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Objectives To evaluate accuracy and inter-observer variability using Vesical Imaging-Reporting and Data System (VI-RADS) for discrimination between non-muscle invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). Methods Between September 2017 and July 2018, 78 patients referred for suspected bladder cancer underwent multiparametric MRI of the bladder (mpMRI) prior to transurethral resection of bladder tumor (TURBT). All mpMRI were reviewed by two radiologists, who scored each lesion according to VI-RADS. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each VI-RADS cutoff. Receiver operating characteristics curves were used to evaluate the performance of mpMRI. The Ƙ statistics was used to estimate inter-reader agreement.Results Seventy-five patients were included in the final analysis, 53 with NMIBC and 22 with MIBC. Sensitivity and specificity were 91% and 89% for reader 1 and 82% and 85% for reader 2 respectively when the cutoff VI-RADS > 2 was used to define MIBC. At the same cutoff, PPV and NPV were 77% and 96% for reader 1 and 69% and 92% for reader 2. When the cutoff VI-RADS > 3 was used, sensitivity and specificity were 82% and 94% for reader 1 and 77% and 89% for reader 2. Corresponding PPVand NPV were 86% and 93% for reader 1 and 74% and 91% for reader 2. Area under curve was 0.926 and 0.873 for reader 1 and 2 respectively. Inter-reader agreement was good for the overall score (Ƙ = 0.731).Conclusions VI-RADS is accurate in differentiating MIBC from NMIBC. Inter-reader agreement is overall good. Key Points • Traditionally, the local staging of bladder cancer relies on transurethral resection of bladder tumor. • However, transurethral resection of bladder tumor carries a significant risk of understaging a cancer; therefore, more accurate, faster, and non-invasive staging techniques are needed to improve outcomes. • Multiparametric MRI has proved to be the best imaging modality for local staging; therefore, its use in suitable patients has the potential to expedite radical treatment when necessary and non-invasive diagnosis in patients with poor fitness.

Summary Since sperm require high energy levels to perform their specialised function, it is vital that essential nutrients are available for spermatozoa when they develop, capacitate and acquire motility. However, they are vulnerable to a lack of energy and excess amounts of reactive oxygen species, which can impair sperm function, lead to immotility, acrosomal reaction impairment, DNA fragmentation and cell death. This monocentric, randomised, double‐blind, placebo‐controlled trial investigated the effect of 6 months of supplementation with l‐carnitine, acetyl‐l‐carnitine and other micronutrients on sperm quality in 104 subjects with oligo‐ and/or astheno‐ and/or teratozoospermia with or without varicocele. In 94 patients who completed the study, sperm concentration was significantly increased in supplemented patients compared to the placebo (p = .0186). Total sperm count also increased significantly (p = .0117) in the supplemented group as compared to the placebo group. Both, progressive and total motility were higher in supplemented patients (p = .0088 and p = .0120, respectively). Although pregnancy rate was not an endpoint of the study, of the 12 pregnancies that occurred during the follow‐up, 10 were reported in the supplementation group. In general, all these changes were more evident in varicocele patients. In conclusion, supplementation with metabolic and antioxidant compounds could be efficacious when included in strategies to improve fertility.

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Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.

Background Clinical behavior of non‐muscle‐invasive bladder cancer (NMIBC) is largely unpredictable, and even patients treated according to European Association of Urology recommendations have a heterogeneous prognosis. High‐grade T1 (HGT1) bladder cancer is the highest‐risk subtype of NMIBC, with an almost 40% rate of recurrence and 20% of progression at 5 years. Nomograms predicting risk of recurrence, progression, and cancer‐specific survival (CSS) are not available specifically within HGT1 bladder cancer, and the identification of robust prognostic biomarkers to better guide therapeutic strategies in this subgroup of patients is of paramount importance. Strategies to identify putative biomarkers in liquid biopsies from blood and urine collected from patients with bladder cancer have been intensively studied in the last few years. Subjects, Materials, and Methods We here report the final analysis of a single‐center prospective study aimed to investigate the impact of circulating tumor cells (CTCs) on CSS and overall survival (OS) in 102 patients with HGT1 bladder cancer, in a median follow‐up of 63 months. Results We here demonstrate that the presence of even a single CTC is predictive of shorter CSS and OS, as compared with the standard predictive variables. Points of attention in this multivariable analysis are the long‐term follow‐up and the adequate number of outcome events. Conclusion The accurate risk stratification provided by CTCs might be essential for determining the best surveillance strategy for patients after diagnosis. A closer follow‐up, an early radical surgery, or even a systemic treatment might be recommended in patients with super‐high‐risk non‐muscle‐invasive bladder cancer. Implications for Practice Circulating tumor cells identify patients with super‐high‐risk non‐muscle‐invasive bladder cancer who require closer monitoring for local recurrence and/or progression of disease. This super‐high‐risk subgroup of patients might also require more aggressive treatment interventions, which should be evaluated in large prospective cohorts.

Background Basophils, eosinophils and monocytes may be involved in BCG-induced immune responses and be associated with outcomes of bladder cancer patients receiving intravesical BCG. Our objective was to explore the association of baseline counts of basophils, eosinophils and monocytes with outcomes of patients with high-grade T1 bladder cancer receiving a standard course of intravesical BCG. Methods We retrospectively reviewed medical records of patients with primary T1 HG/G3 bladder cancer. After re-TURBT, patients were treated with a 6-week course of intravesical BCG induction followed by intravesical BCG every week for 3 weeks given at 3, 6, 12, 18, 24, 30 and 36 months from initiation of therapy The analysis of potential risk factors for recurrence, muscle invasion and cancer-specific and overall survival was performed using univariable Cox regression models. Those factors that presented, at univariate analysis, an association with the event at a liberal p < 0.1, have been selected for the development of a multivariable model. Results A total of 1045 patients with primary T1 HG/G3 were included. A total of 678 (64.9%) recurrences, 303 (29.0%) progressions and 150 (14.3%) deaths were observed during follow-up. Multivariate analysis showed that logarithmic transformation of basophils count was associated with a 30% increment in the hazard of recurrence per unit increase of logarithmic basophils count (HR 1.30; 95% confidence interval 1.09-1.54; p = 0.0026). Basophil count modeled by quartiles was also significantly associated with time to recurrence [second vs. lower quartile HR 1.42 (1.12-1.79); p = 0.003, third vs. lower quartile HR 1.26 (1.01-1.57); p = 0.041; upper vs. lower quartile HR 1.36 (1.1-1.68); p = 0.005]. The limitations of a retrospective study are applicable. Conclusion Baseline basophil count may predict recurrence in BCG-treated HG/G3 T1 bladder cancer patients. External validation is warranted.