The goals of transurethral resection of bladder tumour (TURBT) are to identify and eradicate visualized bladder tumour if technically safe and feasible and to obtain a specimen of satisfactory quality to enable accurate histological diagnosis. In the setting of high grade bladder tumour this generally entails the inclusion of detrusor muscle and assessment for the presence of associated carcinoma in situ (CIS), lymphovascular involvement or any variant form of bladder cancer. This will assist in determining risk stratification and prognostication of the bladder cancer and guides further treatment planning. Conversely, if suboptimal TURBT is performed there will be detrimental consequences on patient outcomes in regards to undergrading or understaging, increased recurrence or progression, and subsequently need for further treatments including more invasive interventions. This review article firstly summarises the key principles and complications of TURBT, as well as significance of re-TURBT. We also discuss a number of modifications and advances in detection technology and resection techniques that have shown to improve perioperative as well as pathological and oncological outcomes of bladder cancer. They include enhanced cystoscopy such as blue light cystoscopy (BLC), narrow band imaging (NBI) and en bloc resection of bladder tumour (ERBT) technique using various types of energy source.

Background: Artificial intelligence (AI) chatbots, such as ChatGPT, have made significant progress. These chatbots, particularly popular among health care professionals and patients, are transforming patient education and disease experience with personalized information. Accurate, timely patient education is crucial for informed decision-making, especially regarding prostate-specific antigen screening and treatment options. However, the accuracy and reliability of AI chatbots’ medical information must be rigorously evaluated. Studies testing ChatGPT’s knowledge of prostate cancer are emerging, but there is a need for ongoing evaluation to ensure the quality and safety of information provided to patients. Objective: This study aims to evaluate the quality, accuracy, and readability of ChatGPT-4’s responses to common prostate cancer questions posed by patients. Methods: Overall, 8 questions were formulated with an inductive approach based on information topics in peer-reviewed literature and Google Trends data. Adapted versions of the Patient Education Materials Assessment Tool for AI (PEMAT-AI), Global Quality Score, and DISCERN-AI tools were used by 4 independent reviewers to assess the quality of the AI responses. The 8 AI outputs were judged by 7 expert urologists, using an assessment framework developed to assess accuracy, safety, appropriateness, actionability, and effectiveness. The AI responses’ readability was assessed using established algorithms (Flesch Reading Ease score, Gunning Fog Index, Flesch-Kincaid Grade Level, The Coleman-Liau Index, and Simple Measure of Gobbledygook [SMOG] Index). A brief tool (Reference Assessment AI [REF-AI]) was developed to analyze the references provided by AI outputs, assessing for reference hallucination, relevance, and quality of references. Results: The PEMAT-AI understandability score was very good (mean 79.44%, SD 10.44%), the DISCERN-AI rating was scored as “good” quality (mean 13.88, SD 0.93), and the Global Quality Score was high (mean 4.46/5, SD 0.50). Natural Language Assessment Tool for AI had pooled mean accuracy of 3.96 (SD 0.91), safety of 4.32 (SD 0.86), appropriateness of 4.45 (SD 0.81), actionability of 4.05 (SD 1.15), and effectiveness of 4.09 (SD 0.98). The readability algorithm consensus was “difficult to read” (Flesch Reading Ease score mean 45.97, SD 8.69; Gunning Fog Index mean 14.55, SD 4.79), averaging an 11th-grade reading level, equivalent to 15- to 17-year-olds (Flesch-Kincaid Grade Level mean 12.12, SD 4.34; The Coleman-Liau Index mean 12.75, SD 1.98; SMOG Index mean 11.06, SD 3.20). REF-AI identified 2 reference hallucinations, while the majority (28/30, 93%) of references appropriately supplemented the text. Most references (26/30, 86%) were from reputable government organizations, while a handful were direct citations from scientific literature. Conclusions: Our analysis found that ChatGPT-4 provides generally good responses to common prostate cancer queries, making it a potentially valuable tool for patient education in prostate cancer care. Objective quality assessment tools indicated that the natural language processing outputs were generally reliable and appropriate, but there is room for improvement.

Objectives: To determine whether local treatment of primary prostate cancer gives palliative benefit to men who later develop castrate‐resistant prostate cancer ( CRPC ). Local treatments of primary prostate cancer are defined as radical retropubic prostatectomy ( RRP ) or external beam radiation therapy ( EBRT ). Patients and Methods: Patient records were reviewed in five different hospitals in S ydney, A ustralia, and 263 men with CRPC were identified. Eligible patients comprised men who had progressive disease during androgen deprivation therapy with castrate levels of testosterone. Clinical and pathological data were reviewed and evaluated using the chi‐squared test and relative risk analysis to determine the relationship between previous local prostate treatment and complications secondary to local disease. The end‐point was complications and morbidity attributed to cancer progression locally (i.e. from the prostate). Results: Primary treatment of the prostate by either RRP or EBRT significantly reduces the incidence of local complications compared to no primary treatment (32.6% vs 54.6%; P = 0.001). RRP showed a significantly lower level of local complications compared to EBRT (20.0% vs 46.7%; P = 0.007). The most common local complications were bladder outlet obstruction (35.0%) and ureteric obstruction (15.2%). Conclusions: The present retrospective analysis supports the hypothesis that primary local prostatic treatment gives palliative benefit to men who later develop CRPC . RRP was associated with the lowest local complication rate experienced at the stage of metastatic disease.