The prognostic role of next-generation imaging-driven upstaging in newly diagnosed prostate cancer patients.

PURPOSE: Phase III evidence showed that next-generation imaging (NGI), such as prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT), provides higher diagnostic accuracy than bone scan and contrast-enhanced computed tomography (conventional imaging, CI) in the primary staging of intermediate-to-high-risk prostate cancer (PCa) patients. However, due to the lack of outcome data, the introduction of NGI in routine clinical practice is still debated. Analysing the oncological outcome of patients upstaged by NGI (though managed according to CI) might shed light on this issue, supporting the design of randomised trials comparing the effects of treatments delivered based on NGI vs. CI. METHODS: We prospectively enrolled a cohort of 100 biopsy-proven intermediate-to-high-risk PCa patients staged with CI and PSMA PET/CT (though managed according to the CI stage), to assess the frequency of the stage migration phenomenon. Stage migration was then assessed as biochemical recurrence-free survival (bRFS) predictor. RESULTS: Three patients were lost at follow-up after imaging. PSMA PET/CT upstaged 26.8% of patients compared to CI, while it downstaged 6.1% of patients. Notably, 50% of patients excluded from surgery due to the presence of bone metastases at CI would have been treated with radical-intent approaches if PSMA PET/CT had guided the treatment choice. After a median follow-up of 6 months of surgically treated patients, 22/83 (26.5%) had biochemical recurrence (BCR). PSMA PET/CT-driven upstaging determined a significant risk increase for BCR (HR:3.41, 95%CI:1.21-9.56, p = 0.019). Including stage migration in a univariable and multivariable model identified PSMA PET/CT-upstaging as an independent predictor of bRFS. CONCLUSIONS: In conclusion, implementing NGI for staging purposes improves the prediction of bRFS. Although phase III evidence is still needed, this advancement suggests that NGI may better identify patients who would benefit from local treatments than those who may achieve better oncological outcomes through systemic treatment.

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.

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.

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.

Outcomes of Cytoreductive Radical Prostatectomy for Oligometastatic Prostate Cancer on Prostate-specific Membrane Antigen Positron Emission Tomography: Results of a Multicenter European Study.

BACKGROUND: De novo oligometastatic prostate cancer (omPCa) on prostate-specific membrane antigen (PSMA) positron emission tomography (PET) is a new disease entity and its optimal management remains unknown. OBJECTIVE: To analyze the outcomes of patients treated with cytoreductive radical prostatectomy (cRP) for omPCa on PSMA-PET. DESIGN, SETTING, AND PARTICIPANTS: Overall, 116 patients treated with cRP at 13 European centers were identified. Oligometastatic PCa was defined as miM1a and/or miM1b with five or fewer osseous metastases and/or miM1c with three or fewer lung lesions on PSMA-PET. INTERVENTION: Cytoreductive radical prostatectomy. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Thirty-day complications according to Clavien-Dindo, continence rates, time to castration-resistant PCa (CRPC), and overall survival (OS) were analyzed. RESULTS AND LIMITATIONS: Overall, 95 (82%) patients had miM1b, 18 (16%) miM1a, and three (2.6%) miM1c omPCa. The median prebiopsy prostate-specific antigen was 14 ng/ml, and 102 (88%) men had biopsy grade group ≥3 PCa. The median number of metastases on PSMA-PET was 2; 38 (33%), 29 (25%), and 49 (42%) patients had one, two, and three or more distant positive lesions. A total of 70 (60%) men received neoadjuvant systemic therapy, and 37 (32%) underwent metastasis-directed therapy. Any and Clavien-Dindo grade ≥3 complications occurred in 36 (31%) and six (5%) patients, respectively. At a median follow-up of 27 mo, 19 (16%) patients developed CRPC and eight (7%) patients died. The 1-yr urinary continence rate was 82%. The 2-yr CRPC-free survival and OS were 85.8% (95% confidence interval [CI] 78.5-93.7%) and 98.9% (95% CI 96.8-100%), respectively. The limitations include retrospective design and short-term follow-up. CONCLUSIONS: Cytoreductive radical prostatectomy is a safe and feasible treatment option in patients with de novo omPCa on PSMA-PET. Despite overall favorable oncologic outcomes, some of these patients have a non-negligible risk of early progression and thus should be considered for multimodal therapy. PATIENT SUMMARY: We found that patients treated at expert centers with surgery for prostate cancer, with a limited number of metastases detected using novel molecular imaging, have favorable short-term survival, functional results, and acceptable rates of complications.