is pivotal for achieving favorable surgical outcomes. Due to advancements in imaging visualization technology and the shift of the medical paradigm toward precision medicine, an increasing number of navigation systems have been implemented in partial nephrectomy procedures. The navigation system can assist surgeons in formulating optimal surgical strategies and enhance the safety, precision, and feasibility of resecting complex renal tumors. In this review, we provide an overview of currently available navigation systems and their feasible applications, with a focus on how they contribute to the improvement of surgical performance and outcomes during robotic-assisted and laparoscopic partial nephrectomy.

Patient-specific simulations and navigation systems for partial nephrectomy

Partial nephrectomy (PN) is the standard treatment for T1 renal cell carcinoma. PN is affected more by surgical variations and requires greater surgical experience than radical nephrectomy. Patient-specific simulations and navigation systems may help to reduce the surgical experience required for PN. Recent advances in three-dimensional (3D) virtual reality (VR) imaging and 3D printing technology have allowed accurate patient-specific simulations and navigation systems. We reviewed previous studies about patient-specific simulations and navigation systems for PN. Recently, image reconstruction technology has developed, and commercial software that converts two-dimensional images into 3D images has become available. Many urologists are now able to view 3DVR images when preparing for PN. Surgical simulations based on 3DVR images can change surgical plans and improve surgical outcomes, and are useful during patient consultations. Patient-specific simulators that are capable of simulating surgical procedures, the gold-standard form of patient-specific simulations, have also been reported. Besides VR, 3D printing is also useful for understanding patient-specific information. Some studies have reported simulation and navigation systems for PN based on solid 3D models. Patient-specific simulations are a form of preoperative preparation, whereas patient-specific navigation is used intraoperatively. Navigation-assisted PN procedures using 3DVR images have become increasingly common, especially in robotic surgery. Some studies found that these systems produced improvements in surgical outcomes. Once its accuracy has been confirmed, it is hoped that this technology will spread further and become more generalized.

Preclinical Application of Augmented Reality in Pediatric Craniofacial Surgery: An Accuracy Study

Background: Augmented reality (AR) allows the overlapping and integration of virtual information with the real environment. The camera of the AR device reads the object and integrates the virtual data. It has been widely applied to medical and surgical sciences in recent years and has the potential to enhance intraoperative navigation. Materials and methods: In this study, the authors aim to assess the accuracy of AR guidance when using the commercial HoloLens 2 head-mounted display (HMD) in pediatric craniofacial surgery. The Authors selected fronto-orbital remodeling (FOR) as the procedure to test (specifically, frontal osteotomy and nasal osteotomy were considered). Six people (three surgeons and three engineers) were recruited to perform the osteotomies on a 3D printed stereolithographic model under the guidance of AR. By means of calibrated CAD/CAM cutting guides with different grooves, the authors measured the accuracy of the osteotomies that were performed. We tested accuracy levels of ±1.5 mm, ±1 mm, and ±0.5 mm. Results: With the HoloLens 2, the majority of the individuals involved were able to successfully trace the trajectories of the frontal and nasal osteotomies with an accuracy level of ±1.5 mm. Additionally, 80% were able to achieve an accuracy level of ±1 mm when performing a nasal osteotomy, and 52% were able to achieve an accuracy level of ±1 mm when performing a frontal osteotomy, while 61% were able to achieve an accuracy level of ±0.5 mm when performing a nasal osteotomy, and 33% were able to achieve an accuracy level of ±0.5 mm when performing a frontal osteotomy. Conclusions: despite this being an in vitro study, the authors reported encouraging results for the prospective use of AR on actual patients.

Improving Augmented Reality Through Deep Learning: Real-time Instrument Delineation in Robotic Renal Surgery

Several barriers prevent the integration and adoption of augmented reality (AR) in robotic renal surgery despite the increased availability of virtual three-dimensional (3D) models. Apart from correct model alignment and deformation, not all instruments are clearly visible in AR. Superimposition of a 3D model on top of the surgical stream, including the instruments, can result in a potentially hazardous surgical situation. We demonstrate real-time instrument detection during AR-guided robot-assisted partial nephrectomy and show the generalization of our algorithm to AR-guided robot-assisted kidney transplantation. We developed an algorithm using deep learning networks to detect all nonorganic items. This algorithm learned to extract this information for 65 927 manually labeled instruments on 15 100 frames. Our setup, which runs on a standalone laptop, was deployed in three different hospitals and used by four different surgeons. Instrument detection is a simple and feasible way to enhance the safety of AR-guided surgery. Future investigations should strive to optimize efficient video processing to minimize the 0.5-s delay currently experienced. General AR applications also need further optimization, including detection and tracking of organ deformation, for full clinical implementation.

A survey of augmented reality methods to guide minimally invasive partial nephrectomy

INTRODUCTION

Minimally invasive partial nephrectomy (MIPN) has become the standard of care for localized kidney tumors over the past decade. The characteristics of each tumor, in particular its size and relationship with the excretory tract and vessels, allow one to judge its complexity and to attempt predicting the risk of complications. The recent development of virtual 3D model reconstruction and computer vision has opened the way to image-guided surgery and augmented reality (AR).

OBJECTIVE

Our objective was to perform a systematic review to list and describe the different AR techniques proposed to support PN.

MATERIALS AND METHODS

The systematic review of the literature was performed on 12/04/22, using the keywords "nephrectomy" and "augmented reality" on Embase and Medline. Articles were considered if they reported surgical outcomes when using AR with virtual image overlay on real vision, during ex vivo or in vivo MIPN. We classified them according to the registration technique they use.

RESULTS

We found 16 articles describing an AR technique during MIPN procedures that met the eligibility criteria. A moderate to high risk of bias was recorded for all the studies. We classified registration methods into three main families, of which the most promising one seems to be surface-based registration.

CONCLUSION

Despite promising results, there do not exist studies showing an improvement in clinical outcomes using AR. The ideal AR technique is probably yet to be established, as several designs are still being actively explored. More clinical data will be required to establish the potential contribution of this technology to MIPN.

Workflow assessment of an augmented reality application for planning of perforator flaps in plastic reconstructive surgery: Game or game changer?

Objective

In contrast to the rising amount of financial investments for research and development in medical technology worldwide is the lack of usability and clinical readiness of the produced systems. We evaluated an augmented reality (AR) setup under development for preoperative perforator vessel mapping for elective autologous breast reconstruction.

Methods

In this grant-supported research pilot, we used magnetic resonance angiography data (MR-A) of the trunk to superimpose the scans on the corresponding patients with hands-free AR goggles to identify regions-of-interest for surgical planning. Perforator location was assessed using MR-A imaging (MR-A projection) and Doppler ultrasound data (3D distance) and confirmed intraoperatively in all cases. We evaluated usability (System Usability Scale, SUS), data transfer load and documented personnel hours for software development, correlation of image data, as well as processing duration to clinical readiness (time from MR-A to AR projections per scan).

Results

All perforator locations were confirmed intraoperatively, and we found a strong correlation between MR-A projection and 3D distance measurements (Spearman r = 0.894). The overall usability (SUS) was 67 ± 10 (=moderate to good). The presented setup for AR projections took 173 min to clinical readiness (=availability on AR device per patient).

Conclusion

In this pilot, we calculated development investments based on project-approved grant-funded personnel hours with a moderate to good usability outcome resulting from some limitations: assessment was based on one-time testing with no previous training, a time lag of AR visualizations on the body and difficulties in spatial AR orientation. The use of AR systems can provide new opportunities for future surgical planning, but has more potential for educational (e.g., patient information) or training purposes of medical under- and postgraduates (spatial recognition of imaging data associated with anatomical structures and operative planning). We expect future usability improvements with refined user interfaces, faster AR hardware and artificial intelligence-enhanced visualization techniques.

Application of Augmented Reality to Maxillary Resections: A Three-Dimensional Approach to Maxillofacial Oncologic Surgery

In the relevant global context, although virtual reality, augmented reality, and mixed reality have been emerging methodologies for several years, only now have technological and scientific advances made them suitable for revolutionizing clinical care and medical settings through the provision of advanced features and improved healthcare services. Over the past fifteen years, tools and applications using augmented reality (AR) have been designed and tested in the context of various surgical and medical disciplines, including maxillofacial surgery. The purpose of this paper is to show how a marker-less AR guidance system using the Microsoft^® HoloLens 2 can be applied in mandible and maxillary demolition surgery to guide maxillary osteotomies. We describe three mandibular and maxillary oncologic resections performed during 2021 using AR support. In these three patients, we applied a marker-less tracking method based on recognition of the patient's facial profile. The surgeon, using HoloLens 2 smart glasses, could see the virtual surgical planning superimposed on the patient's anatomy. We showed that performing osteotomies under AR guidance is feasible and viable, as demonstrated by comparison with osteotomies performed using CAD-CAM cutting guides. This technology has advantages and disadvantages. However, further research is needed to improve the stability and robustness of the marker-less tracking method applied to patient face recognition.

3D renal model for surgical planning of partial nephrectomy: A way to improve surgical outcomes

OBJECTIVE

to evaluate the impact of 3D model for a comprehensive assessment of surgical planning and quality of partial nephrectomy (PN).

MATERIALS AND METHODS

195 patients with cT1-T2 renal mass scheduled for PN were enrolled in two groups: Study Group (n= 100), including patients referred to PN with revision of both 2D computed tomography (CT) imaging and 3D model; Control group (n= 95), including patients referred to PN with revision of 2D CT imaging. Overall, 20 individuals were switched to radical nephrectomy (RN). The primary outcome was the impact of 3D models-based surgical planning on Trifecta achievement (defined as the contemporary absence of positive surgical margin, major complications and ≤30% postoperative eGFR reduction). The secondary outcome was the impact of 3D models on surgical planning of PN. Multivariate logistic regressions were used to identify predictors of selective clamping and Trifecta's achievement in patients treated with PN (n=175).

RESULTS

Overall, 73 (80.2%) patients in Study group and 53 (63.1%) patients in Control group achieved the Trifecta (p=0.01). The preoperative plan of arterial clamping was recorded as clampless, main artery and selective in 22 (24.2%), 22 (24.2%) and 47 (51.6%) cases in Study group vs. 31 (36.9%), 46 (54.8%) and 7 (8.3%) cases in Control group, respectively (p<0.001). At multivariate logistic regressions, the use of 3D model was found to be independent predictor of both selective or super-selective clamping and Trifecta's achievement.

CONCLUSION

3D-guided approach to PN increase the adoption of selective clamping and better predict the achievement of Trifecta.

Novel Volumetric and Morphological Parameters Derived from Three-dimensional Virtual Modeling to Improve Comprehension of Tumor's Anatomy in Patients with Renal Cancer

BACKGROUND

Three-dimensional (3D) models improve the comprehension of renal anatomy.

OBJECTIVE

To evaluate the impact of novel 3D-derived parameters, to predict surgical outcomes after robot-assisted partial nephrectomy (RAPN).

DESIGN, SETTING, AND PARTICIPANTS

Sixty-nine patients with cT1-T2 renal mass scheduled for RAPN were included. Three-dimensional virtual modeling was achieved from computed tomography. The following volumetric and morphological 3D parameters were calculated: V_T (volume of the tumor); V_T/V_K (ratio between tumor volume and kidney volume); CSA_3D (ie, contact surface area); UCS_3D (contact to the urinary collecting system); Tumor-Artery_3D: tumor's blood supply by tertiary segmental arteries (score = 1), secondary segmental artery (score = 2), or primary segmental/main renal artery (scoren = 3); S_T (tumor's sphericity); Conv_T (tumor's convexity); and Endophyticity_3D (ratio between the CSA_3D and the global tumor surface).

INTERVENTION

RAPN with a 3D model.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Three-dimensional parameters were compared between patients with and without complications. Univariate logistic regression was used to predict overall complications and type of clamping; linear regression was used to predict operative time, warm ischemia time, and estimated blood loss.

RESULTS AND LIMITATIONS

Overall, 11 (15%) individuals experienced overall complications (7.2% had Clavien ≥3 complications). Patients with urinary collecting system (UCS) involvement at 3D model (UCS_3D = 2), tumor with blood supply by primary or secondary segmentary arteries (Tumor-Artery_3D = 1 and 2), and high Endophyticity_3D values had significantly higher rates of overall complications (all p ≤ 0.03). At univariate analysis, UCS_3D, Tumor-Artery_3D, and Endophyticity_3D are significantly associated with overall complications; CSA_3D and Endophyticity_3D were associated with warm ischemia time; and CSA_3D was associated with selective clamping (all p ≤ 0.03). Sample size and the lack of interobserver variability are the main limits.

CONCLUSIONS

Three-dimensional modeling provides novel volumetric and morphological parameters to predict surgical outcomes after RAPN.

PATIENT SUMMARY

Novel morphological and volumetric parameters can be derived from a three-dimensional model to describe surgical complexity of renal mass and to predict surgical outcomes after robot-assisted partial nephrectomy.

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%–17% 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.

"Augmented reality" applications in urology: a systematic review

INTRODUCTION

Augmented reality (AR) applied to surgical procedures refers to the superimposition of preoperative or intra-operative images onto the operative field. Augmented reality has been increasingly used in myriad surgical specialties including Urology. The following study reviews advances in the use of AR for improvements in urologic outcomes.

EVIDENCE ACQUISITION

We identified all descriptive, validity, prospective randomized/nonrandomized trials and retrospective comparative/noncomparative studies about the use of AR in Urology up until March 2021. The MEDLINE, Scopus, and Web of Science databases were used for literature search. We conducted the study selection according to the PRISMA (Preferred Reporting Items for Systematic Reviews and meta-analysis statement) guidelines. We limited included studies to only those using AR, excluding all that used virtual reality technology.

EVIDENCE SYNTHESIS

A total of 60 studies were identified and included in the present analysis. Overall, 19 studies were descriptive/validity/phantom studies for specific AR methodologies, 4 studies were case reports, and 37 studies included clinical prospective/retrospective comparative studies.

CONCLUSIONS

Advances in AR have led to increasing registration accuracy as well as increased ability to identify anatomic landmarks and improve outcomes during Urologic procedures such as RARP and robot-assisted partial nephrectomy.

Robotic partial nephrectomy in 3D virtual reconstructions era: is the paradigm changed?

CONTEXT

The development of a tailored, patient-specific medical and surgical approach is becoming object of intense research. In kidney oncologic surgery, where a clear understanding of case-specific surgical anatomy is considered a key point to optimize the perioperative outcomes, such philosophy gained increasing importance. Recently, important advances in 3D virtual modeling technologies have fueled the interest for their application in the field of robotic minimally invasive surgery for kidney tumors.

OBJECTIVE

To provide a synthesis of current applications of 3D virtual models for robot-assisted partial nephrectomy.

EVIDENCE ACQUISITION

Medline, PubMed, the Cochrane Database, and Embase were screened for Literature regarding the use of 3D virtual models for robot-assisted partial nephrectomy (RAPN).

EVIDENCE SYNTHESIS

The use of 3D virtual models for RAPN has been tested in different settings, including surgical indication and planning, intraoperative guidance, and training. Currently, several studies are available on the application of this technology for surgical planning, demonstrating impact on clinical outcomes such as renal function recovery, whilst experiences concerning their intraoperative application for navigation are still experimental. One of the latest innovations in this field is represented by the development of dedicated softwares able to automatically overlap the 3D virtual models to the real anatomy, to perform augmented reality procedures.

CONCLUSIONS

The available Literature suggests a potentially crucial role of 3D virtual reconstructions during RAPN. Encouraging results concerning surgical planning and indication, intraoperative navigation, and surgical training are available. In the future, artificial intelligence may represent the key to further improve the 3D virtual modeling technology during RAPN.

Augmented Reality to Assist Skin Paddle Harvesting in Osteomyocutaneous Fibular Flap Reconstructive Surgery: A Pilot Evaluation on a 3D-Printed Leg Phantom

Background

Augmented Reality (AR) represents an evolution of navigation-assisted surgery, providing surgeons with a virtual aid contextually merged with the real surgical field. We recently reported a case series of AR-assisted fibular flap harvesting for mandibular reconstruction. However, the registration accuracy between the real and the virtual content needs to be systematically evaluated before widely promoting this tool in clinical practice. In this paper, after description of the AR based protocol implemented for both tablet and HoloLens 2 smart glasses, we evaluated in a first test session the achievable registration accuracy with the two display solutions, and in a second test session the success rate in executing the AR-guided skin paddle incision task on a 3D printed leg phantom.

Methods

From a real computed tomography dataset, 3D virtual models of a human leg, including fibula, arteries and skin with planned paddle profile for harvesting, were obtained. All virtual models were imported into Unity software to develop a marker-less AR application suitable to be used both via tablet and via HoloLens 2 headset. The registration accuracy for both solutions was verified on a 3D printed leg phantom obtained from the virtual models, by repeatedly applying the tracking function and computing pose deviations between the AR-projected virtual skin paddle profile and the real one transferred to the phantom via a CAD/CAM cutting guide. The success rate in completing the AR-guided task of skin paddle harvesting was evaluated using CAD/CAM templates positioned on the phantom model surface.

Results

On average, the marker-less AR protocol showed comparable registration errors (ranging within 1-5 mm) for tablet-based and HoloLens-based solution. Registration accuracy seems to be quite sensitive to ambient light conditions. We found a good success rate in completing the AR-guided task within an error margin of 4 mm (97% and 100% for tablet and HoloLens, respectively). All subjects reported greater usability and ergonomics for HoloLens 2 solution.

Conclusions

Results revealed that the proposed marker-less AR based protocol may guarantee a registration error within 1-5 mm for assisting skin paddle harvesting in the clinical setting. Optimal lightening conditions and further improvement of marker-less tracking technologies have the potential to increase the efficiency and precision of this AR-assisted reconstructive surgery.

AEducaAR, Anatomical Education in Augmented Reality: A Pilot Experience of an Innovative Educational Tool Combining AR Technology and 3D Printing

Gross anatomy knowledge is an essential element for medical students in their education, and nowadays, cadaver-based instruction represents the main instructional tool able to provide three-dimensional (3D) and topographical comprehensions. The aim of the study was to develop and test a prototype of an innovative tool for medical education in human anatomy based on the combination of augmented reality (AR) technology and a tangible 3D printed model that can be explored and manipulated by trainees, thus favoring a three-dimensional and topographical learning approach. After development of the tool, called AEducaAR (Anatomical Education with Augmented Reality), it was tested and evaluated by 62 second-year degree medical students attending the human anatomy course at the International School of Medicine and Surgery of the University of Bologna. Students were divided into two groups: AEducaAR-based learning ("AEducaAR group") was compared to standard learning using human anatomy atlas ("Control group"). Both groups performed an objective test and an anonymous questionnaire. In the objective test, the results showed no significant difference between the two learning methods; instead, in the questionnaire, students showed enthusiasm and interest for the new tool and highlighted its training potentiality in open-ended comments. Therefore, the presented AEducaAR tool, once implemented, may contribute to enhancing students' motivation for learning, increasing long-term memory retention and 3D comprehension of anatomical structures. Moreover, this new tool might help medical students to approach to innovative medical devices and technologies useful in their future careers.

Percutaneous ablation or minimally invasive partial nephrectomy for cT1a renal masses? A propensity score-matched analysis

OBJECTIVE

Local tumor ablation to treat small renal mass is increasing. The aim of the present study was to compare oncologic outcomes among patients with T1 renal mass treated with partial nephrectomy and local tumor ablation.

METHODS

To reduce the inherent differences between patients undergoing laparoscopic or robot-assisted partial nephrectomy (n = 405) and local tumor ablation (n = 137), we used a 1:1 propensity score-matched analysis. Local tumor ablation consisted of radiofrequency ablation and cryoablation. Disease-free survival, overall survival and other causes mortality-free survival rates were estimated using the Kaplan-Meier method. Multivariable logistic regression and competing-risk regression models were used to identify predictors of complications, recurrence and other causes mortality, respectively.

RESULTS

Partial nephrectomy had higher disease-free survival estimates, as compared with local tumor ablation (92.8% vs 80.4% at 5 years, P = 0.02), with no significant difference between radiofrequency ablation and cryoablation (P = 0.9). Ablation showed comparable overall survival estimates to partial nephrectomy (91% vs 95.8% at 5 years, P = 0.6). The 5-year recurrence rates were 7.9% versus 23.8% for patients aged ≤70 years, and 2.5% versus 11.9% for patients aged >70 years treated with partial nephrectomy and ablation, respectively; the 5-year other causes mortality rates were 0% and 2.2% for patients treated with partial nephrectomy and ablation aged ≤70 years, and 3% versus 10.9% for patients aged >70 years treated with partial nephrectomy and ablation, respectively. At multivariable analysis, ablation was associated with fewer complications (odds ratio 0.41; P = 0.01). At competing risks analysis, age (hazard ratio 0.96) and ablation (hazard ratio 4.56) were independent predictors of disease recurrence (all P ≤ 0.008).

CONCLUSIONS

Local tumor ablation showed a higher risk of recurrence and lower risk of complications compared with partial nephrectomy, with comparable overall survival rates.

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

Simple Summary

Prostate Cancer is one of the main threats to men’s health. Its accurate diagnosis is crucial to properly treat patients depending on the cancer’s level of aggressiveness. Tumor risk-stratification is still a challenging task due to the difficulties met during the reading of multi-parametric Magnetic Resonance Images. Artificial Intelligence models may help radiologists in staging the aggressiveness of the equivocal lesions, reducing inter-observer variability and evaluation time. However, these algorithms need many high-quality images to work efficiently, bringing up overfitting and lack of standardization and reproducibility as emerging issues to be addressed. This study attempts to illustrate the state of the art of current research of Artificial Intelligence methods to stratify prostate cancer for its clinical significance suggesting how widespread use of public databases could be a possible solution to these issues.

Abstract

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.

The Use of Augmented Reality to Guide the Intraoperative Frozen Section During Robot-assisted Radical Prostatectomy

BACKGROUND

Multiparametric magnetic resonance imaging (mpMRI) can guide the surgical plan during robot-assisted radical prostatectomy (RARP), and intraoperative frozen section (IFS) can facilitate real-time surgical margin assessment.

OBJECTIVE

To assess a novel technique of IFS targeted to the index lesion by using augmented reality three-dimensional (AR-3D) models in patients scheduled for nerve-sparing RARP (NS-RARP).

DESIGN, SETTING, AND PARTICIPANTS

Between March 2019 and July 2019, 20 consecutive prostate cancer patients underwent NS-RARP with IFS directed to the index lesion with the help of AR-3D models (study group). Control group consists of 20 patients matched with 1:1 propensity score for age, clinical stage, Prostate Imaging Reporting and Data System score v2, International Society of Urological Pathology grade, prostate volume, NS approach, and prostate-specific antigen in which RARP was performed by cognitive assessment of mpMRI.

SURGICAL PROCEDURE

In the study group, an AR-3D model was superimposed to the surgical field to guide the surgical dissection. Tissue sampling for IFS was taken in the area in which the index lesion was projected by AR-3D guidance.

MEASUREMENTS

Chi-square test, Student t test, and Mann-Whitney U test were used to compare, respectively, proportions, means, and medians between the two groups.

RESULTS AND LIMITATIONS

Patients in the AR-3D group had comparable preoperative characteristics and those undergoing the NS approach were referred to as the control group (all p ≥ 0.06). Overall, positive surgical margin (PSM) rates were comparable between the two groups; PSMs at the level of the index lesion were significantly lower in patients referred to AR-3D guided IFS to the index lesion (5%) than those in the control group (20%; p = 0.01).

CONCLUSIONS

The novel technique of AR-3D guidance for IFS analysis may allow for reducing PSMs at the level of the index lesion.

PATIENT SUMMARY

Augmented reality three-dimensional guidance for intraoperative frozen section analysis during robot-assisted radical prostatectomy facilitates the real-time assessment of surgical margins and may reduce positive surgical margins at the index lesion.