Interactive virtual 3D models of renal cancer patient anatomies alter partial nephrectomy surgical planning decisions and increase surgeon confidence compared to volume-rendered images

Assessment of resectability of pancreatic cancer using novel immersive high-performance virtual reality rendering of abdominal computed tomography and magnetic resonance imaging

PURPOSE

Virtual reality (VR) allows for an immersive and interactive analysis of imaging data such as computed tomography (CT) and magnetic resonance imaging (MRI). The aim of this study is to assess the comprehensibility of VR anatomy and its value in assessing resectability of pancreatic ductal adenocarcinoma (PDAC).

METHODS

This study assesses exposure to VR anatomy and evaluates the potential role of VR in assessing resectability of PDAC. Firstly, volumetric abdominal CT and MRI data were displayed in an immersive VR environment. Volunteering physicians were asked to identify anatomical landmarks in VR. In the second stage, experienced clinicians were asked to identify vascular involvement in a total of 12 CT and MRI scans displaying PDAC (2 resectable, 2 borderline resectable, and 2 locally advanced tumours per modality). Results were compared to 2D standard PACS viewing.

RESULTS

In VR visualisation of CT and MRI, the abdominal anatomical landmarks were recognised by all participants except the pancreas (30/34) in VR CT and the splenic (31/34) and common hepatic artery (18/34) in VR MRI, respectively. In VR CT, resectable, borderline resectable, and locally advanced PDAC were correctly identified in 22/24, 20/24 and 19/24 scans, respectively. Whereas, in VR MRI, resectable, borderline resectable, and locally advanced PDAC were correctly identified in 19/24, 19/24 and 21/24 scans, respectively. Interobserver agreement as measured by Fleiss κ was 0.7 for CT and 0.4 for MRI, respectively (p < 0.001). Scans were significantly assessed more accurately in VR CT than standard 2D PACS CT, with a median of 5.5 (IQR 4.75-6) and a median of 3 (IQR 2-3) correctly assessed out of 6 scans (p < 0.001).

CONCLUSION

VR enhanced visualisation of abdominal CT and MRI scan data provides intuitive handling and understanding of anatomy and might allow for more accurate staging of PDAC and could thus become a valuable adjunct in PDAC resectability assessment in the future.

Tubular structures segmentation of pediatric abdominal-visceral ceCT images with renal tumors: Assessment, comparison and improvement

Renal tubular structures, such as ureters, arteries and veins, are very important for building a complete digital 3D anatomical model of a patient. However, they can be challenging to segment from ceCT images due to their elongated shape, diameter variation and intra- and inter-patient contrast heterogeneity. This task is even more difficult in pediatric and pathological subjects, due to high inter-subject anatomical variations, potential presence of tumors, small volume of these structures compared to the surrounding, and small available labeled datasets. Given the limited literature on methods dedicated to children, and in order to find inspirational approaches, a complete assessment of state-of-the-art methods for the segmentation of renal tubular structures on ceCT images on adults is presented. Then, these methods are tested and compared on a private pediatric and pathological dataset of 79 abdominal-visceral ceCT images with arteriovenous phase acquisitions. To the best of our knowledge, both assessment and comparison in this specific case are novel. Eventually, we also propose a new loss function which leverages for the first time the use of vesselness functions on the predicted segmentation. We show that the combination of this loss function with state-of-the-art methods improves the topological coherence of the segmented tubular structures.2.

Editorial by Reza Mehrazin and Shirin Razdan on p. 388-389 of this issue: Can preoperative planning using IRIS™ three-dimensional anatomical virtual models predict operative findings during robot-assisted partial nephrectomy?

Objective

To evaluate the predictive validity of IRIS™ (Intuitive Surgical®, Sunnyvale, CA, USA) as a planning tool for robot-assisted partial nephrectomy (RAPN) by assessing the degree of overlap with intraoperative execution.

Methods

Thirty-one patients scheduled for RAPN by four experienced urologists were enrolled in a prospective study. Prior to surgery, urologists reviewed the IRIS™ three-dimensional model on an iphone Operating System (iOS) app and completed a questionnaire outlining their surgical plan including surgical approach, and ischemia technique as well as confidence in executing this plan. Postoperatively, questionnaires assessing the procedural approach, clinical utility, efficiency, and effectiveness of IRIS™ were completed. The degree of overlap between the preoperative and intraoperative questionnaires and between the planned approach and actual execution of the procedure was analyzed. Questionnaires were answered on a 5-point Likert scale and scores of 4 or greater were considered positive.

Results

Mean age was 65.1 years with a mean tumor size of 27.7 mm (interquartile range 17.5-44.0 mm). Hilar tumors consisted of 32.3%; 48.4% of patients had R.E.N.A.L. nephrometry scores of 7-9. On preoperative questionnaires, the surgeons reported that in 67.7% cases they were confident that they can perform the procedure successfully, and on intraoperative questionnaires, the surgeons reported that in 96.8% cases IRIS™ helped achieve good spatial sensation of the anatomy. There was a high degree of overlap between preoperative and intraoperative questionnaires for the surgical approach, interpreting anatomical details and clinical utility. When comparing plans for selective or off-clamp, the preoperative plan was executed in 90.0% of cases intraoperatively.

Conclusion

A high degree of overlap between the preoperative surgical approach and intraoperative RAPN execution was found using IRIS™. This is the first study to evaluate the predictive accuracy of IRIS™ during RAPN by comparing preoperative plan and intraoperative execution.

Implications of Tumor Surgery in Horseshoe Kidneys: A Comparative Study of Outcomes and Surgical Practices

OBJECTIVE

To contrast surgical outcomes of Horseshoe Kidney (HSK) patients with localized renal masses suspected of cancer with nonfused nonectopic kidney patients, emphasizing safe surgical practices for HSKs.

METHODS

The study examined solid tumors from the Mayo Clinic Nephrectomy registry between 1971 and 2021. Each HSK case was matched to three non-HSK patients based on various factors. The outcomes measured included complications within 30days of surgery, change in estimated glomerular filtration rate, and overall, cancer-specific, and metastasis-free survival rates.

RESULTS

Thirty of the 34 HSKs had malignant tumors compared with 90 of the 102 patients in the nonfused nonectopic referent cohort. Accessory isthmus arteries were present in 93% of HSK cases, with 43% exhibiting multiple arteries and 7% with 6 or more arteries. Estimated blood loss and surgery duration were significantly higher in HSKs (900 vs 300 mL, P = .004; 246 vs 163 minutes, P < .001, respectively). The HSK group demonstrated an overall complication rate of 26% (vs 17% in referents, P = .2) and a median change in estimated glomerular filtration rate at 3months of - 8.5 (vs -8.1 in referents, P = .8). At 5-year follow-up, survival rates for HSK patients were 72%, 91%, and 69% for overall, cancer-specific, and metastasis-free survival, respectively. The corresponding rates were 79%, 86%, and 77%, respectively, for matched referent patients (P > .05).

CONCLUSION

HSK tumor management is technically challenging with higher blood loss; however, the data demonstrate comparable outcomes for patients with HSK tumors, including complications and survival, to those without HSKs in experienced centers.

A three-dimensional renal tumor anatomy and intrarenal relationship nephrometry (ADDD) for robot-assisted partial nephrectomy : 3D-CT based nephrometry for RAPN

OBJECTIVE

To develop a 3D scoring system of tumor anatomy and intrarenal relationship for assessing surgical complexity and outcomes of robot-assisted partial nephrectomy (RAPN).

METHODS

We prospectively enrolled patients with a renal tumor who had a 3D model and underwent RAPN between Mar 2019 and Mar 2022. The ADDD nephrometry consisted of the contact surface area between tumor and parenchyma (A), the depth of tumor invasion into the renal parenchyma (D1), the distance from tumor to the main intrarenal artery (D2), and to the collecting system (D3). The primary outcomes included perioperative complication rate and trifecta outcome (WIT ≤ 25 min, negative surgical margins, and no major complications).

RESULTS

We enrolled a total of 301 patients. The mean tumor size was 2.93 ± 1.44 cm. There were 104 (34.6%) patients, 119 (39.5%) patients, and 78 (25.9%) patients in the low-, intermediate-, and high-risk groups, respectively. Each point increase in the ADDD score increased the risk of complications [hazard ratio (HR) 1.501]. A lower grade indicated a lower risk of failed trifecta (HR low group 15.103, intermediate group 9.258) and renal function damage (HR low risk 8.320, intermediate risk 3.165) compared to the high-risk group. The AUC of ADDD score and grade were 0.738 and 0.645 for predicting major complications, 0.766 and 0.714 for predicting trifecta outcome, and 0.746 and 0.730 for predicting postoperative renal function reservation.

CONCLUSION

The 3D-ADDD scoring system shows the tumor anatomy and its intraparenchymal relationships and has better efficacy in predicting surgical outcomes of RAPN.

Impact of 3D printed models on quantitative surgical outcomes for patients undergoing robotic-assisted radical prostatectomy: a cohort study

BACKGROUND

Three-dimensional (3D) printed anatomic models can facilitate presurgical planning by providing surgeons with detailed knowledge of the exact location of pertinent anatomical structures. Although 3D printed anatomic models have been shown to be useful for pre-operative planning, few studies have demonstrated how these models can influence quantitative surgical metrics.

OBJECTIVE

To prospectively assess whether patient-specific 3D printed prostate cancer models can improve quantitative surgical metrics in patients undergoing robotic-assisted radical prostatectomy (RARP).

METHODS

Patients with MRI-visible prostate cancer (PI-RADS V2 ≥ 3) scheduled to undergo RARP were prospectively enrolled in our IRB approved study (n = 82). Quantitative surgical metrics included the rate of positive surgical margins (PSMs), operative times, and blood loss. A qualitative Likert scale survey to assess understanding of anatomy and confidence regarding surgical approach was also implemented.

RESULTS

The rate of PSMs was lower for the 3D printed model group (8.11%) compared to that with imaging only (28.6%), p = 0.128. The 3D printed model group had a 9-min reduction in operating time (213 ± 42 min vs. 222 ± 47 min) and a 5 mL reduction in average blood loss (227 ± 148 mL vs. 232 ± 114 mL). Surgeon anatomical understanding and confidence improved after reviewing the 3D printed models (3.60 ± 0.74 to 4.20 ± 0.56, p = 0.62 and 3.86 ± 0.53 to 4.20 ± 0.56, p = 0.22).

CONCLUSIONS

3D printed prostate cancer models can positively impact quantitative patient outcomes such as PSMs, operative times, and blood loss in patients undergoing RARP.

Side-firing intraoperative ultrasound applied to resection of pituitary macroadenomas and giant adenomas: A single-center retrospective case-control study

Introduction

Multiple intraoperative navigation and imaging modalities are currently available as an adjunct to endoscopic transsphenoidal resection of pituitary adenomas, including intraoperative CT and MRI, fluorescence guidance, and neuronavigation. However, these imaging techniques have several limitations, including intraoperative tissue shift, lack of availability in some centers, and the increased cost and time associated with their use. The side-firing intraoperative ultrasound (IOUS) probe is a relatively new technology in endoscopic endonasal surgery that may help overcome these obstacles.

Methods

A retrospective analysis was performed on patients admitted for resection of pituitary adenomas by a single surgeon at the University of Mississippi Medical Center. The control (non-ultrasound) group consisted of twelve (n=12) patients who received surgery without IOUS guidance, and the IOUS group was composed of fifteen (n=15) patients who underwent IOUS-guided surgery. Outcome measures used to assess the side-firing IOUS were the extent of tumor resection, postoperative complications, length of hospital stay (LOS) in days, operative time, and self-reported surgeon confidence in estimating the extent of resection intraoperatively.

Results

Preoperative data analysis showed no significant differences in patient demographics or presenting symptoms between the two groups. Postoperative data revealed no significant difference in the rate of gross total resection between the groups (p = 0.716). Compared to the non-US group, surgeon confidence was significantly higher (p &lt; 0.001), and operative time was significantly lower for the US group in univariate analysis (p = 0.011). Multivariate analysis accounting for tumor size, surgeon confidence, and operative time confirmed these findings. Interestingly, we noted a trend for a lower incidence of postoperative diabetes insipidus in the US group, although this did not quite reach our threshold for statistical significance.

Conclusion

Incorporating IOUS as an aid for endonasal resection of pituitary adenomas provides real-time image guidance that increases surgeon confidence in intraoperative assessment of the extent of resection and decreases operative time without posing additional risk to the patient. Additionally, we identified a trend for reduced diabetes insipidus with IOUS.

Tumorous kidney segmentation in abdominal CT images using active contour and 3D-UNet

BACKGROUND AND PURPOSE

The precise segmentation of the kidneys in computed tomography (CT) images is vital in urology for diagnosis, treatment, and surgical planning. Medical experts can get assistance through segmentation, as it provides information about kidney malformations in terms of shape and size. Manual segmentation is slow, tedious, and not reproducible. An automatic computer-aided system is a solution to this problem. This paper presents an automated kidney segmentation technique based on active contour and deep learning.

MATERIALS AND METHODS

In this work, 210 CTs from the KiTS 19 repository were used. The used dataset was divided into a train set (168 CTs), test set (21 CTs), and validation set (21 CTs). The suggested technique has broadly four phases: (1) extraction of kidney regions using active contours, (2) preprocessing, (3) kidney segmentation using 3D U-Net, and (4) reconstruction of the segmented CT images.

RESULTS

The proposed segmentation method has received the Dice score of 97.62%, Jaccard index of 95.74%, average sensitivity of 98.28%, specificity of 99.95%, and accuracy of 99.93% over the validation dataset.

CONCLUSION

The proposed method can efficiently solve the problem of tumorous kidney segmentation in CT images by using active contour and deep learning. The active contour was used to select kidney regions and 3D-UNet was used for precisely segmenting the tumorous kidney.

Clinical experience of using virtual 3D modelling for pre and intraoperative guidance during robotic-assisted partial nephrectomy

Objective:

Surgical planning for robotic-assisted partial nephrectomy is widely performed using two-dimensional computed tomography images. It is unclear to what extent two-dimensional images fully simulate surgical anatomy and case complexity. To overcome these limitations, software has been developed to reconstruct three-dimensional models from computed tomography data. We present the results of a feasibility study, to explore the role and practicality of virtual three-dimensional modelling (by Innersight Labs) in the context of surgical utility for preoperative and intraoperative use, as well as improving patient involvement.

Methods:

A prospective study was conducted on patients undergoing robotic-assisted partial nephrectomy at our high volume kidney cancer centre. Approval from a research ethics committee was obtained. Patient demographics and tumour characteristics were collected. Surgical outcome measures were recorded. The value of the three-dimensional model to the surgeon and patient was assessed using a survey. The prospective cohort was compared against a retrospective cohort and cases were individually matched using RENAL (radius, exophytic/endophytic, nearness to collecting system or sinus, anterior/posterior, location relative to polar lines) scores.

Results:

This study included 22 patients. Three-dimensional modelling was found to be safe for this prospective cohort and resulted in good surgical outcome measures. The mean (standard deviation) console time was 158.6 (35) min and warm ischaemia time was 17.3 (6.3) min. The median (interquartile range) estimated blood loss was 125 (50–237.5) ml. Two procedures were converted to radical nephrectomy due to the risk of positive margins during resection. The median (interquartile range) length of stay was 2 (2–3) days. No postoperative complications were noted and all patients had negative surgical margins. Patients reported improved understanding of their procedure using the three-dimensional model.

Conclusion:

This study shows the potential benefit of three-dimensional modelling technology with positive uptake from surgeons and patients. Benefits are improved perception of vascular anatomy and resection approach, and procedure understanding by patients. A randomised controlled trial is needed to evaluate the technology further.

Level of evidence:

2b

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

Objective

Methods

Results

Conclusion

The impact of three-dimensional reconstruction and standardised CT interpretation (AMIGO) on the anatomical understanding of mesenteric vascular anatomy for planning complete mesocolic excision surgery: A randomised crossover study

BACKGROUND

Preoperative planning is a crucial aspect of safe complete mesocolic excision (CME) surgery. 3D models derived from imaging may help improve anatomical understanding of the complex vascular anatomy. Here, we assessed the effect of 3D models on surgeons' anatomical understanding in comparison to a systematic approach for CT scan interpretation (AMIGO).

METHOD

Fifteen cases were included in the study. Two GI radiology consultants reviewed each scan to ascertain the vascular anatomy. Virtual 3D models were produced and displayed on a web-based platform (https://skfb.ly/6OZUZ). A total of 13 surgical trainees were recruited. Candidates were assessed after baseline anatomical training and subsequently using the AMIGO method and 3D models. Five cases were randomly allocated in each round of testing for each participant. The primary outcome measure was an objective vascular anatomy knowledge score. The secondary outcome measure was subjective feedback from participants.

RESULTS

Both 3D and AMIGO significantly improved anatomical understanding in comparison to baseline testing. However, 3D was superior to AMIGO (3D [n = 65; median score 8/14] vs. AMIGO [n = 65; median score 6/14; p < 0.0001]. For 13/15 patient cases examined, 3D was superior to the AMIGO method. Eleven participants demonstrated better anatomical understanding using 3D models versus AMIGO. Ten participants preferred 3D models in comparison to standard CT imaging.

CONCLUSIONS

3D models improve anatomical understanding of mesenteric vascular anatomy in a group of colorectal surgical trainees in comparison to a formal CT interpretation method. 3D models may be a useful planning adjunct to 2D imaging for CME surgery.

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.

Improving accuracy, reliability, and efficiency of the RENAL nephrometry score with 3D reconstructed virtual imaging

OBJECTIVE

To clarify the diagnostic performance of the three-dimensional reconstructed virtual image (3D-RVI) in evaluating RENAL nephrometry score (RENAL-NS).

METHODS

This study included 130 patients who underwent preoperative contrast-enhanced computed tomography (CECT) followed by partial nephrectomy for renal tumors suggestive of renal cell carcinoma. RENAL-NS was calculated prior to the surgery, and tumor resection was performed referring to the score. We retrospectively reviewed preoperative CECT images. We calculated the inter-observer variability of RENAL-NS using 3D-RVI versus two-dimensional (2D) imaging and compared the ability of RENAL-NS using 3D-RVI versus 2D imaging to predict the risk of opening of the urinary collecting system. We also compared the two modalities for the time required to evaluate RENAL-NS.

RESULTS

RENAL-NS evaluated using 3D-RVI showed a higher inter-observer agreement compared to 2D-imaging (r_s = 0.85 vs. r_s = 0.65). The "nearness to sinus" score was more strongly associated with the opening of the urinary collecting system when evaluated using 3D-RVI than 2D-imaging (AUC = 0.71 vs AUC = 0.57, p = 0.016). RENAL-NS using 2D-imaging required a significantly longer time compared to 3D-RVI (p = 0.036).

CONCLUSIONS

Using 3D-RVI improves the accuracy, reliability and efficiency of RENAL-NS evaluation in preoperative assessment and can play an important role in preoperative assessment and intraoperative navigation.

Interactive virtual 3D image reconstruction to assist renal surgery in patients with fusion anomalies of the kidney

Objective:

Renal fusion anomalies are rare and usually present as horseshoe kidneys or crossed fusion ectopia. The complex renal anatomy seen in patients with these anomalies can present a challenge. Pre-operative planning is therefore paramount in the surgical management of these cases. Herein we report the use of interactive virtual three-dimensional (3D) reconstruction to aid renal surgery in patients with fusion anomalies of the kidney.

Materials and Methods:

A total of seven cases were performed between May 2016 and October 2020. 3D reconstruction was rendered by Innersight Labs using pre-operative computed tomography (CT) scans.

Results:

Five patients had malignant disease and two patients had benign pathology. Robotic and open operations were performed in four and three patients, respectively.

Conclusion:

The use of 3D reconstruction in the cases reported in this series allowed for the identification of variations in renal vasculature, and this informed the choice of operative approach.

Oxford Centre for Evidence-Based Medicine Evidence Level:

4

Virtual reality of three-dimensional surgical field for surgical planning and intraoperative management

PURPOSE

To investigate the impact of virtual reality (VR) technologies on urological surgeries, specifically in the management of prostate cancer and renal cancer.

METHODS

A non-systematic review of the literature was performed. Medline, Pubmed, and the Cochrane Database were screened for studies regarding the use of VR technologies in the management of prostate and renal cancer.

RESULTS

In the management of prostate cancer, VR technologies have been increasingly applied for diagnosis with magnetic resonance imaging/ultrasound fusion biopsy, surgical training using a simulator, surgical navigation in robot-assisted radical prostatectomy, and targeted focal therapy. In partial nephrectomy, surgical simulation and intra-surgical guidance with three-dimensional VR have been used for better understanding of the hilar vascular information, tumor location, and positional relationships of the tumor-feeding vessel and pyelocaliceal system.

CONCLUSIONS

VR contributes to the education, training, and simulation of surgical procedures as well as helping the surgeons to tailor surgical planning on each patient. Further prospective studies are needed to assess the beneficial impacts of this technology for both the physician and patient by objective parameters.

Robotic-Assisted Partial Nephrectomy: Techniques to Improve Clinical Outcomes

PURPOSE OF REVIEW

To summarize current options available for robot-assisted partial nephrectomy RECENT FINDINGS: Partial nephrectomy (PN) is a standard treatment option for management of cT1 renal masses. It may be carried out by multiple approaches. Robot-assisted (RA) PN is one such option. The goal of treatment is both correct oncological (negative surgical margins) and functional (preservation of sufficient amount of renal parenchyma of the operated kidney) outcome. Appropriate outcomes depend on multiple factors. There are many, but among others tumor characteristics (size, location, i.e., tumor complexity), patient baseline renal function, patient comorbidities, and performance status etc. Based on all these, the surgeon adapts the intervention for each mass/patient by preoperative planning, absence/use/duration of warm or cold ischemia, perioperative imaging, resection technique adapted to tumor location and depth of invasion, use of hemostatics, type and degree of renal parenchymal closure and others details. Nephroprotective agents have not shown efficacy so far. It should not be forgotten that surgeon's experience plays a key role in the achievement of good results. Although multiple factors have a role in the RA partial nephrectomy, surgeon experience and adaptation of technique of intervention have the crucial role in the achievement of both functional and oncological results.

Application of IRIS Three-Dimensional Anatomical Models As Preoperative Surgical Planning Tools in the Management of Localized Renal Masses

Introduction: The use of volume-rendered images is gaining popularity in the surgical planning for complex procedures. IRIS™ is an interactive software that delivers three-dimensional (3D) virtual anatomical models. We aimed to evaluate the preoperative clinical utility of IRIS for patients with ≤T2 localized renal tumors who underwent either partial nephrectomy (PN) or radical nephrectomy (RN). Patients and Methods: Six urologists (four faculty and two trainees) reviewed CT scans of 40 cases over 2 study phases, using conventional two-dimensional (2D) CT alone (Phase-I), followed by the CT + IRIS 3D model (Phase-II). After each review, surgeons reported their decision on performing a PN or an RN and rated (Likert scale) their confidence in completing the procedure as well as how the imaging modality influenced specific procedural decisions. Modifications to the choice of procedure and confidence in decisions between both phases were compared for the same surgeon. Concordance between surgeons was also evaluated. Results: A total of 462 reviews were included in the analysis (231 in each phase). In 64% (95% CI: 58-70%) of reviews, surgeons reported that IRIS achieved a better spatial orientation, understanding of the anatomy, and offered additional information compared with 2D CT alone. IRIS impacted the planned procedure in 20% of the reviews (3.5% changed decision from PN to RN and 16.5% changed from RN to PN). In the remaining 80% of reviews, surgeons' confidence increased from 78% (95% CI: 72-84%) with 2D CT, to 87% (95% CI: 82-92%) with IRIS (p = 0.02); this confidence change was more pronounced in cases with a high RENAL score (p = 0.009). In 99% of the reviews, surgeons rated that the IRIS accurately represented the anatomical details of all kidney components. Conclusion: Application of IRIS 3D models could influence the surgical decision-making process and improve surgeons' confidence, especially for robot-assisted management of complex renal tumors.

Rapid Segmentation of Renal Tumours to Calculate Volume Using 3D Interpolation

Small renal masses are commonly diagnosed with modern medical imaging. Renal tumour volume has been explored as a prognostic tool to help decide when intervention is needed and appears to provide additional prognostic information for smaller tumours compared with tumour diameter. However, the current method of calculating tumour volume in clinical practice uses the ellipsoid equation (π/6 × length × width × height) which is an oversimplified approach. Some research groups trace the contour of the tumour in every image slice which is impractical for clinical use. In this study, we demonstrate a method of using 3D segmentation software and the 3D interpolation method to rapidly calculate renal tumour volume in under a minute. Using this method in 27 patients that underwent radical or partial nephrectomy, we found a 10.07% mean absolute difference compared with the traditional ellipsoid method. Our segmentation volume was closer to the calculated histopathological tumour volume than the traditional method (p = 0.03) with higher Lin's concordance correlation coefficient (0.79 vs 0.72). 3D segmentation has many uses related to 3D printing and modelling and is becoming increasingly common. Calculation of tumour volume is one additional benefit it provides. Further studies on the association between segmented tumour volume and prognosis are needed.

Personalised three-dimensional printed transparent kidney model for robot-assisted partial nephrectomy in patients with complex renal tumours (R.E.N.A.L. nephrometry score ≥7): a prospective case-matched study

OBJECTIVES

To evaluate the effectiveness of a three-dimensional (3D) printed transparent kidney model as a surgical navigator for robot-assisted partial nephrectomy (RPN) in patients with complex renal tumours, defined by a R.E.N.A.L. (Radius, Exophytic/Endophytic, Nearness, Anterior/Posterior, Location) nephrometry score of ≥7.

PATIENTS AND METHODS

A total of 80 patients who underwent RPN were included in the present prospective case-matched study (case group [n = 40, application of 3D-printed transparent kidney model during RPN] vs matching group [n = 40, routine protocol]). The RPNs were performed by a single experienced surgeon. The RPN procedure consisted of six steps: (i) preparation of the renal hilar vessel for clamping, (ii) tumour detection and dissection, (iii) robotic ultrasonography, (iv) tumour resection, (v) calyx repair and haemostasis, and (vi) renorrhaphy. The time for each step, console time, and warm ischaemia time were compared between the two groups as a surrogate marker for surgical effectiveness.

RESULTS

Both groups were well-balanced for all baseline characteristics. The use of the model reduced the console time by ~20% compared to the matched group (64.6 vs 78.5 min, P = 0.001). On multivariate logistic regression analysis, tumour radius (P < 0.001) and application of the model (P = 0.009) were identified as significant predictors of a console time of ≤70 min.

CONCLUSION

We established the usefulness of a personalised 3D-printed transparent kidney model for more effective RPNs. Use of the 3D-printed transparent kidney model reduced the operative time even for complex renal tumours and would be expected to broaden the indications for PN.

Virtual reality tumor navigated robotic radical prostatectomy by using three‐dimensional reconstructed multiparametric prostate MRI and 68 Ga‐PSMA PET/CT images: A useful tool to guide the robotic surgery?

Objectives

To evaluate the use and benefits of tumor navigation during performing robotic assisted radical prostatectomy (RARP).

Patients and Methods

Borders of the visible tumor(s) was/were and surrounding structures marked on multiparametric prostate magnetic resonance imaging (mpMRI) and ⁶⁸Ga‐labeled prostate‐specific membrane antigen ligand using positron emission computed tomography (Ga68 PSMA‐PET/CT). Three dimensional (3D) reconstruction of the images were done that were transferred to virtual reality (VR) headsets and Da Vinci surgical robot via TilePro. Images were used as a guide during RARP procedures in five cases. Indocyanine green (ICG) guided pelvic lymph node dissection (n = 2) and Martini Klinik Neurosafe technique (n = 2) were also applied.

Results

Mean patient age was 60.6 ± 3.7 years (range, 56‐66). All VR models were finalized with the agreement of radiologist, urologist, nuclear physician, and engineer. Surgeon examined images before the surgery. All VR models were found very useful particularly in pT3 diseases. Pathological stages included pT2N0 (n = 1), pT3aN0 (n = 1), pT3aN1 (n = 2), and pT3bN1 (n = 1). Positive surgical margins (SMs) occurred in two patients with extensive disease (pT3aN1 and pT3bN1) and tumor occupied 30% and 50% of the prostate volumes. Mean estimated blood loss was 150 ± 86.6 cc (range, 100‐300). Mean follow‐up was 3.4 ± 1.7 months (range, 2‐6). No complication occurred during perioperative (0‐30 days) and postoperative (30‐90 days) periods in any patient.

Conclusions

3D reconstructed VR models by using mpMRI and Ga68 PSMA‐PET/CT images can be accurately prepared and effectively applied during RARP that might be a useful tool for tumor navigation. Images show prostate tumors and anatomy and might be a guide for the console surgeon. This is promising new technology that needs further study and validation.

Three-dimensional virtual imaging of renal tumours: a new tool to improve the accuracy of nephrometry scores

OBJECTIVES

To apply the standard PADUA and RENAL nephrometry score variables to three-dimensional (3D) virtual models (VMs) produced from standard bi-dimensional imaging, thereby creating three-dimensional (3D)-based (PADUA and RENAL) nephrometry scores/categories for the reclassification of the surgical complexity of renal masses, and to compare the new 3D nephrometry score/category with the standard 2D-based nephrometry score/category, in order to evaluate their predictive role for postoperative complications.

MATERIALS AND METHODS

All patients with localized renal tumours scheduled for minimally invasive partial nephrectomy (PN) between September 2016 and September 2018 underwent 3D and 2D nephrometry score/category assessments preoperatively. After nephrometry score/category evaluation, all the patients underwent surgery. Chi-squared tests were used to evaluate the individual patients' grouping on the basis of the imaging tool (3D VMs and 2D imaging) used to assess the nephrometry score/category, while Cohen's κ coefficient was used to test the concordance between classifications. Receiver-operating characteristic curves were produced to evaluate the sensitivity and specificity of the 3D nephrometry score/category vs the 2D nephrometry score/category in predicting the occurrence of postoperative complications. A general linear model was used to perform multivariable analyses to identify predictors of overall and major postoperative complications.

RESULTS

A total of 101 patients were included in the study. The evaluation of PADUA and RENAL nephrometry scores via 3D VMs showed a downgrading in comparison with the same scores evaluated with 2D imaging in 48.5% and 52.4% of the cases. Similar results were obtained for nephrometry categories (29.7% and 30.7% for PADUA risk and RENAL complexity categories, respectively). The 3D nephrometry score/category demonstrated better accuracy than the 2D nephrometry score/category in predicting overall and major postoperative complications (differences in areas under the curve for each nephrometry score/category were statistically significant comparing the 3D VMs with 2D imaging assessment). Multivariable analyses confirmed 3D PADUA/RENAL nephrometry category as the only independent predictors of overall (P = 0.007; P = 0.003) and major postoperative complications (P = 0.03; P = 0.003).

CONCLUSIONS

In the present study, we showed that 3D VMs were more precise than 2D standard imaging in evaluating the surgical complexity of renal masses according to nephrometry score/category. This was attributable to a better perception of tumour depth and its relationships with intrarenal structures using the 3D VM, as confirmed by the higher accuracy of the 3D VM in predicting postoperative complications.