Prognostic significance of residual tumor at restaging transurethral bladder resection in high-risk non-muscle-invasive bladder cancer.

PURPOSE: To assess prognostic significance of residual tumor at repeat transurethral resection (reTUR) in contemporary non-muscle-invasive bladder cancer (NMIBC) patients. METHODS: Patients were identified retrospectively from eight referral centers in France, Italy and Spain. The cohort included consecutive patients with high or very-high risk NMIBC who underwent reTUR and subsequent adjuvant BCG therapy. RESULTS: A total of 440 high-risk NMIBC patients were screened, 29 (6%) were upstaged ≥ T2 at reTUR and 411 were analyzed (T1 stage: n = 275, 67%). Residual tumor was found in 191 cases (46%). In patients with T1 tumor on initial TURBT, persistent T1 tumor was found in 18% of reTUR (n = 49/275). In patients with high-grade Ta tumor on initial TURBT, T1 tumor was found in 6% of reTUR (n = 9/136). In multivariable logistic regression analysis, we found no statistical association between the use of photodynamic diagnosis (PDD, p = 0.4) or type of resection (conventional vs. en bloc, p = 0.6) and the risk of residual tumor. The estimated 5-yr recurrence and progression-free survival were 56% and 94%, respectively. Residual tumor was significantly associated with a higher risk of recurrence (p < 0.001) but not progression (p = 0.11). Only residual T1 tumor was associated with a higher risk of progression (p < 0.001) with an estimated 5-yr progression-free survival rate of 76%. CONCLUSIONS: ReTUR should remain a standard for T1 tumors, irrespective of the use of en bloc resection or PDD and could be safely omitted in high-grade Ta tumors. Persistent T1 tumor at reTUR should not exclude these patients from conservative management, and further studies are needed to explore the benefit of a third resection in this subgroup.

Urinary PSA-ZINC biomarker outperforms standard of care in early detection of prostate cancer.

BACKGROUND: Urine is a promising biological fluid for prostate cancer (PCa) diagnostics due to its non-invasive collection and wide range of biomarkers. The aim of this study was to assess the role of urinary PSA (uPSA) and urinary Zinc (uZinc) as biomarkers for the diagnosis of PCa in combination with routine parameters of standard of care (SOC - blood PSA, abnormal DRE, age) and MRI in patients candidates for prostate biopsy. METHODS: Urine samples after prostatic massages were collected from men with suspected PCa scheduled for prostate biopsy. Quantification of uPSA was performed by ECLIA platform and confirmed by ELISA assay, while uZinc measurement was evaluated by ICP-MS and confirmed by colorimetric in vitro assay. Six multivariate logistic regression analysis were performed to assess diagnostic performance of uPSA and uZinc (urine), SOC and MRI alone, and combination of MRI+SOC, MRI+urine and SOC+MRI+urine. The discriminative power of the logistic models was assessed by calculating the area under the receiver operating characteristic (ROC) curves (AUC). RESULTS: Two hundred thirty-eight patients were included in the analysis; 145 of them were diagnosed with PCa. Urine test showed a better discrimination of HS from CP, in respect of uPSA and uZinc alone, both for PCa of any grade and Gleason Score ≥7 (4+3) (AUC 0.804 and 0.823 respectively). ROC curve combining SOC+MRI+urine showed an AUC=0.882, that is statistically different from SOC or MRI alone, or MRI+SOC (P=0.0001, P=0.0001, and P=0.008 respectively). PCa risk algorithm designed considering SOC+MRI+urine results in potential reduction of 57% of unnecessary biopsies compared to the current standard parameters. CONCLUSIONS: The loss of uPSA and Zinc production and secretion during neoplastic transformation of the prostate could potentially represent a hallmark of PCa. Its combination with age, PSA and DRE, as well as with mpMRI could represent an interesting approach to improve the diagnostic accuracy of PCa.

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.

Development of Machine Learning Algorithm to Predict the Risk of Incontinence After Robot-Assisted Radical Prostatectomy.

Introduction: Predicting postoperative incontinence beforehand is crucial for intensified and personalized rehabilitation after robot-assisted radical prostatectomy. Although nomograms exist, their retrospective limitations highlight artificial intelligence (AI)'s potential. This study seeks to develop a machine learning algorithm using robot-assisted radical prostatectomy (RARP) data to predict postoperative incontinence, advancing personalized care. Materials and Methods: In this propsective observational study, patients with localized prostate cancer undergoing RARP between April 2022 and January 2023 were assessed. Preoperative variables included age, body mass index, prostate-specific antigen (PSA) levels, digital rectal examination (DRE) results, Gleason score, International Society of Urological Pathology grade, and continence and potency questionnaires responses. Intraoperative factors, postoperative outcomes, and pathological variables were recorded. Urinary continence was evaluated using the Expanded Prostate cancer Index Composite questionnaire, and machine learning models (XGBoost, Random Forest, Logistic Regression) were explored to predict incontinence risk. The chosen model's SHAP values elucidated variables impacting predictions. Results: A dataset of 227 patients undergoing RARP was considered for the study. Post-RARP complications were predominantly low grade, and urinary continence rates were 74.2%, 80.7%, and 91.4% at 7, 13, and 90 days after catheter removal, respectively. Employing machine learning, XGBoost proved the most effective in predicting postoperative incontinence risk. Significant variables identified by the algorithm included nerve-sparing approach, age, DRE, and total PSA. The model's threshold of 0.67 categorized patients into high or low risk, offering personalized predictions about the risk of incontinence after surgery. Conclusions: Predicting postoperative incontinence is crucial for tailoring rehabilitation after RARP. Machine learning algorithm, particularly XGBoost, can effectively identify those variables more heavily, impacting the outcome of postoperative continence, allowing to build an AI-driven model addressing the current challenges in post-RARP rehabilitation.

Robotic Vena Cava Thrombectomy with Three-dimensional Augmented Reality Guidance.

Robotic surgery has recently been used for treatment of renal cell carcinoma (RCC) and neoplastic thrombus located in the renal vein or inferior vena cava (IVC). Accurate identification of the thrombus location is crucial, and three-dimensional augmented reality (3D AR) may be valuable in achieving this. We enrolled patients with nonmetastatic RCC and level 0-I venous thrombus (Mayo Clinic classification) for robot-assisted radical nephrectomy and thrombectomy with 3D AR guidance. Five patients were prospectively enrolled; three had a level 0 thrombus and two had a level I thrombus. The mean operative time was 123 ± 15 min, mean IVC clamping time was 9.4 ± 6.8 min, and mean estimated blood loss was 750 ± 150 ml. The AR system allowed precise estimation of the thrombus location in all cases. No intraoperative complications or postoperative Clavien-Dindo grade >2 complications occurred. Use of 3D AR guidance allowed correct estimation of the limits of the thrombus and guided the surgeon in selecting an appropriate surgical strategy.

Oncologic surveillance intensity after endoscopic treatment of upper tract urothelial carcinoma.

BACKGROUND: The optimal oncologic surveillance in patients with upper tract urothelial carcinoma (UTUC) elected for conservative treatment is still a matter of debate. METHODS: Patients elected for endoscopic treatment of UTUC were followed up according to EAU guidelines recommendations after treatment. Bladder cancer recurrence-free survival (BCa-RFS), UTUC recurrence-free survival (UTUC-RFS), radical nephroureterectomy-free survival (RNU-FS), and cancer-specific survival (CSS) were estimated using the Kaplan-Meier method. The crude risks of BCa and UTUC recurrences over time were estimated with the Locally Weighted Scatterplot Smoothing method. RESULTS: Overall, 54 and 55 patients had low- and high-risk diseases, respectively. Median follow-up was 46.9 (IQR: 28.7-68.7) and 36.9 (IQR: 19.8-60.1) months in low and high-risk patients, respectively. In low-risk patients, BCa recurrence risk was more than 20% at 24 months follow-up. At 60 months, time point after which cystoscopy and imaging should be interrupted, the risk of BCa recurrence and UTUC recurrence were 14% and 7%, respectively. In high-risk patients, the risk of BCa and UTUC recurrence at 36 months was approximately 40% and 10%, respectively. Conversely, at 60 months, the risk of bladder recurrence and UTUC recurrence was 28% and 8%, respectively. CONCLUSIONS: For low-risk patients, cystoscopy should be performed semi-annually until 24 months, while upper tract assessment should be obtained up to 60 months, as per current EAU guidelines recommendations. For high-risk patients, upper tract assessment should be intensified to semi-annually up to 36 months, then obtained yearly. Conversely, cystoscopy should be ideally performed semi-annually until 60 months and yearly thereafter.

Computer Vision and Machine-Learning Techniques for Automatic 3D Virtual Images Overlapping During Augmented Reality Guided Robotic Partial Nephrectomy.

OBJECTIVES: The research's purpose is to develop a software that automatically integrates and overlay 3D virtual models of kidneys harboring renal masses into the Da Vinci robotic console, assisting surgeon during the intervention. INTRODUCTION: Precision medicine, especially in the field of minimally-invasive partial nephrectomy, aims to use 3D virtual models as a guidance for augmented reality robotic procedures. However, the co-registration process of the virtual images over the real operative field is performed manually. METHODS: In this prospective study, two strategies for the automatic overlapping of the model over the real kidney were explored: the computer vision technology, leveraging the super-enhancement of the kidney allowed by the intraoperative injection of Indocyanine green for superimposition and the convolutional neural network technology, based on the processing of live images from the endoscope, after a training of the software on frames from prerecorded videos of the same surgery. The work-team, comprising a bioengineer, a software-developer and a surgeon, collaborated to create hyper-accuracy 3D models for automatic 3D-AR-guided RAPN. For each patient, demographic and clinical data were collected. RESULTS: Two groups (group A for the first technology with 12 patients and group B for the second technology with 8 patients) were defined. They showed comparable preoperative and post-operative characteristics. Concerning the first technology the average co-registration time was 7 (3-11) seconds while in the case of the second technology 11 (6-13) seconds. No major intraoperative or postoperative complications were recorded. There were no differences in terms of functional outcomes between the groups at every time-point considered. CONCLUSION: The first technology allowed a successful anchoring of the 3D model to the kidney, despite minimal manual refinements. The second technology improved kidney automatic detection without relying on indocyanine injection, resulting in better organ boundaries identification during tests. Further studies are needed to confirm this preliminary evidence.

Evaluation of predictive factors for i-CLARAS (intraoperative complications in laparoscopic renal and adrenal surgery): a multicentre international retrospective cohort study.

The laparoscopic approach represents the standard of treatment for renal and adrenal diseases, and its use is increasing even outside referral centres. Although most procedures are routinely performed, intraoperative complications do not occur, and the rate and predictive factors of these complications have not been established. The aim of this study was to evaluate the incidence and type of intraoperative complications and to identify predictive factors in patients undergoing laparoscopic renal and adrenal surgery. This was a cohort, multicentre, international retrospective study. Patients who underwent laparoscopic renal and adrenal surgeries between April 2017 and March 2022 were included in the study. Bivariate analysis was performed using contingency tables and the χ2 test for independent samples to compare qualitative variables and the T test and Mood test for continuous variables. Multivariate analysis was performed using a logistic regression model to obtain adjusted odds ratios. A total of 2374 patients were included in the study. Intraoperative complications were reported for 8.09% of patients who underwent renal surgery, with the most common complications reported being hollow viscus and vascular complications, and for 6.75% of patients who underwent adrenal surgery, with the most common complication reported being parenchymatous viscous complications. Multivariate analysis revealed that both adrenal and renal surgery radiological preoperative factors, such as invasive features during adrenalectomy and the RENAL score during nephrectomy, are predictive factors of intraoperative complications. In contrast to existing data, surgeon experience was not associated with a reduction in the incidence of perioperative complications.

Metaverse Surgical Planning with Three-dimensional Virtual Models for Minimally Invasive Partial Nephrectomy.

The recent integration of new virtual visualization modalities with artificial intelligence and high-speed internet connection has opened the door to the advent of the metaverse in medicine. In this totally virtual environment, three-dimensional virtual models (3DVMs) of the patient's anatomy can be visualized and discussed via digital avatars. Here we present for the first time a metaverse preoperative clinical case discussion before minimally invasive partial nephrectomy. The surgeons' digital avatars met in a virtual room and participated in a virtual consultation on the surgical strategy and clamping approach before the procedure. Robotic or laparoscopic procedures are then carried out according to the simulated surgical strategy. We demonstrate how this immersive virtual reality experience overcomes the barriers of distance and how the quality of surgical planning is enriched by a great sense of "being there", even if virtually. Further investigation will improve the quality of interaction with the models and among the avatars.

The Role of 99mTc-Sestamibi Single-photon Emission Computed Tomography/Computed Tomography in the Diagnostic Pathway for Renal Masses: A Systematic Review and Meta-analysis.

CONTEXT: The diagnostic accuracy of current imaging techniques in differentiating benign from malignant neoplasms in the case of indeterminate renal masses is still suboptimal. OBJECTIVE: To evaluate the diagnostic accuracy of 99mTc-sestamibi (SestaMIBI) single-photon emission tomography computed tomography (SPECT)/CT in characterizing indeterminate renal masses by differentiating renal oncocytoma and hybrid oncocytic/chromophobe tumor (HOCT) from (1) all other renal lesions and (2) all malignant renal lesions. Secondary outcomes were: (1) benign versus malignant; (2) renal oncocytoma and HOCT versus clear cell (ccRCC) and papillary (pRCC) renal cell carcinoma; and (3) renal oncocytoma and HOCT versus chromophobe renal cell carcinoma (chRCC). EVIDENCE ACQUISITION: A literature search was conducted up to November 2022 using the PubMed/MEDLINE, Embase, and Web of Science databases. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to identify eligible studies. Studies included were prospective and retrospective cross-sectional studies in which SestaMIBI SPECT/CT findings were compared to histology after renal mass biopsy or surgery. EVIDENCE SYNTHESIS: Overall, eight studies involving 489 patients with 501 renal masses met our inclusion criteria. The sensitivity and specificity of SestaMIBI SPECT/CT for renal oncocytoma and HOCT versus all other renal lesions were 89% (95% confidence interval [CI] 70-97%) and 89% (95% CI 86-92%), respectively. Notably, for renal oncocytoma and HOCT versus ccRCC and pRCC, SestaMIBI SPECT/CT showed specificity of 98% (95% CI 91-100%) and similar sensitivity. Owing to the relatively high risk of bias and the presence of heterogeneity among the studies included, the level of evidence is still low. CONCLUSIONS: SestaMIBI SPECT/CT has good sensitivity and specificity in differentiating renal oncocytoma and HOCT from all other renal lesions, and in particular from those with more aggressive oncological behavior. Although these results are promising, further studies are needed to support the use of SestaMIBI SPECT/CT outside research trials. PATIENT SUMMARY: A scan method called SestaMIBI SPECT/CT has promise for diagnosing whether kidney tumors are malignant or not. However, it should still be limited to research trials because the level of evidence from our review is low.

Development of Bleeding Artificial Intelligence Detector (BLAIR) System for Robotic Radical Prostatectomy.

BACKGROUND: Addressing intraoperative bleeding remains a significant challenge in the field of robotic surgery. This research endeavors to pioneer a groundbreaking solution utilizing convolutional neural networks (CNNs). The objective is to establish a system capable of forecasting instances of intraoperative bleeding during robot-assisted radical prostatectomy (RARP) and promptly notify the surgeon about bleeding risks. METHODS: To achieve this, a multi-task learning (MTL) CNN was introduced, leveraging a modified version of the U-Net architecture. The aim was to categorize video input as either "absence of blood accumulation" (0) or "presence of blood accumulation" (1). To facilitate seamless interaction with the neural networks, the Bleeding Artificial Intelligence-based Detector (BLAIR) software was created using the Python Keras API and built upon the PyQT framework. A subsequent clinical assessment of BLAIR's efficacy was performed, comparing its bleeding identification performance against that of a urologist. Various perioperative variables were also gathered. For optimal MTL-CNN training parameterization, a multi-task loss function was adopted to enhance the accuracy of event detection by taking advantage of surgical tools' semantic segmentation. Additionally, the Multiple Correspondence Analysis (MCA) approach was employed to assess software performance. RESULTS: The MTL-CNN demonstrated a remarkable event recognition accuracy of 90.63%. When evaluating BLAIR's predictive ability and its capacity to pre-warn surgeons of potential bleeding incidents, the density plot highlighted a striking similarity between BLAIR and human assessments. In fact, BLAIR exhibited a faster response. Notably, the MCA analysis revealed no discernible distinction between the software and human performance in accurately identifying instances of bleeding. CONCLUSION: The BLAIR software proved its competence by achieving over 90% accuracy in predicting bleeding events during RARP. This accomplishment underscores the potential of AI to assist surgeons during interventions. This study exemplifies the positive impact AI applications can have on surgical procedures.

Robot-assisted radical cystectomy and ileal conduit with Hugo™ RAS system: feasibility, setting and perioperative outcomes

ABSTRACT Introduction: Robotic approach has shown its feasibility and safety with respect to open approach for radical cystectomy (1). The performances of Hugo™ RAS system (Medtronic, Minneapolis, USA) have been demonstrated in several clinical scenarios (2-5). We report the feasibility and surgical settings of the first series of robot-assisted radical cystectomy (RARC) with intracorporeal ileal-conduit performed with Hugo™ RAS system. Methods: Two patients were submitted to RARC with ileal conduit at our institution. The trocar placement scheme and the operating room setting with docking angles of the four arms were already described (6). A 12-mm and a 5-mm trocar for the assistant were placed. In both cases, an ileal-conduit with a Wallace type-1 uretero-enteric derivation was performed intra-corporeally. Results: The first patient was a 71-year-old male with a very-high risk non-muscle invasive bladder cancer(BC), and the second patient was a 64-year-old male with a diagnosis of T2 high-grade BC. Operative times were 360 and 420 minutes with a docking time of 12 and 9 minutes, respectively. No intraoperative complications occurred. The estimated blood loss was 200ml and 400ml, respectively. The second patient developed an ileus on postoperative day 4 (Clavien-Dindo grade 2). No positive surgical margins were recorded. No recurrence nor progression occurred during follow-up. Conclusion: RARC with intracorporeal ileal conduit urinary diversion is feasible with Hugo™ RAS system. We provided insight into the surgical setting using this novel robotic platform to help new adopters to face this challenging procedure. These findings may help a wider distribution of robotic programs for BC treatment.

3D Model Artificial Intelligence-Guided Automatic Augmented Reality Images during Robotic Partial Nephrectomy.

More than ever, precision surgery is making its way into modern surgery for functional organ preservation. This is possible mainly due to the increasing number of technologies available, including 3D models, virtual reality, augmented reality, and artificial intelligence. Intraoperative surgical navigation represents an interesting application of these technologies, allowing to understand in detail the surgical anatomy, planning a patient-tailored approach. Automatic superimposition comes into this context to optimally perform surgery as accurately as possible. Through a dedicated software (the first version) called iKidney, it is possible to superimpose the images using 3D models and live endoscopic images during partial nephrectomy, targeting the renal mass only. The patient is 31 years old with a 28 mm totally endophytic right-sided renal mass, with a PADUA score of 9. Thanks to the automatic superimposition and selective clamping, an enucleoresection of the renal mass alone was performed with no major postoperative complication (i.e., Clavien-Dindo < 2). iKidney-guided partial nephrectomy is safe, feasible, and yields excellent results in terms of organ preservation and functional outcomes. Further validation studies are needed to improve the prototype software, particularly to improve the rotational axes and avoid human help. Furthermore, it is important to reduce the costs associated with these technologies to increase its use in smaller hospitals.

Robot-assisted oncologic pelvic surgery with Hugo™ robot-assisted surgery system: A single-center experience.

Objective: To report the outcomes of intra- and extra-peritoneal robot-assisted radical prostatectomy (RARP) and robot-assisted radical cystectomy (RARC) with Hugo™ robot-assisted surgery (RAS) system (Medtronic, Minneapolis, MN, USA). Methods: Data of twenty patients who underwent RARP and one RARC at our institution between February 2022 and January 2023 were reported. The primary endpoint of the study was to report the surgical setting of Hugo™ RAS system to perform RARP and RARC. The secondary endpoint was to assess the feasibility of RARP and RARC with this novel robotic platform and report the outcomes. Results: Seventeen patients underwent RARP with a transperitoneal approach, and three with an extraperitoneal approach; and one patient underwent RARC with intracorporeal ileal conduit. No intraoperative complications occurred. Median docking and console time were 12 (interquartile range [IQR] 7-16) min and 185 (IQR 177-192) min for transperitoneal RARP, 15 (IQR 12-17) min and 170 (IQR 162-185) min for extraperitoneal RARP. No intraoperative complications occurred. One patient submitted to extraperitoneal RARP had a urinary tract infection in the postoperative period that required an antibiotic treatment (Clavien-Dindo Grade 2). In case of transperitoneal RARP, two minor complications occurred (one pelvic hematoma and one urinary tract infection; both Clavien-Dindo Grade 2). Conclusion: Hugo™ RAS system is a novel promising robotic platform that allows to perform major oncological pelvic surgery. We showed the feasibility of RARP both intra- and extra-peritoneally and RARC with intracorporeal ileal conduit with this novel platform.

The importance of second-look ureteroscopy implementation in the conservative management of upper tract urothelial carcinoma.

PURPOSE: The purpose of the study was to evaluate the effect of second-look ureteroscopy (SU) in the endoscopic operative work-up of patients with upper tract urothelial carcinoma (UTUC). MATERIALS AND METHODS: Patients with UTUC who underwent SU between 2016 and 2021 were included. Cancer detection rate (CDR) at SU was defined as endoscopic visualization of tumor. The effect of SU on recurrence-free survival (RFS), radical nephroureterectomy-free survival (RNU-FS), bladder cancer-free survival (BC-FS), and cancer-specific survival (CSS) was estimated using the Kaplan-Meier method. Multivariate logistic regression analysis (MLR) assessed predictors of negative SU. Finally, we evaluated the effect of SU timing on oncological outcomes, classifying SUs as "early" (≤ 8 weeks) and "late" (> 8 weeks). RESULTS: Overall, 85 patients underwent SU. The CDR at SU was 44.7%. After a median follow-up was 35 (IQR: 15-56) months, patients with positive SU had a higher rate of UTUC recurrence (47.4% vs 19.1%, p = 0.01) and were more frequently radically treated (34.2% vs 8.5%, p = 0.007). Patients with high-grade disease (hazard ratio [HR]: 3.14, 95% CI 1.18-8.31; p = 0.02) had a higher risk of UTUC recurrence, while high-grade tumor (HR: 3.87, 95%CI 1.08-13.77; p = 0.04) and positive SU (HR: 4.56, 95%CI 1.05-22.81; p = 0.04) were both predictors of RNU. Low-grade tumors [odds ratio (OR): 5.26, 95%CI 1.81-17.07, p = 0.003] and tumor dimension < 20 mm (OR: 5.69, 95%CI 1.48-28.31, p = 0.01) were predictors of negative SU. Finally, no significant difference emerged regarding UTUC recurrence, RNU, BC-FS, and CSM between early vs. late SUs (all p > 0.05). CONCLUSIONS: SU may help in identifying patients with UTUC experiencing an early recurrence after conservative treatment. Patients with low-grade and small tumors are those in which SU could be safely postponed after 8 weeks.

A new machine-learning model to predict long-term renal function impairment after minimally invasive partial nephrectomy: the Fundació Puigvert predictive model.

PURPOSE: To provide a new model to predict long-term renal function impairment after partial nephrectomy (PN). METHODS: Data of consecutive patients who underwent minimally invasive PN from 2005 to 2022 were analyzed. A minimum of 12 months of follow-up was required. We relied on a machine-learning algorithm, namely classification and regression tree (CART), to identify the predictors and associated clusters of chronic kidney disease (CKD) stage migration during follow-up. RESULTS: 568 patients underwent minimally invasive PN at our center. A total of 381 patients met our inclusion criteria. The median follow-up was 69 (IQR 38-99) months. A total of 103 (27%) patients experienced CKD stage migration at last follow-up. Progression of CKD stage after surgery, ACCI and baseline CKD stage were selected as the most informative risk factors to predict CKD progression, leading to the creation of four clusters. The progression of CKD stage rates for cluster #1 (no progression of CKD stage after surgery, baseline CKD stage 1-2, ACCI 1-4), #2 (no progression of CKD stage after surgery, baseline CKD stage 1-2, ACCI ≥ 5), #3 (no progression of CKD stage after surgery and baseline CKD stage 3-4-5) and #4 (progression of CKD stage after surgery) were 6.9%, 28.2%, 37.1%, and 69.6%, respectively. The c-index of the model was 0.75. CONCLUSION: We developed a new model to predict long-term renal function impairment after PN where the perioperative loss of renal function plays a pivotal role to predict lack of functional recovery. This model could help identify patients in whom functional follow-up should be intensified to minimize possible worsening factors of renal function.

Robot-assisted radical cystectomy and ileal conduit with HugoTM RAS system: feasibility, setting and perioperative outcomes.

INTRODUCTION: Robotic approach has shown its feasibility and safety with respect to open approach for radical cystectomy (1). The performances of HugoTM RAS system (Medtronic, Minneapolis, USA) have been demonstrated in several clinical scenarios (2-5). We report the feasibility and surgical settings of the first series of robot-assisted radical cystectomy (RARC) with intracorporeal ileal-conduit performed with HugoTM RAS system. METHODS: Two patients were submitted to RARC with ileal conduit at our institution. The trocar placement scheme and the operating room setting with docking angles of the four arms were already described (6). A 12-mm and a 5-mm trocar for the assistant were placed. In both cases, an ileal-conduit with a Wallace type-1 uretero-enteric derivation was performed intra-corporeally. RESULTS: The first patient was a 71-year-old male with a very-high risk non-muscle invasive bladder cancer(BC), and the second patient was a 64-year-old male with a diagnosis of T2 high-grade BC. Operative times were 360 and 420 minutes with a docking time of 12 and 9 minutes, respectively. No intraoperative complications occurred. The estimated blood loss was 200ml and 400ml, respectively. The second patient developed an ileus on postoperative day 4 (Clavien-Dindo grade 2). No positive surgical margins were recorded. No recurrence nor progression occurred during follow-up. CONCLUSION: RARC with intracorporeal ileal conduit urinary diversion is feasible with HugoTM RAS system. We provided insight into the surgical setting using this novel robotic platform to help new adopters to face this challenging procedure. These findings may help a wider distribution of robotic programs for BC treatment.

Machine-Learning-Based Tool to Predict Target Prostate Biopsy Outcomes: An Internal Validation Study.

The aim of this study is to present a personalized predictive model (PPM) with a machine learning (ML) system that is able to identify and classify patients with suspected prostate cancer (PCa) following mpMRI. We extracted all the patients who underwent fusion biopsy (FB) from March 2014 to December 2019, while patients from August 2020 to April 2021 were included as a validation set. The proposed system was based on the following four ML methods: a fuzzy inference system (FIS), the support vector machine (SVM), k-nearest neighbors (KNN), and self-organizing maps (SOMs). Then, a system based on fuzzy logic (FL) + SVM was compared with logistic regression (LR) and standard diagnostic tools. A total of 1448 patients were included in the training set, while 181 patients were included in the validation set. The area under the curve (AUC) of the proposed FIS + SVM model was comparable with the LR model but outperformed the other diagnostic tools. The FIS + SVM model demonstrated the best performance, in terms of negative predictive value (NPV), on the training set (78.5%); moreover, it outperformed the LR in terms of specificity (92.1% vs. 83%). Considering the validation set, our model outperformed the other methods in terms of NPV (60.7%), sensitivity (90.8%), and accuracy (69.1%). In conclusion, we successfully developed and validated a PPM tool using the FIS + SVM model to calculate the probability of PCa prior to a prostate FB, avoiding useless ones in 15% of the cases.

Artificial Intelligence-Based Hyper Accuracy Three-Dimensional (HA3D®) Models in Surgical Planning of Challenging Robotic Nephron-Sparing Surgery: A Case Report and Snapshot of the State-of-the-Art with Possible Future Implications.

Recently, 3D models (3DM) gained popularity in urology, especially in nephron-sparing interventions (NSI). Up to now, the application of artificial intelligence (AI) techniques alone does not allow us to obtain a 3DM adequate to plan a robot-assisted partial nephrectomy (RAPN). Integration of AI with computer vision algorithms seems promising as it allows to speed up the process. Herein, we present a 3DM realized with the integration of AI and a computer vision approach (CVA), displaying the utility of AI-based Hyper Accuracy Three-dimensional (HA3D®) models in preoperative planning and intraoperative decision-making process of challenging robotic NSI. A 54-year-old Caucasian female with no past medical history was referred to the urologist for incidental detection of the right renal mass. Preoperative contrast-enhanced abdominal CT confirmed a 35 × 25 mm lesion on the anterior surface of the upper pole (PADUA 7), with no signs of distant metastasis. CT images in DICOM format were processed to obtain a HA3D® model. RAPN was performed using Da Vinci Xi surgical system in a three-arm configuration. The enucleation strategy was achieved after selective clamping of the tumor-feeding artery. Overall operative time was 85 min (14 min of warm ischemia time). No intra-, peri- and post-operative complications were recorded. Histopathological examination revealed a ccRCC (stage pT1aNxMx). AI is breaking new ground in medical image analysis panorama, with enormous potential in organ/tissue classification and segmentation, thus obtaining 3DM automatically and repetitively. Realized with the integration of AI and CVA, the results of our 3DM were accurate as demonstrated during NSI, proving the potentialities of this approach for HA3D® models' reconstruction.

Long-Term Oncologic Outcomes of Endoscopic Management of High-Risk Upper Tract Urothelial Carcinoma: The Fundació Puigvert's Experience.

Objectives: Many patients with upper tract urothelial carcinoma (UTUC) outside of the low-risk criteria may possess low absolute risks of distant progression. Herein, we hypothesized that careful selection of high-risk patients undergoing an endoscopic approach could result in acceptable oncologic outcomes. Materials and Methods: Patients with high-risk UTUC managed endoscopically between 2015 and 2021 were retrospectively identified from a prospectively maintained single academic institution database. Elective and imperative indications for endoscopic treatment were considered. Regarding elective indications, the decision to perform endoscopic treatment was systematically proposed to high-risk patients in whom macroscopically complete ablation was deemed feasible, excluding invasive appearance on CT scan, and without histologic variant. Results: A total of 60 patients with high-risk UTUC met our inclusion criteria (29 imperative and 31 elective indications). The median follow-up in patients without any event was 36 months. At 5 years, the estimated overall survival, cancer-specific survival, metastasis-free survival, UTUC recurrence-free survival, radical nephroureterectomy-free survival, and bladder recurrence-free survival were 57% (41-79), 75% (57-99), 86% (71-100), 56% (40-76), 81% (70-93), and 69% (54-88), respectively. All oncologic outcomes were similar between patients with elective and imperative indications (all log-rank p > 0.05). Conclusions: In conclusion, we report the first large series of endoscopic treatment in patients with high-risk UTUC, arguing that promising oncologic outcomes can be achieved in properly selected candidates. We encourage multi-institutional collaborative work as a large cohort of high-risk patients treated endoscopically may allow subgroup analyses to define the best candidates.