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Background The benefit of adjuvant chemotherapy was investigated for patients with T3 upper urinary tract urothelial carcinoma (UTUC) who underwent radical nephroureterectomy. Methods This is a multicenter retrospective observational study of 482 patients with pathological T3 N0 and Nx UTUC who underwent radical nephroureterectomy with and without adjuvant chemotherapy. The median overall survival (OS), cancer-specific survival (CSS), and disease-free survival (DFS) of patients who received adjuvant chemotherapy were analyzed to determine predictors of patient prognosis. Results Of the 482 patients, 140 (29.0%) received adjuvant chemotherapy. Adjuvant chemotherapy showed a benefit only for the median DFS rate (58% vs 49%; p = 0.04). Multivariate analyses revealed that adjuvant chemotherapy improved median DFS (HR 0.537; CI 0.35–0.83; p = 0.005). Surgical margin also significantly influenced median OS (HR 4.038; p

Purpose The COVID-19 pandemic has led to the cancellation or deferment of many elective cancer surgeries. We performed a systematic review on the oncological effects of delayed surgery for patients with localised or metastatic renal cell carcinoma (RCC) in the targeted therapy (TT) era. Method The protocol of this review is registered on PROSPERO(CRD42020190882). A comprehensive literature search was performed on Medline, Embase and Cochrane CENTRAL using MeSH terms and keywords for randomised controlled trials and observational studies on the topic. Risks of biases were assessed using the Cochrane RoB tool and the Newcastle–Ottawa Scale. For localised RCC, immediate surgery [including partial nephrectomy (PN) and radical nephrectomy (RN)] and delayed surgery [including active surveillance (AS) and delayed intervention (DI)] were compared. For metastatic RCC, upfront versus deferred cytoreductive nephrectomy (CN) were compared. Results Eleven studies were included for quantitative analysis. Delayed surgery was significantly associated with worse cancer-specific survival (HR 1.67, 95% CI 1.23–2.27, p

Funder: University College London (UCL), INTRODUCTION: Robot-assisted surgery is becoming increasingly adopted by multiple surgical specialties. There is evidence of inherent risks of utilising new technologies that are unfamiliar early in the learning curve. The development of standardised and validated training programmes is crucial to deliver safe introduction. In this review, we aim to evaluate the current evidence and opportunities to integrate novel technologies into modern digitalised robotic training curricula. METHODS: A systematic literature review of the current evidence for novel technologies in surgical training was conducted online and relevant publications and information were identified. Evaluation was made on how these technologies could further enable digitalisation of training. RESULTS: Overall, the quality of available studies was found to be low with current available evidence consisting largely of expert opinion, consensus statements and small qualitative studies. The review identified that there are several novel technologies already being utilised in robotic surgery training. There is also a trend towards standardised validated robotic training curricula. Currently, the majority of the validated curricula do not incorporate novel technologies and training is delivered with more traditional methods that includes centralisation of training services with wet laboratories that have access to cadavers and dedicated training robots. CONCLUSIONS: Improvements to training standards and understanding performance data have good potential to significantly lower complications in patients. Digitalisation automates data collection and brings data together for analysis. Machine learning has potential to develop automated performance feedback for trainees. Digitalised training aims to build on the current gold standards and to further improve the 'continuum of training' by integrating PBP training, 3D-printed models, telementoring, telemetry and machine learning.

To develop a novel Taiwanese prostate cancer (PCa) risk model for predicting PCa, comparing its predictive performance with that of two well-established PCa risk calculator apps. 1545 men undergoing prostate biopsies in a Taiwanese tertiary medical center between 2012 and 2019 were identified retrospectively. A five-fold cross-validated logistic regression risk model was created to calculate the probabilities of PCa and high-grade PCa (Gleason score ≧ 7), to compare those of the Rotterdam and Coral apps. Discrimination was analyzed using the area under the receiver operator characteristic curve (AUC). Calibration was graphically evaluated with the goodness-of-fit test. Decision-curve analysis was performed for clinical utility. At different risk thresholds to biopsy, the proportion of biopsies saved versus low- and high-grade PCa missed were presented. Overall, 278/1309 (21.2%) patients were diagnosed with PCa, and 181 out of 278 (65.1%) patients had high-grade PCa. Both our model and the Rotterdam app demonstrated better discriminative ability than the Coral app for detection of PCa (AUC: 0.795 vs 0.792 vs 0.697, DeLong’s method: P

BackgroundThe advantage of adjuvant chemotherapy for upper urinary tract urothelial cancer (UTUC) has been reported, whereas its impact on upper tract cancer with variant histology remains unclear. We aimed to answer the abovementioned question with our real-world data.Design, Setting, and ParticipantsPatients who underwent radical nephroureterectomy (RNU) and were confirmed to have variant UTUC were retrospectively evaluated for eligibility of analysis. In the Taiwan UTUC Collaboration database, we identified 245 patients with variant UTUC among 3,109 patients with UTUC who underwent RNU after excluding patients with missing clinicopathological information.InterventionThose patients with variant UTUC were grouped based on their history of receiving adjuvant chemotherapy or not.Outcome Measurements and Statistical AnalysisPropensity score matching was used to reduce the treatment assignment bias. Multivariable Cox regression model was used for the analysis of overall, cancer-specific, and disease-free survival.Results and LimitationsFor the patients with variant UTUC who underwent adjuvant chemotherapy compared with those without chemotherapy, survival benefit was identified in overall survival in univariate analysis (hazard ratio (HR), 0.527; 95% confidence interval (CI), 0.285–0.973; p = 0.041). In addition, in multivariate analysis, patients with adjuvant chemotherapy demonstrated significant survival benefits in cancer-specific survival (OS; HR, 0.454; CI, 0.208–0.988; p = 0.047), and disease-free survival (DFS; HR, 0.324; 95% CI, 0.155–0.677; (p = 0.003). The main limitations of the current study were its retrospective design and limited case number.ConclusionsAdjuvant chemotherapy following RNU significantly improved cancer-related survivals in patients with UTUC with variant histology.

The clinical efficacy of adjuvant chemotherapy in upper tract urothelial carcinoma (UTUC) is unclear. We aimed to assess the therapeutic outcomes of adjuvant chemotherapy in patients with advanced UTUC (pT3-T4) after radical nephroureterectomy (RNU). We retrospectively reviewed the data of 2108 patients from the Taiwan UTUC Collaboration Group between 1988 and 2018. Comprehensive clinical features, pathological characteristics, and survival outcomes were recorded. Univariate and multivariate Cox proportional hazards models were used to evaluate overall survival (OS), cancer-specific survival (CSS), and disease-free survival (DFS). Of the 533 patients with advanced UTUC included, 161 (30.2%) received adjuvant chemotherapy. In the multivariate analysis, adjuvant chemotherapy was significantly associated with a reduced risk of overall death (hazard ratio (HR), 0.599; 95% confidence interval (CI), 0.419–0.857; p = 0.005), cancer-specific mortality (HR, 0.598; 95% CI, 0.391–0.914; p = 0.018), and cancer recurrence (HR, 0.456; 95% CI, 0.310–0.673; p < 0.001). The Kaplan–Meier survival analysis revealed that patients receiving adjuvant chemotherapy had significantly better five-year OS (64% vs. 50%, p = 0.002), CSS (70% vs. 62%, p = 0.043), and DFS (60% vs. 48%, p = 0.002) rates compared to those who did not receive adjuvant chemotherapy. In conclusion, adjuvant chemotherapy after RNU had significant therapeutic benefits on OS, CSS, and DFS in advanced UTUC.

PurposeThis study aimed to compare the oncological outcomes of patients with upper tract urothelial carcinoma (UTUC) without clinical lymph node metastasis (cN0) undergoing lymph node dissection (LND) during radical nephroureterectomy (NU).MethodsFrom the updated data of the Taiwan UTUC Collaboration Group, a total of 2726 UTUC patients were identified. We only include patients with ≥ pT2 stage and enrolled 658 patients. The Kaplan–Meier estimator and Cox proportional hazards model were used to analyze overall survival (OS), cancer-specific survival (CSS), disease-free survival (DFS), and bladder recurrence-free survival (BRFS) in LND (+) and LND (−) groups.ResultsA total of 658 patients were included and 463 patients without receiving LND and 195 patients receiving LND. From both univariate and multivariate survival analysis, there are no significant difference between LND (+) and LND (-) group in survival rate. In LND (+) group, 18.5% patients have pathological LN metastasis. After analyzing pN+ subgroup, it revealed worse CSS (p = 0.010) and DFS (p < 0.001) compared with pN0 patients.ConclusionsWe found no significant survival benefit related to LND in cN0 stage, ≥ pT2 stage UTUC, irrespective of the number of LNs removed, although pN+ affected cancer prognosis. However, from the result of pN (+) subgroup of LND (+) cohort analysis, it may be reasonable to not perform LND in patients with cT2N0 stage due to low positive predictive value of pN (+). In addition, performing LND may be considered for ureter cancer, which tends to cause lymphatic and hematogenous tumor spreading. Further large prospective studies are needed to validate our findings.

Background Mobile health apps have emerged as useful tools for patients and clinicians alike, sharing health information or assisting in clinical decision-making. Prostate cancer (PCa) risk calculator mobile apps have been introduced to assess risks of PCa and high-grade PCa (Gleason score ≥7). The Rotterdam Prostate Cancer Risk Calculator and Coral–Prostate Cancer Nomogram Calculator apps were developed from the 2 most-studied PCa risk calculators, the European Randomized Study of Screening for Prostate Cancer (ERSPC) and the North American Prostate Cancer Prevention Trial (PCPT) risk calculators, respectively. A systematic review has indicated that the Rotterdam and Coral apps perform best during the prebiopsy stage. However, the epidemiology of PCa varies among different populations, and therefore, the applicability of these apps in a Taiwanese population needs to be evaluated. This study is the first to validate the PCa risk calculator apps with both biopsy and prostatectomy cohorts in Taiwan. Objective The study’s objective is to validate the PCa risk calculator apps using a Taiwanese cohort of patients. Additionally, we aim to utilize postprostatectomy pathology outcomes to assess the accuracy of both apps with regard to high-grade PCa. Methods All male patients who had undergone transrectal ultrasound prostate biopsies in a single Taiwanese tertiary medical center from 2012 to 2018 were identified retrospectively. The probabilities of PCa and high-grade PCa were calculated utilizing the Rotterdam and Coral apps, and compared with biopsy and prostatectomy results. Calibration was graphically evaluated with the Hosmer-Lemeshow goodness-of-fit test. Discrimination was analyzed utilizing the area under the receiver operating characteristic curve (AUC). Decision curve analysis was performed for clinical utility. Results Of 1134 patients, 246 (21.7%) were diagnosed with PCa; of these 246 patients, 155 (63%) had high-grade PCa, according to the biopsy results. After confirmation with prostatectomy pathological outcomes, 47.2% (25/53) of patients were upgraded to high-grade PCa, and 1.2% (1/84) of patients were downgraded to low-grade PCa. Only the Rotterdam app demonstrated good calibration for detecting high-grade PCa in the biopsy cohort. The discriminative ability for both PCa (AUC: 0.779 vs 0.687; DeLong’s method: P Conclusions The Rotterdam and Coral apps can be applied to the Taiwanese cohort with accuracy. The Rotterdam app outperformed the Coral app in the prediction of PCa and high-grade PCa. Despite the small size of the prostatectomy cohort, both apps, to some extent, demonstrated the predictive capacity for true high-grade PCa, confirmed by the whole prostate specimen. Following our external validation, the Rotterdam app might be a good alternative to help detect PCa and high-grade PCa for Taiwanese men.

To develop a novel Taiwanese prostate cancer (PCa) risk model for predicting PCa, comparing its predictive performance with that of two well-established PCa risk calculator apps.1545 men undergoing prostate biopsies in a Taiwanese tertiary medical center between 2012 and 2019 were identified retrospectively. A five-fold cross-validated logistic regression risk model was created to calculate the probabilities of PCa and high-grade PCa (Gleason score ≧ 7), to compare those of the Rotterdam and Coral apps. Discrimination was analyzed using the area under the receiver operator characteristic curve (AUC). Calibration was graphically evaluated with the goodness-of-fit test. Decision-curve analysis was performed for clinical utility. At different risk thresholds to biopsy, the proportion of biopsies saved versus low- and high-grade PCa missed were presented.Overall, 278/1309 (21.2%) patients were diagnosed with PCa, and 181 out of 278 (65.1%) patients had high-grade PCa. Both our model and the Rotterdam app demonstrated better discriminative ability than the Coral app for detection of PCa (AUC: 0.795 vs 0.792 vs 0.697, DeLong's method: P 0.001) and high-grade PCa (AUC: 0.869 vs 0.873 vs 0.767, P 0.001). Using a ≥ 10% risk threshold for high-grade PCa to biopsy, our model could save 67.2% of total biopsies; among these saved biopsies, only 3.4% high-grade PCa would be missed.Our new logistic regression model, similar to the Rotterdam app, outperformed the Coral app in the prediction of PCa and high-grade PCa. Additionally, our model could save unnecessary biopsies and avoid missing clinically significant PCa in the Taiwanese population.

BACKGROUND Mobile health apps have emerged as useful tools for patients and clinicians alike, sharing health information or assisting in clinical decision-making. Prostate cancer (PCa) risk calculator mobile apps have been introduced to assess risks of PCa and high-grade PCa (Gleason score ≥7). The Rotterdam Prostate Cancer Risk Calculator and Coral–Prostate Cancer Nomogram Calculator apps were developed from the 2 most-studied PCa risk calculators, the European Randomized Study of Screening for Prostate Cancer (ERSPC) and the North American Prostate Cancer Prevention Trial (PCPT) risk calculators, respectively. A systematic review has indicated that the Rotterdam and Coral apps perform best during the prebiopsy stage. However, the epidemiology of PCa varies among different populations, and therefore, the applicability of these apps in a Taiwanese population needs to be evaluated. This study is the first to validate the PCa risk calculator apps with both biopsy and prostatectomy cohorts in Taiwan. OBJECTIVE The study’s objective is to validate the PCa risk calculator apps using a Taiwanese cohort of patients. Additionally, we aim to utilize postprostatectomy pathology outcomes to assess the accuracy of both apps with regard to high-grade PCa. METHODS All male patients who had undergone transrectal ultrasound prostate biopsies in a single Taiwanese tertiary medical center from 2012 to 2018 were identified retrospectively. The probabilities of PCa and high-grade PCa were calculated utilizing the Rotterdam and Coral apps, and compared with biopsy and prostatectomy results. Calibration was graphically evaluated with the Hosmer-Lemeshow goodness-of-fit test. Discrimination was analyzed utilizing the area under the receiver operating characteristic curve (AUC). Decision curve analysis was performed for clinical utility. RESULTS Of 1134 patients, 246 (21.7%) were diagnosed with PCa; of these 246 patients, 155 (63%) had high-grade PCa, according to the biopsy results. After confirmation with prostatectomy pathological outcomes, 47.2% (25/53) of patients were upgraded to high-grade PCa, and 1.2% (1/84) of patients were downgraded to low-grade PCa. Only the Rotterdam app demonstrated good calibration for detecting high-grade PCa in the biopsy cohort. The discriminative ability for both PCa (AUC: 0.779 vs 0.687; DeLong’s method: PPP=.128). CONCLUSIONS The Rotterdam and Coral apps can be applied to the Taiwanese cohort with accuracy. The Rotterdam app outperformed the Coral app in the prediction of PCa and high-grade PCa. Despite the small size of the prostatectomy cohort, both apps, to some extent, demonstrated the predictive capacity for true high-grade PCa, confirmed by the whole prostate specimen. Following our external validation, the Rotterdam app might be a good alternative to help detect PCa and high-grade PCa for Taiwanese men.