scholarly journals Diagnosis and treatment of non-muscle-invasive bladder cancer

2015 ◽  
Vol 6 (2) ◽  
pp. 23-27 ◽  
Author(s):  
Hugh Mostafid ◽  
Richard T. Bryan ◽  
Jonathan Rees
2018 ◽  
Vol 12 (2) ◽  
pp. 129-133 ◽  
Author(s):  
Daniel Chalk ◽  
Neil Trent ◽  
Sarath Vennam ◽  
John McGrane ◽  
Mark Mantle

Objective: To develop a simulation model to identify key bottlenecks in the bladder cancer pathway at Royal Cornwall Hospital and predict the impact of potential changes to reduce these delays. Materials and methods: The diagnosis and treatment of muscle-invasive bladder cancer can suffer numerous delays, which can significantly affect patient outcomes. We developed a discrete event computer simulation model of the flow of patients through the bladder cancer pathway at the hospital, using anonymised patient records from 2014 and 2015. The changes tested in the model were for patients suspected to have muscle-invasive disease on flexible cystoscopy. Those patients were ‘fast-tracked’ to receive their transurethral resection of bladder tumour (TURBT) treatment using operating slots kept free for these patients. A staging computed tomography scan was booked in the haematuria clinic. Pathology requests were marked as 48 hour turnaround. The nurse specialist would then speak to the patient whilst they were on the ward following their TURBT to give information about their ongoing treatment and provide support. Results: The model predicted that if the changes were implemented, delays in the system could be reduced by around 5 weeks. The changes were implemented, and analysis of 3 months of the data post-implementation shows that the average time in the system was reduced by 5 weeks. The environment created by the changes in the pathway improved referral to treatment times in both muscle-invasive and non-muscle-invasive groups. Conclusion: The simulation model proved an invaluable tool for facilitating the implementation of changes. Simple changes to the pathway led to significant reductions in delays for bladder cancer patients at Royal Cornwall Hospital. Level of evidence: Not applicable for this cohort study.


2016 ◽  
Vol 196 (4) ◽  
pp. 1021-1029 ◽  
Author(s):  
Sam S. Chang ◽  
Stephen A. Boorjian ◽  
Roger Chou ◽  
Peter E. Clark ◽  
Siamak Daneshmand ◽  
...  

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Yadong Xu ◽  
Cheng Luo ◽  
Jieqiong Wang ◽  
Lingwu Chen ◽  
Junxing Chen ◽  
...  

AbstractBladder cancer (BC) is a common malignancy in the genitourinary system and the current theranostic approaches are unsatisfactory. Sensitivity and specificity of current diagnosis methods are not ideal and high recurrence and progression rates after initial treatment indicate the urgent need for management improvements in clinic. Nanotechnology has been proposed as an effective method to improve theranosis efficiency for both non-muscle invasive bladder cancer (NMIBC) and muscle invasive bladder cancer (MIBC). For example, gold nanoparticles (AuNPs) have been developed for simple, fast and sensitive urinary sample test for bladder cancer diagnosis. Nanoparticles targeting bladder cancers can facilitate to distinguish the normal and abnormal bladder tissues during cystoscopy and thus help with the complete removal of malignant lesions. Both intravenous and intravesical agents can be modified by nanotechnology for targeted delivery, high anti-tumor efficiency and excellent tolerability, exhibiting encouraging potential in bladder cancer treatment. Photosensitizers and biological agents can also be delivered by nanotechnology, intermediating phototherapy and targeted therapy. The management of bladder cancer remained almost unchanged for decades with unsatisfactory effect. However, it is likely to change with the fast-developed nanotechnology. Herein we summarized the current utility of nanotechnology in bladder cancer diagnosis and treatment, providing insights for the future designing and discovering novel nanoparticles for bladder cancer management. Graphical Abstract


Author(s):  
Jessica Marinaro ◽  
Alexander Zeymo ◽  
Jillian Egan ◽  
Filipe Carvalho ◽  
Ross Krasnow ◽  
...  

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