BJUI compass最新文献

Purpose

This study aimed to report a newly developed, high-fidelity synthetic simulator to simulate excision and primary anastomotic (EPA) bulbar urethroplasty and its clinical use for new practitioners in shortening the learning curve.

Material and Methods

The bulbar urethral anastomosis simulator consists of several standardized components created according to the actual size of the male patient. Interns, novice residents, and fellows inexperienced with urethral reconstruction (n = 10, 5, 5) from different medical centres were invited to participate in the training programme. Two reconstructive urology experts monitored each practice. Following the training, three kinds of validity testing were used to assess the simulator: face, content, and construct. In the intern group, the task performance in the first five training sessions and the last five training ones were compared using a self-control approach. In the resident and fellow group, the real surgical data, including estimated blood loss, operative duration, and 6-month post-operative success rate of trainees after training, are plotted, which are compared with that of reconstructive urology experts (n = 5) included retrospectively to study the effectiveness of the simulator in shortening the learning curve.

Results

The overall mean satisfaction rate for the simulators was inspiring and evaluated by experts. In the intern group, significant improvement can be achieved through 10 training sessions (p < 0.05). In clinical practice, the intraoperative indicators and surgical success rate of both the training groups showed the tendency to close or even better than those in the expert group. In terms of the learning curve, training groups performed better compared with experts in the early stages of their careers.

Conclusions

In conclusion, this synthetic training simulator for bulbar urethral anastomosis is novel, effective, and convenient for beginners of different groups. The training course can bridge the gap between preclinical use and actual surgery via this simulator.

Objective

The objective of this study is to compare detection rates of extracapsular extension (ECE) of prostate cancer (PCa) using artificial intelligence (AI)-generated cancer maps versus MRI and conventional nomograms.

Materials and methods

We retrospectively analysed data from 147 patients who received MRI-targeted biopsy and subsequent radical prostatectomy between September 2016 and May 2022. AI-based software cleared by the United States Food and Drug Administration (Unfold AI, Avenda Health) was used to map 3D cancer probability and estimate ECE risk. Conventional ECE predictors including MRI Likert scores, capsular contact length of MRI-visible lesions, PSMA T stage, Partin tables, and the “PRedicting ExtraCapsular Extension” nomogram were used for comparison.

Postsurgical specimens were processed using whole-mount histopathology sectioning, and a genitourinary pathologist assessed each quadrant for ECE presence. ECE predictors were then evaluated on the patient (Unfold AI versus all comparators) and quadrant level (Unfold AI versus MRI Likert score). Receiver operator characteristic curves were generated and compared using DeLong's test.

Results

Unfold AI had a significantly higher area under the curve (AUC = 0.81) than other predictors for patient-level ECE prediction. Unfold AI achieved 68% sensitivity, 78% specificity, 71% positive predictive value, and 75% negative predictive value. At the quadrant level, Unfold AI exceeded the AUC of MRI Likert scores for posterior (0.89 versus 0.82, p = 0.003), anterior (0.84 versus 0.80, p = 0.34), and all quadrants (0.89 versus 0.82, p = 0.002). The false negative rate of Unfold AI was lower than MRI in both the anterior (−60%) and posterior prostate (−40%).

Conclusions

Unfold AI accurately predicted ECE risk, outperforming conventional methodologies. It notably improved ECE prediction over MRI in posterior quadrants, with the potential to inform nerve-spare technique and prevent positive margins. By enhancing PCa staging and risk stratification, AI-based cancer mapping may lead to better oncological and functional outcomes for patients.

Objective

The objective of this study was to measure and describe the national patient experience of radical cystectomy (RC) pathways in the UK using the validated Cystectomy-Pathway Assessment Tool (C-PAT).

Patients and Methods

A cohort of 1081 patients who underwent RC for bladder cancer, between 1 January 2021 and 31 July 2022 at 33 UK cystectomy centres, returned completed C-PAT responses. SPSS was employed for data summary statistics, including median, interquartile range, Mann Whitney U test or Chi-square test with a 95% confidence interval to assess statistical significance between potentially associated variables. Open-text responses in the C-PAT tool were analysed and coded using NVivo software.

Results

In this cohort, the greatest perceived delay in the RC pathway, reported by 19% of patients (n = 208), was at the GP consultation to first hospital referral stage with suspected bladder cancer. Around 10% of patients perceived delays at each of the other stages in their pathway. Cancer nurse specialist (CNS) contact was strongly associated with an improved patient experience (p < 0.001); however, 9.5% of patients reported that they were not assigned a cancer nurse specialist in their pathway. Overall, 96% (n = 1028) reported their experience of RC pathway care to be good or excellent. There were no significant differences in reported patient experience found between cystectomy centres.

Conclusion

This audit demonstrates the feasibility of measuring patient experience of RC pathways at scale. The C-PAT tool demonstrated utility in identifying specific pathway areas for quality improvement. Overall UK patients report a high quality pathway experience. A focus on improving the referral pathway between primary and secondary care is necessary.

Objectives

The objective of this study is to report the pilot phase of the Targeted Prostate Health Check programme that aims to identify men in the Surrey and Sussex region who have prostate cancer and who failed to be detected during the Covid era.

Subjects and methods

Men aged 50 to 70, or 45 to 70 if Black or with a family history of prostate cancer, were identified from participating general practitioner (GP) records. Short message service (SMS) texts invited men to visit www.talkprostate.co.uk for information on prostate cancer and give consent to prostate-specific antigen (PSA) checks coordinated by a third-party virtual healthcare provider. Elevated age-related PSA levels, or levels below age-related thresholds but at 3 ng/mL or more, triggered referral to a rapid access urology clinic. GPs were informed of the results.

Results

From 1842 text messages inviting 1549 people, 544 men consented to a PSA check. From 500 phlebotomy appointments, 485 (30% of invited men) took the PSA test of whom 68 (14%) were referred with an elevated PSA. After clinical review with multiparametric magnetic resonance imaging (mp-MRI), 22 patients underwent transperineal biopsies, and prostate cancer was detected in 18 men of whom 17 (95%) had clinically significant cancer.

Conclusion

Our Targeted Prostate Cancer Health Check system identifies men at risk without burdening primary care. Awareness on prostate cancer risk was raised in 1549 invited men, half of whom were further educated via the registration website. One third of invited men were checked in whom clinically significant prostate cancer was found in 3.5%.

Context

Prostate-specific antigen is non-specific for prostate cancer. This is improved by multiparametric MRI but a significant amount of indolent prostate cancer is detected by the current MRI pathway and data is emerging that clinically significant cancers maybe missed using a standard PSA threshold. Volatile organic compound (VOC) analysis may offer novel biomarkers for prostate cancer and clinically significant disease.

Objective

To perform a systematic review of the literature to evaluate the current evidence for the use of VOCs as novel biomarkers for prostate cancer and clinically significant prostate cancer.

Evidence Acquisition

A systematic search of MEDLINE, Scopus, Web of Science and the Cochrane Library was undertaken by two independent reviewers and papers were assessed for inclusion in the review. Study characteristics, sensitivity and specificity of GC–MS or eNose were extracted. Risk of bias and applicability issues were determined using QUADAS 2 and the quality of reporting using the STARD checklist.

Evidence Synthesis

Nineteen studies were included, of which 6 utilised eNose and 13 GC–MS. eNose sensitivity and specificity were 0.71–0.95 and 0.79–0.96, respectively, and GC–MS found a sensitivity and specificity of 0.66–1.00 and 0.53–0.97, respectively. There were concerns about bias in patient recruitment due to differences in the timing of the index test relative to the reference standard.

Conclusion

This review has found promising early results for urinary metabolomics in the detection of prostate cancer. However, there is a need for larger, high-quality studies to validate this. Future work should focus on the detection of clinically significant prostate cancer.

Objective

This study aimed to evaluate the association of dynamic contrast enhancement (DCE) with clinically significant prostate cancer (csPCa, Gleason Grade Group ≥2) and compare biparametric magnetic resonance imaging (bpMRI) and multiparametric MRI (mpMRI) nomograms.

Subjects/patients and methods

We identified a retrospective cohort of biopsy naïve patients who underwent pre-biopsy MRI separated by individual MRI series from 2018 to 2022. csPCa detection rates were calculated for patients with peripheral zone (PZ) lesions scored 3–5 on diffusion weighted imaging (DWI) with available DCE (annotated as − or +). bpMRI Prostate Imaging Reporting and Data System (PIRADS) (3 = 3−, 3+; 4 = 4−, 4+; 5 = 5−, 5+) and mpMRI PIRADS (3 = 3−; 4 = 3+, 4−, 4+; 5 = 5−, 5+) approaches were compared in multivariable logistic regression models. Nomograms for detection of csPCa and ≥GG3 PCa incorporating all biopsy naïve patients who underwent prostate MRI were generated based on available serum biomarkers [PHI, % free prostate-specific antigen (PSA), or total PSA] and validated with an independent cohort.

Results

Patients (n = 1010) with highest PIRADS lesion in PZ were included in initial analysis with 127 (12.6%) classified as PIRADS 3+ (PIRADS 3 on bpMRI but PIRADS 4 on mpMRI). On multivariable analysis, PIRADS 3+ lesions were associated with higher csPCa rates compared to PIRADS 3− (3+ vs. 3−: OR 1.86, p = 0.024), but lower csPCa rates compared to PIRADS DWI 4 lesions (4 vs. 3+: OR 2.39, p < 0.001). csPCa rates were 19% (3−), 31% (3+), 41.5% (4−), 65.9% (4+), 62.5% (5−), and 92.3% (5+). bpMRI nomograms were non-inferior to mpMRI nomograms in the development (n = 1410) and independent validation (n = 353) cohorts. Risk calculators available at: https://rossnm1.shinyapps.io/MynMRIskCalculator/.

Conclusion

While DCE positivity by itself was associated with csPCa among patients with highest PIRADS lesions in the PZ, nomogram comparisons suggest that there is no significant difference in performance of bpMRI and mpMRI. bpMRI may be considered as an alternative to mpMRI for prostate cancer evaluation in many situations.

Objective

The aim of this study is to evaluate treatment patterns and long-term oncological outcomes of patients with locally advanced prostate cancer (LAPCa).

Patients and methods

This is a population-based study including LAPC (cT3-4, M0) patients from the Stockholm region (Sweden). A sub-analysis was performed in men treated with primary cystoprostatectomy or total pelvic exenteration (TPE) for cT4 prostate cancer (PCa).

Cox regression was used to identify predictors of overall mortality (OM) and cancer-specific mortality (CSM). Biochemical progression-free survival (BPFS) and 90 days complications were reported for the radical surgery subgroup.

Results

We included 2921 patients with cT3(N = 2713) or cT4(N = 208), M0 PCa diagnosed between 2003 and 2019. Out of these, 249(9%), 1497(51%) and 1175(40%) underwent radical prostatectomy, RT + ADT and androgen deprivation therapy (ADT), respectively. Survival rates were 76% (IQR: 68, 83), 47% (IQR: 44, 50) and 23% (IQR: 20, 27), respectively at 10 years. Irrespective of treatment modalities, cT4 patients had worse survival compared to cT3 patients (OM: HR1.44, IQR:1.17,1.77; PCSM: HR1.39, IQR:1.06,1.82). Twenty-seven patients with cT4, N0-1, M0 were treated with cystoprostatectomy or TPE. Twenty-two patients (81.5%) received neoadjuvant ADT. The 5-year BPFS, CSS and OS rates were 39.6%, 68.8% and 63.8%, respectively. Nine patients (33.3%) had Clavien-Dindo grade III and 1 (3.7%) grade IV complication within 90 days after surgery.

Conclusions

Pelvic surgery with radical intent as part of a multidisciplinary management may be an effective alternative for selected patients with locally advanced PCa leading to local tumour control and an acceptable morbidity.

Introduction

ChatGPT (generative pre-trained transformer [GPT]), developed by OpenAI, is a type of generative artificial intelligence (AI) that has been widely utilised since its public release. It orchestrates an advanced conversational intelligence, producing sophisticated responses to questions. ChatGPT has been successfully demonstrated across several applications in healthcare, including patient management, academic research and clinical trials. We aim to evaluate the different ways ChatGPT has been utilised in urology and more broadly in surgery.

Methods

We conducted a literature search of the PubMed and Embase electronic databases for the purpose of writing a narrative review and identified relevant articles on ChatGPT in surgery from the years 2000 to 2023. A PRISMA flow chart was created to highlight the article selection process. The search terms ‘ChatGPT’ and ‘surgery’ were intentionally kept broad given the nascency of the field. Studies unrelated to these terms were excluded. Duplicates were removed.

Results

Multiple papers have been published about novel uses of ChatGPT in surgery, ranging from assisting in administrative tasks including answering frequently asked questions, surgical consent, writing operation reports, discharge summaries, grants, journal article drafts, reviewing journal articles and medical education. AI and machine learning has also been extensively researched in surgery with respect to patient diagnosis and predicting outcomes. There are also several limitations with the software including artificial hallucination, bias, out-of-date information and patient confidentiality.

Conclusion

The potential of ChatGPT and related generative AI models are vast, heralding the beginning of a new era where AI may eventually become integrated seamlessly into surgical practice. Concerns with this new technology must not be disregarded in the urge to hasten progression, and potential risks impacting patients' interests must be considered. Appropriate regulation and governance of this technology will be key to optimising the benefits and addressing the intricate challenges of healthcare delivery and equity.