NCCN Guidelines® Insights: Bladder Cancer, Version 3.2024.

Bladder cancer, the sixth most common cancer in the United States, is most commonly of the urothelial carcinoma histologic subtype. The clinical spectrum of bladder cancer is divided into 3 categories that differ in prognosis, management, and therapeutic aims: (1) non-muscle-invasive bladder cancer (NMIBC); (2) muscle invasive, nonmetastatic disease; and (3) metastatic bladder cancer. These NCCN Guidelines Insights detail recent updates to the NCCN Guidelines for Bladder Cancer, including changes in the fifth edition of the WHO Classification of Tumours: Urinary and Male Genital Tumours and how the NCCN Guidelines aligned with these updates; new and emerging treatment options for bacillus Calmette-Guérin (BCG)-unresponsive NMIBC; and updates to systemic therapy recommendations for advanced or metastatic disease.

Surgeons' dominant surgical modality: Impacts on wellbeing, burnout, and interventions for neuromusculoskeletal disorders.

BACKGROUND: This study investigated the impact of surgical modalities on surgeon wellbeing with a focus on burnout, job satisfaction, and interventions used to address neuromusculoskeletal disorders (NMSDs). METHODS: An electronic survey was sent to surgeons across an academic integrated multihospital system. The survey consisted of 47 questions investigating different aspects of surgeons' wellbeing. RESULTS: Out of 245 thoracic and abdominopelvic surgeons, 79 surgeons (32.2 â€‹%) responded, and 65 surgeons (82 â€‹%) were able to be categorized as having a dominant surgical modality. Compared to robotic surgeons, laparoscopic (p â€‹= â€‹0.042) and open (p â€‹= â€‹0.012) surgeons reported more frequent feelings of burnout. The number of surgeons who used any treatment/intervention to minimize the operative discomfort/pain was lower for robotic surgeons than the other three modalities (all p â€‹< â€‹0.05). CONCLUSIONS: NMSDs affect different aspects of surgeons' lives and occupations. Robotic surgery was associated with decreased feelings of burnout than the other modalities.

Automated surgical step recognition in transurethral bladder tumor resection using artificial intelligence: transfer learning across surgical modalities.

Objective: Automated surgical step recognition (SSR) using AI has been a catalyst in the "digitization" of surgery. However, progress has been limited to laparoscopy, with relatively few SSR tools in endoscopic surgery. This study aimed to create a SSR model for transurethral resection of bladder tumors (TURBT), leveraging a novel application of transfer learning to reduce video dataset requirements. Materials and methods: Retrospective surgical videos of TURBT were manually annotated with the following steps of surgery: primary endoscopic evaluation, resection of bladder tumor, and surface coagulation. Manually annotated videos were then utilized to train a novel AI computer vision algorithm to perform automated video annotation of TURBT surgical video, utilizing a transfer-learning technique to pre-train on laparoscopic procedures. Accuracy of AI SSR was determined by comparison to human annotations as the reference standard. Results: A total of 300 full-length TURBT videos (median 23.96 min; IQR 14.13-41.31 min) were manually annotated with sequential steps of surgery. One hundred and seventy-nine videos served as a training dataset for algorithm development, 44 for internal validation, and 77 as a separate test cohort for evaluating algorithm accuracy. Overall accuracy of AI video analysis was 89.6%. Model accuracy was highest for the primary endoscopic evaluation step (98.2%) and lowest for the surface coagulation step (82.7%). Conclusion: We developed a fully automated computer vision algorithm for high-accuracy annotation of TURBT surgical videos. This represents the first application of transfer-learning from laparoscopy-based computer vision models into surgical endoscopy, demonstrating the promise of this approach in adapting to new procedure types.

Navigating Now and Next: Recent Advances and Future Horizons in Robotic Radical Prostatectomy.

Robotic-assisted radical prostatectomy (RARP) has become the leading approach for radical prostatectomy driven by innovations aimed at improving functional and oncological outcomes. The initial advancement in this field was transperitoneal multiport robotics, which has since undergone numerous technical modifications. These enhancements include the development of extraperitoneal, transperineal, and transvesical approaches to radical prostatectomy, greatly facilitated by the advent of the Single Port (SP) robot. This review offers a comprehensive analysis of these evolving techniques and their impact on RARP. Additionally, we explore the transformative role of artificial intelligence (AI) in digitizing robotic prostatectomy. AI advancements, particularly in automated surgical video analysis using computer vision technology, are unprecedented in their scope. These developments hold the potential to revolutionize surgeon feedback and assessment and transform surgical documentation, and they could lay the groundwork for real-time AI decision support during surgical procedures in the future. Furthermore, we discuss future robotic platforms and their potential to further enhance the field of RARP. Overall, the field of minimally invasive radical prostatectomy for prostate cancer has been an incubator of innovation over the last two decades. This review focuses on some recent developments in robotic prostatectomy, provides an overview of the next frontier in AI innovation during prostate cancer surgery, and highlights novel robotic platforms that may play an increasing role in prostate cancer surgery in the future.

Automated Identification of Key Steps in Robotic-Assisted Radical Prostatectomy Using Artificial Intelligence.

PURPOSE: The widespread use of minimally invasive surgery generates vast amounts of potentially useful data in the form of surgical video. However, raw video footage is often unstructured and unlabeled, thereby limiting its use. We developed a novel computer-vision algorithm for automated identification and labeling of surgical steps during robotic-assisted radical prostatectomy (RARP). MATERIALS AND METHODS: Surgical videos from RARP were manually annotated by a team of image annotators under the supervision of 2 urologic oncologists. Full-length surgical videos were labeled to identify all steps of surgery. These manually annotated videos were then utilized to train a computer vision algorithm to perform automated video annotation of RARP surgical video. Accuracy of automated video annotation was determined by comparing to manual human annotations as the reference standard. RESULTS: A total of 474 full-length RARP videos (median 149 minutes; IQR 81 minutes) were manually annotated with surgical steps. Of these, 292 cases served as a training dataset for algorithm development, 69 cases were used for internal validation, and 113 were used as a separate testing cohort for evaluating algorithm accuracy. Concordance between artificial intelligence‒enabled automated video analysis and manual human video annotation was 92.8%. Algorithm accuracy was highest for the vesicourethral anastomosis step (97.3%) and lowest for the final inspection and extraction step (76.8%). CONCLUSIONS: We developed a fully automated artificial intelligence tool for annotation of RARP surgical video. Automated surgical video analysis has immediate practical applications in surgeon video review, surgical training and education, quality and safety benchmarking, medical billing and documentation, and operating room logistics.

Impact of time from biopsy to surgery on complications, functional and oncologic outcomes following radical prostatectomy

ABSTRACT Introduction: To determine the impact of time from biopsy to surgery on outcomes following radical prostatectomy (RP) as the optimal interval between prostate biopsy and RP is unknown. Material and methods: We identified 7, 350 men who underwent RP at our institution between 1994 and 2012 and had a prostate biopsy within one year of surgery. Patients were grouped into five time intervals for analysis: ≤ 3 weeks, 4-6 weeks, 7-12 weeks, 12-26 weeks, and > 26 weeks. Oncologic outcomes were stratified by NCCN disease risk for comparison. The associations of time interval with clinicopathologic features and survival were evaluated using multivariate logistic and Cox regression analyses. Results: Median time from biopsy to surgery was 61 days (IQR 37, 84). Median follow-up after RP was 7.1 years (IQR 4.2, 11.7) while the overall perioperative complication rate was 19.7% (1,448/7,350). Adjusting for pre-operative variables, men waiting 12-26 weeks until RP had the highest likelihood of nerve sparing (OR: 1.45, p = 0.02) while those in the 4-6 week group had higher overall complications (OR: 1.33, p = 0.01). High risk men waiting more than 6 months had higher rates of biochemical recurrence (HR: 3.38, p = 0.05). Limitations include the retrospective design. Conclusions: Surgery in the 4-6 week time period after biopsy is associated with higher complications. There appears to be increased biochemical recurrence rates in delaying RP after biopsy, for men with both low and high risk disease.

Oncological outcomes following radical prostatectomy for patients with pT4 prostate cancer

ABSTRACT Objectives: Radical prostatectomy (RP) for locally advanced prostate cancer may reduce the risk of metastasis and cancer-specific death. Herein, we evaluated the outcomes for patients with pT4 disease treated with RP. Materials and methods: Among 19,800 men treated with RP at Mayo Clinic from 1987 to 2010, 87 were found to have pT4 tumors. Biochemical recurrence (BCR)-free survival, systemic progression (SP) free survival and overall survival (OS) were estimated using the Kaplan-Meier method and compared with the log-rank test. Cox proportional hazards regression models were used to assess the association of clinic-pathological features with outcome. Results: Median follow-up was 9.8 years (IQR 3.6, 13.4). Of the 87 patients, 50 (57.5%) were diagnosed with BCR, 30 (34.5%) developed SP, and 38 (43.7%) died, with 11 (12.6%) dying of prostate cancer. Adjuvant androgen deprivation therapy was administered to 77 men, while 32 received adjuvant external beam radiation therapy. Ten-year BCR-free survival, SP-free survival, and OS was 37%, 64%, and 70% respectively. On multivariate analysis, the presence of positive lymph nodes was marginally significantly associated with patients' risk of BCR (HR: 1.94; p=0.05), while both positive lymph nodes (HR 2.96; p=0.02) and high pathologic Gleason score (HR 1.95; p=0.03) were associated with SP. Conclusions: Patients with pT4 disease may experience long-term survival following RP, and as such, when technically feasible, surgical resection should be considered in the multimodal treatment approach to these men.