NHS RAPID pathway improves patient satisfaction via quick diagnosis and minimal biopsies in men with suspected prostate cancer.
Read the full article: https://bjui-journals.onlinelibrary.wiley.com/doi/10.1111/bju.15899
Urinary incontinence is not life‐threatening and does not kill patients, but it is highly prevalent affecting millions of people worldwide, it significantly impairs quality of life, and the related health‐care costs are enormous. Thus, guidelines are crucial for helping us to achieve an optimal management of our patients with urinary incontinence.
In this month’s issue of the BJUI, Sussman et al. present a Guideline of Guidelines on urinary incontinence in women. They reviewed the guidelines of the American College of Obstetrics and Gynecology (ACOG) / American Urogynecologic Society (AUGS), American Urological Association (AUA) / Society of Urodynamics, Female Pelvic Medicine and Urogenital Reconstruction (SUFU), European Association of Urology (EAU), International Consultation on Incontinence (ICI), and National Institute for Health and Care Excellence (NICE). The recommendations of the different guidelines were similar for the initial evaluation and conservative therapies but differed considerably in some points of invasive management. In brief, the most essential issues are the following: Basic work‐up includes detailed history taking and specifying the type of urinary incontinence, urodynamics is performed when it changes management and in cases of recurrent urinary incontinence after interventions, treatment follows a stepwise approach starting with conservative therapy and moving to invasive options as appropriate, and treatment of women with mixed urinary incontinence is focused on the predominant symptom.
Although the management of urinary incontinence is well defined and excellently summarised by Sussman et al., treatment often remains demanding in daily clinical practice due to insufficient effectiveness or relevant side effects, so that new therapeutic options are urgently needed. Vibegron is a novel β3‐adrenoreceptor agonist, and Yoshida et al. present in the current issue of the BJUI promising findings with this drug for treating severe urgency urinary incontinence related to overactive bladder. In a post hoc analysis of a randomised, placebo‐controlled, double‐blind, comparative phase 3 study, vibegron significantly reduced the number of urgency urinary incontinence episodes and significantly increased voided volume per micturition with a response rate exceeding 50% [Yoshida et al]. These results are encouraging and warrant further randomised controlled trials, but also vibegron seems not to be a miracle agent showing effects in the range of mirabegron or antimuscarinics. However, there is some light at the end of the tunnel: Closed‐loop optogenetic neuromodulation systems targeting specific neurons to control urinary tract function might completely revolutionise the field, although there are still relevant hurdles to overcome.
Guidelines should result from a rigorous and transparent process informed by the best available up‐to‐date evidence and safeguarded against biases and conflict of interests. This is a major challenge, and from a bird’s eye view, it is hard to comprehend that several guidelines on the same topic exist and it is even more difficult to understand that the recommendations of these guidelines are not congruent and sometimes even contradictory. For instance, in the case of a pelvic organ prolapse repair in a continent woman, the ACOG / AUGS, AUA / SUFU, and EAU guidelines discuss prophylactic anti‐incontinence surgery as an option, whereas ICI and NICE guidelines explicitly recommend against it. The redundancy is enormous, but societies and organisations still create their own guidelines – an unnecessary waste of resources. In recent times, the coronavirus pandemic has rapidly changed our life and paralysed our usual activities, we have to stand together, it is definitively time to join forces! The relevant societies and organisations should consult each other and coordinate their efforts. Together we are strong, let’s move forward to joint guidelines!
Every month, the Editor-in-Chief selects an Article of the Month from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.
In addition to the article itself, there is an editorial prepared by a prominent member of the urological community and a video by the authors; we invite you to use the comment tools at the bottom of each post to join the conversation.
If you only have time to read one article this month, we recommend this one.
Rachael D. Sussman*, Raveen Syan† and Benjamin M. Brucker‡
*Department of Urology, MedStar Georgetown University Hospital, Washington, DC, †Department of Urology, Stanford School of Medicine, Stanford, CA, and ‡Department of Urology, New York University Medical Center, New York, NY, USA
Urinary incontinence (UI) is a common disease, with prevalence rates as high as 44–57% in middle‐aged and post‐menopausal women. Those with UI may experience physical, functional, and psychological limitations and diminished quality of life (QoL) at home and at work. The financial burden of UI care is significant, with an estimated direct cost of $19.5 billion (American dollars) in the USA alone.
UI can be classified into a number of different categories, with stress UI (SUI) and urgency UI (UUI) being the most common. Many professional organisations have created guidelines to help clinicians navigate the diagnosis and evaluation of UI, as well as the treatments including conservative, pharmacological, and surgical. The methodologies upon which most guidelines are based are similar, starting with systematic reviews and grading of available literature. Organisations then make recommendations with different definitions and strengths. Guidelines are not exhaustive, but rather serve as a practical review of evidence‐based management of ‘index patients’.
The present ‘Guideline of guidelines,’ updated from a 2016 publication, reviews various international guidelines that have been updated at different time intervals and provides an updated summary of the important similarities and differences on the management of UI in women.
Maria Uloko is a Urology Resident at the University of Minnesota Hospital. In this podcast she is joined by Dr Christopher Weight, an Associate Professor in the Department of Urology at the University of Minnesota. They are discussing a recent BJUI Article of the month:
To investigate the applications of artificial intelligence (AI) in diagnosis, treatment and outcome prediction in urologic diseases and evaluate its advantages over traditional models and methods.
A literature search was performed after PROSPERO registration (CRD42018103701) and in compliance with Preferred Reported Items for Systematic Reviews and Meta‐Analyses (PRISMA) methods. Articles between 1994 and 2018 using the search terms “urology”, “artificial intelligence”, “machine learning” were included and categorized by the application of AI in urology. Review articles, editorial comments, articles with no full‐text access, and nonurologic studies were excluded.
Initial search yielded 231 articles, but after excluding duplicates and following full‐text review and examination of article references, only 111 articles were included in the final analysis. AI applications in urology include: utilizing radiomic imaging or ultrasonic echo data to improve or automate cancer detection or outcome prediction, utilizing digitized tissue specimen images to automate detection of cancer on pathology slides, and combining patient clinical data, biomarkers, or gene expression to assist disease diagnosis or outcome prediction. Some studies employed AI to plan brachytherapy and radiation treatments while others used video-based or robotic automated performance metrics to objectively evaluate surgical skill. Compared to conventional statistical analysis, 71.8% of studies concluded that AI is superior in diagnosis and outcome prediction.
AI has been widely adopted in urology. Compared to conventional statistics AI approaches are more accurate in prediction and more explorative for analyzing large data cohorts. With an increasing library of patient data accessible to clinicians, AI may help facilitate evidence‐based and individualized patient care.
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Dr Weight specializes in the surgical treatment of urologic cancers including prostate, bladder, kidney, adrenal, testis and penile cancer. He performs open, endoscopic, laparoscopic, robotic (da Vinci) and retroperineoscopic surgery.
Dr Weight completed his residency training at Cleveland Clinic where he received several awards including the George and Grace Crile Traveling Fellowship Award, the Society of Laparoendoscopic Surgeons Resident Achievement Award and the ASCO Genitourinary Cancer Symposium Merit Award. Dr. Weight then completed a fellowship in Urologic Oncology at Mayo Clinic, where he also completed a Masters degree in Clinical and Translational Research from Mayo Graduate School and was awarded the Mayo Fellows Association Humanitarian Award.
Dr Weight believes that medical research is a key component to offering excellent patient care. His research is focused on improving patient outcomes and the use of artificial intelligence in different urologic applications. He is an author of more than 45 peer-reviewed publications and book chapters and has been invited to speak at regional, national and international conferences.
Every month, the Editor-in-Chief selects an Article of the Month from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.
In addition to the article itself, there is an editorial and a visual abstract produced by prominent members of the urological community. These are intended to provoke comment and discussion and we invite you to use the comment tools at the bottom of each post to join the conversation.
If you only have time to read one article this month, it should be this one.
Jian Chen*, Daphne Remulla*, Jessica H. Nguyen*, D. Aastha†, Yan Liu†, Prokar Dasgupta‡ and Andrew J. Hung*
*Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation & Education, University of Southern California Institute of Urology, †Computer Science Department, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA, and ‡Division of Transplantation Immunology and Mucosal Biology, Faculty of Life Sciences and Medicine, Kings College London, London, UK
To investigate the applications of artificial intelligence (AI) in diagnosis, treatment and outcome prediction in urologic diseases and evaluate its advantages over traditional models and methods.
A literature search was performed after PROSPERO registration (CRD42018103701) and in compliance with Preferred Reported Items for Systematic Reviews and Meta‐Analyses (PRISMA) methods. Articles between 1994 and 2018 using the search terms “urology”, “artificial intelligence”, “machine learning” were included and categorized by the application of AI in urology. Review articles, editorial comments, articles with no full‐text access, and non-urologic studies were excluded.
Initial search yielded 231 articles, but after excluding duplicates and following full‐text review and examination of article references, only 111 articles were included in the final analysis. AI applications in urology include: utilizing radiomic imaging or ultrasonic echo data to improve or automate cancer detection or outcome prediction, utilizing digitized tissue specimen images to automate detection of cancer on pathology slides, and combining patient clinical data, biomarkers, or gene expression to assist disease diagnosis or outcome prediction. Some studies employed AI to plan brachytherapy and radiation treatments while others used video based or robotic automated performance metrics to objectively evaluate surgical skill. Compared to conventional statistical analysis, 71.8% of studies concluded that AI is superior in diagnosis and outcome prediction.
AI has been widely adopted in urology. Compared to conventional statistics AI approaches are more accurate in prediction and more explorative for analyzing large data cohorts. With an increasing library of patient data accessible to clinicians, AI may help facilitate evidence‐based and individualized patient care.
To investigate the applications of artificial intelligence (AI) in diagnosis, treatment and outcome prediction in urologic diseases and evaluate its advantages over traditional models and methods.
A literature search was performed after PROSPERO registration (CRD42018103701) and in compliance with Preferred Reported Items for Systematic Reviews and Meta‐Analyses (PRISMA) methods. Articles between 1994 and 2018 using the search terms “urology”, “artificial intelligence”, “machine learning” were included and categorized by the application of AI in urology. Review articles, editorial comments, articles with no full‐text access, and non-urologic studies were excluded.
Initial search yielded 231 articles, but after excluding duplicates and following full‐text review and examination of article references, only 111 articles were included in the final analysis. AI applications in urology include: utilizing radiomic imaging or ultrasonic echo data to improve or automate cancer detection or outcome prediction, utilizing digitized tissue specimen images to automate detection of cancer on pathology slides, and combining patient clinical data, biomarkers, or gene expression to assist disease diagnosis or outcome prediction. Some studies employed AI to plan brachytherapy and radiation treatments while others used video based or robotic automated performance metrics to objectively evaluate surgical skill. Compared to conventional statistical analysis, 71.8% of studies concluded that AI is superior in diagnosis and outcome prediction.
AI has been widely adopted in urology. Compared to conventional statistics AI approaches are more accurate in prediction and more explorative for analyzing large data cohorts. With an increasing library of patient data accessible to clinicians, AI may help facilitate evidence‐based and individualized patient care.
Every week, the Editor-in-Chief selects an Article of the Week from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.
In addition to the article itself, there is an editorial written by a prominent member of the urological community and a video prepared by the authors. These are intended to provoke comment and discussion and we invite you to use the comment tools at the bottom of each post to join the conversation.
If you only have time to read one article this week, it should be this one.
Douglas G. Ward*, Naheema S. Gordon*, Rebecca H. Boucher*, Sarah J. Pirrie*, Laura Baxter†, Sascha Ott†, Lee Silcock‡, Celina M. Whalley*, Joanne D. Stockton*, Andrew D. Beggs*, Mike Griffiths§, Ben Abbotts*, Hanieh Ijakipour*, Fathimath N.Latheef*, Robert A. Robinson*, Andrew J. White*, Nicholas D. James*, Maurice P.Zeegers¶, K. K. Cheng** and Richard T. Bryan*
*Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, †Department of Computer Science, University of Warwick, Coventry, ‡Nonacus Limited, Birmingham Research Park, §West Midlands Regional Genetics Laboratory, Birmingham Women’s and Children’s NHS Foundation Trust, Birmingham, UK, ¶NUTRIM School for Nutrition and Translational Research in Metabolism and CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands and **Institute of Applied Health Research, University of Birmingham, Birmingham, UK
To develop a focused panel of somatic mutations (SMs) present in the majority of urothelial bladder cancers (UBCs), to investigate the diagnostic and prognostic utility of this panel, and to compare the identification of SMs in urinary cell‐pellet (cp) DNA and cell‐free (cf) DNA as part of the development of a non‐invasive clinical assay.
A panel of SMs was validated by targeted deep‐sequencing of tumour DNA from 956 patients with UBC. In addition, amplicon and capture‐based targeted sequencing measured mutant allele frequencies (MAFs) of SMs in 314 urine cpDNAs and 153 urine cfDNAs. The association of SMs with grade, stage and clinical outcomes was investigated by univariate and multivariate Cox models. Concordance between SMs detected in tumour tissue and cpDNA and cfDNA was assessed.
The panel comprised SMs in 23 genes: TERT (promoter), FGFR3, PIK3CA, TP53, ERCC2, RHOB, ERBB2, HRAS, RXRA, ELF3, CDKN1A, KRAS, KDM6A, AKT1, FBXW7, ERBB3, SF3B1, CTNNB1, BRAF, C3orf70, CREBBP, CDKN2A and NRAS; 93.5–98.3% of UBCs of all grades and stages harboured ≥1 SM (mean: 2.5 SMs/tumour). RAS mutations were associated with better overall survival (P = 0.04). Mutations in RXRA, RHOB and TERT (promoter) were associated with shorter time to recurrence (P < 0.05). MAFs in urinary cfDNA and cpDNA were highly correlated; using a capture‐based approach, >94% of tumour SMs were detected in both cpDNA and cfDNA.
SMs are reliably detected in urinary cpDNA and cfDNA. The technical capability to identify very low MAFs is essential to reliably detect UBC, regardless of the use of cpDNA or cfDNA. This 23‐gene panel shows promise for the non‐invasive diagnosis and risk stratification of UBC.
In this issue of BJUI, Ward et al. [1] describe the development of DNA‐based urinary biomarkers for urothelial carcinoma (UC). The genomics of UC have been well characterized through interrogation of tumour issues in institutional series (e.g. the Memorial Sloan Kettering Cancer Center [MSKCC] experience), multi‐institutional collaborations (e.g. The Cancer Genome Atlas [TCGA]) and commercial platforms (e.g. the Foundation Medicine experience) [2]. Until recently, these have been largely academic pursuits, with possible impact on prognostication but limited clinical applicability and utility for therapy selection and monitoring of response; however, with the US Food and Drug Administration approval of erdafitinib several weeks ago, patients with advanced UC will routinely receive genomic assessment for FGFR2/3 mutation or fusion, the targets for this therapy [3]. In due time, it is anticipated that multiple other putative targets with associated therapies (e.g. ERBB2, CDKN2A), as well as potential predictive biomarkers, may also warrant testing.
The evolving landscape in advanced UC makes a non‐invasive biomarker particularly attractive. The authors of the present commentary have previously reported results from a series of 369 patients with advanced UC, demonstrating that genomic alterations in ctDNA could be identified in 91% of patients using a commercially available 73-gene panel [4]. More recently, Christensen et al. [5] assessed a cohort of 68 patients receiving neoadjuvant chemotherapy for muscle‐invasive disease, demonstrating 100% sensitivity and 98% specificity for the detection of relapsed disease with a patient‐specific ctDNA assessment (sequenced to a median target coverage of 105 000×) after cystectomy. Impressively, the data also showed that the dynamics of ctDNA appeared to be more useful than pathological downstaging in predicting relapse.
In contrast to these studies, Ward et al. have developed a 23‐gene panel based on frequently expressed genes in a cohort of 916 UC tissue specimens, largely derived from patients with non‐muscle‐invasive disease. Ultimately, with a cohort of 314 patients with DNA derived from a urinary cell pellet, sequencing identified 645 (71.4%) of 903 mutations detected in tumour. Using urinary supernatant, 353 (80.7%) of 437 mutations were detected. These relatively high sensitivities, if they can be interpreted as such, are promising but do not rise to the level of replacing existing strategies for UC detection, staging and monitoring. Notably, another study demonstrated that urinary ctDNA can be detected with high sensitivity and specificity in patients with localized early‐stage bladder cancer and for after‐treatment surveillance, providing the foundation for further studies evaluating the role of ctDNA in non‐invasive detection, genotyping and monitoring [6].
Beyond its use as a diagnostic tool, it is hoped that urinary ctDNA may also find applications in the selection of therapeutics. To this end, Ward et al. identified FGFR3, PIK3CA, ERCC2 and ERBB2 mutations in 45%, 32%, 14% and 7% of patients, respectively. The frequency of FGFR3 alteration decreased with increasing stage and grade, ranging from 72% in pTaG1 disease to just 13% in ≥pT2 disease, consistent with other reports [7]. These results may guide forthcoming studies evaluating FGFR inhibitors in non‐muscle‐invasive, muscle‐invasive and metastatic disease, where studies are ongoing. In reviewing the potential link between genomic alterations and clinical outcomes, perhaps the most curious finding is that between RAS mutations and improved overall survival (P = 0.04), the only such association found in multivariate analysis. These results stand in sharp contrast to reports in lung cancer, colorectal cancer and multiple other tumour types [8]. A closer look at the deleterious nature and functional impact of NRAS and KRAS mutations seen in this series is certainly warranted, along with further external validation in a more homogenous and larger patient population. There is also the potential application of monitoring treatment response by assessing eradication of urinary ctDNA, a hypothesis that is being evaluated in ongoing studies [9].
How will the results of this and other emerging urinary biomarker studies eventually make their way to the clinic? The answer is simple: incorporation of these biomarkers in prospective therapeutic trials. As the bladder cancer investigative community formulates novel trials for non‐muscle‐invasive and muscle‐invasive disease using targeted therapies, an excellent opportunity exists to correlate urinary, blood and tissue‐based biomarkers and to assess their relative predictive capabilities and clinical utility. Furthermore, with clinical surrogate endpoints likely to drive regulatory approval (e.g. landmark complete response rates for non‐muscle‐invasive disease, or pT0N0 rate for muscle‐invasive disease), a validated urinary biomarker could ultimately offer an alternative biological surrogate endpoint [10]. In an era of genomic revolution, prospective validation can help establish the potential clinical utility of promising biomarkers and help realize the dream of ‘precision oncology’.
by Rohit K. Jain, Petros Grivas and Sumanta K. Pal
