Axial view of bladder tumor (A) at time of bladder cancer diagnosis and (B) 2 years before diagnosis on imaging performed for diverticulosis. Coronal view of bladder tumor (C) at time of diagnosis and (D) 2 years before diagnosis. Red arrows to indicate bladder mass. Note the increased noise in images (B,D) secondary to dose reduction techniques.

Axial view of bladder tumor (A) at time of bladder cancer diagnosis and (B) 2 years before diagnosis on imaging performed for diverticulosis. Coronal view of bladder tumor (C) at time of diagnosis and (D) 2 years before diagnosis. Red arrows to indicate bladder mass. Note the increased noise in images (B,D) secondary to dose reduction techniques.

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Background: Bladder cancer is the sixth most common malignancy in the United States (US). Despite its high prevalence and the significant potential benefits of early detection, no reliable, cost-effective screening algorithm exists for asymptomatic patients at risk. Nonetheless, reports of incidentally identified early bladder cancer on CT/MRI sca...

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... a retrospective review of abdominopelvic CT imaging performed before a pathologically established diagnosis of non-muscle-invasive bladder cancer, we found that a bladder mass was discernable in over two-thirds of the patients, and in nearly one-third of the CT studies performed up to five years preceding the biopsy-confirmed diagnosis. Figure 5 is an illustration of one such example in a 62-year-old male who presented with left lower quadrant pain and hematuria and was worked up for bladder cancer with a CT of the abdomen and pelvis that showed a 2.6 cm intraluminal bladder tumor. In retrospect, a 1.0 cm bladder mass was present on a CT performed 2 years prior for diverticular disease. ...

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... The precise evaluation of muscular invasion is crucial for determining the appropriate treatment, as patients with non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) necessitate different therapeutic approaches. In this scenario, computed tomography (CT) and multiparametric magnetic resonance imaging (mpMRI) are non-invasive diagnostic techniques, each exhibiting variable degrees of accuracy in staging BC [4,5]. More specifically, a standardized methodology for imaging and reporting mpMRI in BC patients was developed by Panebianco et al. in 2018 with the Vesical Imaging-Reporting and Data System (VI-RADS) score [6]. ...
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Background: Contrast-enhanced ultrasound (CEUS) is a diagnostic tool that is gaining popularity for its ability to improve overall diagnostic accuracy in bladder cancer (BC) staging. Our aim is to determine the cumulative diagnostic performance of CEUS in predicting preoperative muscle invasiveness using a comprehensive systematic review and pooled meta-analysis. Methods: A systematic review until October 2023 was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Patients with BC suspicion were offered CEUS before the transurethral resection of the bladder tumor (TURBT). The diagnostic performance of CEUS was evaluated based on non-muscle-invasive bladder cancer (NMIBC) vs. muscle-invasive bladder cancer (MIBC) confirmed at the final histopathological examination after TURBT. The outcomes were determined through pooled sensitivity, specificity, pooled positive likelihood ratio (PLR+), negative likelihood ratio (PLR−), and area under the summary receiver operating characteristic (SROC) along with their respective 95% confidence intervals (CI). Results: Overall, five studies were included. In these studies, a total of 362 patients underwent CEUS prior to TURBT. The pooled sensitivity and specificity were 0.88 (95% CI: 0.81–0.93) and 0.88 (95% CI: 0.82–0.92), respectively. SROC curve depicted a diagnostic accuracy of 0.94 (95% CI: 0.81–0.98). The pooled PLR+ and PLR− were 7.3 (95% CI: 4.8–11.2) and 0.14 (95% CI: 0.08–0.23), respectively. Conclusions: Our meta-analysis indicates that CEUS is highly accurate in the diagnosis and staging for BC. Beyond its accuracy, CEUS offers the advantage of being a cost-effective, safe, and versatile imaging tool.
... It requires pathologists' efforts to identify them, but the manual review of pathologist may sometimes bring mistakes (4). Imaging tests can help detect BCa early (6), but they are also influenced by human factors. Treatments for BCa include non-surgical treatments like systemic chemotherapy and radiation therapy and surgical treatments like transurethral resection of bladder tumor, open radical cystectomy, laparoscopic radical cystectomy and roboticassisted laparoscopic radical cystectomy. ...
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Introduction Since the significant breakthroughs in artificial intelligence (AI) algorithms, the application of AI in bladder cancer has rapidly expanded. AI can be used in all aspects of the bladder cancer field, including diagnosis, treatment and prognosis prediction. Nowadays, these technologies have an excellent medical auxiliary effect and are in explosive development, which has aroused the intense interest of researchers. This study will provide an in-depth analysis using bibliometric analysis to explore the trends in this field. Method Documents regarding the application of AI in bladder cancer from 2000 to 2022 were searched and extracted from the Web of Science Core Collection. These publications were analyzed by bibliometric analysis software (CiteSpace, Vosviewer) to visualize the relationship between countries/regions, institutions, journals, authors, references, keywords. Results We analyzed a total of 2368 publications. Since 2016, the number of publications in the field of AI in bladder cancer has increased rapidly and reached a breathtaking annual growth rate of 43.98% in 2019. The U.S. has the largest research scale, the highest study level and the most significant financial support. The University of North Carolina is the institution with the highest level of research. EUROPEAN UROLOGY is the most influential journal with an impact factor of 24.267 and a total citation of 11,848. Wiklund P. has the highest number of publications, and Menon M. has the highest number of total citations. We also find hot research topics within the area through references and keywords analysis, which include two main parts: AI models for the diagnosis and prediction of bladder cancer and novel robotic-assisted surgery for bladder cancer radicalization and urinary diversion. Conclusion AI application in bladder cancer is widely studied worldwide and has shown an explosive growth trend since the 21st century. AI-based diagnostic and predictive models will be the next protagonists in this field. Meanwhile, the robot-assisted surgery is still a hot topic and it is worth exploring the application of AI in it. The advancement and application of algorithms will be a massive driving force in this field.