Faculty Bibliography

INTRODUCTION/BACKGROUND:We aimed to study the association of ethnicity on semen parameters and hormones in patients presenting with infertility. METHODS:Data from men presenting for infertility assessment were prospectively collected and retrospectively reviewed. Demographic and clinical history was self-reported. Semen analysis included volume, count, motility, morphology, and vitality. The 2010 World Health Organization cutoffs were used. Baseline total testosterone and follicle-stimulating hormone (FSH) levels were recorded. Ethnicity data was classified as Caucasian, African Canadian, Asian, Indo-Canadian, Native Canadian, Hispanic, and Middle Eastern. All patients with complete data were included and statistical analysis was performed. RESULTS:A total of 9079 patients were reviewed, of which 3956 patients had complete data. Of these, 839 (21.2%) were azoospermic. After adjusting for age, African Canadians (odds ratio [OR] 1.70; 95% confidence interval [CI] 1.28-2.25) and Asians (1.34; 95% CI 1.11-1.62) were more likely to be azoospermic compared to Caucasians. Similarly, African Canadians (OR 1.75; 95% CI 1.33-2.29) were more likely to be oligospermic and Asians (OR 0.82; 95% CI 0.70-0.97) less likely to be oligospermic. Low volume was found in African Canadian (OR 1.42; 95% CI 1.05-1.91), Asians (OR 1.23; 95% CI 1.01-1.51), and Indo-Canadians (OR 1.47; 95% CI 1.01-2.13). Furthermore, Asians (OR 0.73; 95% CI 0.57-0.93) and Hispanics (OR 0.58; 95% CI 034-0.99) were less likely to have asthenospermia. Asians (OR 0.73; 95% CI 0.57-0.94) and Indo-Canadians (OR 0.58; 95% CI 0.35-0.99) were less likely to have teratozospermia. No differences were seen for vitality. No differences were seen for FSH levels, however, Asians (p<0.01) and Indo-Canadians (p<0.01) were more likely to have lower testosterone. CONCLUSIONS:Our study illustrates that variations in semen analyses and hormones exist in men with infertility. This may provide insight into the workup and management for infertile men from different ethnicities.

INTRODUCTION:Diabetes has been associated with worse survival outcomes in various malignancies; however, there are conflicting data in kidney cancer. Determining whether diabetes is associated with survival in kidney cancer may help guide treatment in a comorbid patient population. METHODS:We used the Canadian Kidney Cancer information system database to identify patients undergoing partial or radical nephrectomy between 1989 and 2017 for localized renal cell carcinoma at 16 institutions across Canada. We derived inverse probability of treatment weights (IPTW) from a propensity score model based on various clinical, surgical, and pathological characteristics. We used Cox proportional hazard models to evaluate the association between diabetes and cancer-specific and overall survival, in the sample weighted by the IPTW. RESULTS:4828 patients met inclusion criteria, of whom 948 (19.6%) were diabetic. Median follow-up in those without death was 26.6 months (interquartile range 9.7-53.8). Among the entire cohort, 901 deaths were from any cause, and 299 deaths from kidney cancer. Before propensity score methods, diabetics were older, more likely to have comorbidities and clear cell histopathology. After propensity score adjustment, all characteristics were balanced between groups (standardized difference <0.10). IPTW-adjusted Cox proportional hazard models demonstrated no significant association between diabetes and cancer-specific (hazard ratio 1.13, 95% confidence interval 0.78-1.62), or overall survival (hazard ratio 1.14, 95% confidence interval 0.94-1.38). CONCLUSIONS:Our multi-centre study found that diabetes and nondiabetics have similar survival following nephrectomy for kidney cancer.

PURPOSE:Active surveillance (AS) for testicular nonseminomatous germ cell tumors (NSGCT) is widely used. Although there is no consensus for optimal treatment at relapse on surveillance, globally patients typically receive chemotherapy. We describe treatment of relapses in our non-risk-adapted NSGCT AS cohort and highlight selective use of primary retroperitoneal lymph node dissection (RPLND). METHODS:From December 1980 to December 2015, 580 patients with clinical stage I NSGCT were treated with AS, and 162 subsequently relapsed. First-line treatment was based on relapse site and extent. Logistic regression was used to explore factors associated with need for multimodal therapy on AS relapse. RESULTS:= .008) in patients undergoing RPLND. When RPLND was performed with normal markers, 82% required no further treatment. Second relapse occurred in 30 of 162 patients (18.5%). With median follow-up of 7.6 years, there were five deaths (3.1% of AS relapses, but 0.8% of whole AS cohort) from NSGCT or treatment complications. CONCLUSION:The retroperitoneum is the most common site of relapse in clinical stage I NSGCT on AS. Most are cured by single-modality treatment. RPLND should be considered for relapsed patients, especially those with disease limited to the retroperitoneum and normal markers, as an option to avoid chemotherapy.

OBJECTIVES:Metformin has been associated with improved survival outcomes in various malignancies. However, studies in kidney cancer are conflicting. We performed a systematic review and meta-analysis to evaluate the association between metformin and kidney cancer survival. MATERIALS AND METHODS:We searched Medline and EMBASE databases from inception to June 2017 to identify studies evaluating the association between metformin use and kidney cancer survival outcomes. We evaluated risk of bias with the Newcastle-Ottawa scale. We pooled hazard ratios (HRs) for recurrence-free, progression-free, cancer-specific, and overall survival using random effects models, and explored heterogeneity with metaregression. We evaluated publication bias through Begg's and Egger's tests, and the trim and fill procedure. RESULTS:We identified 9 studies meeting inclusion criteria, collectively involving 7426 patients. Five studies were at low risk of bias. The direction of association for metformin use was toward benefit for recurrence-free survival (HR, 0.99; 95% confidence interval [CI], 0.36-2.74), progression-free survival (pooled HR, 0.84; 95% CI, 0.66-1.07), cancer-specific (pooled HR, 0.72; 95% CI, 0.48-1.09), and overall survival (pooled HR, 0.73; 95% CI, 0.50-1.09), though none reached statistical significance. Metaregression found no study-level characteristic to be associated with the effect size, and there was no strong evidence of publication bias for any outcome. CONCLUSIONS:There is no evidence of a statistically significant association between metformin use and any survival outcome in kidney cancer. We discuss the potential for bias in chemoprevention studies and provide recommendations to reduce bias in future studies evaluating metformin in kidney cancer.

BACKGROUND:Postchemotherapy retroperitoneal lymph node dissection (pcRPLND) is indicated in testicular cancer patients with normalised or plateaued serum tumour markers and residual retroperitoneal lesions >1cm. Challenges remain in predicting postchemotherapy residual mass (pcRM) histology, which may lead to unnecessary surgery. OBJECTIVE:To develop an accurate model to predict pcRM histology in patients with nonseminomatous germ cell tumours (NSGCTs). DESIGN, SETTING, AND PARTICIPANTS:A retrospective review of 335 patients undergoing pcRPLND for metastatic NSGCTs to develop a model to predict benign histology in retroperitoneal pcRM. Our model was compared with others and externally validated. INTERVENTION:Chemotherapy and pcRPLND. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS:Multivariable logistic regression to evaluate the presence of benign histology, and fractional polynomials to allow for a nonlinear association between continuous variables and the outcome. The final Princess Margaret model (PMM) was selected based on the number of variables used, reliability, and discriminative capacity to predict benign pcRM. RESULTS AND LIMITATIONS:PMM included the presence of teratoma in the orchiectomy, prechemotherapy α-fetoprotein, prechemotherapy mass size, and change in mass size during chemotherapy. Model specificity was 99.3%. Compared with Vergouwe et al's model, PMM had significantly better accuracy (C statistic 0.843 vs 0.783). PMM appropriately identified a larger number of patients for whom pcRPLND can safely be avoided (13.9% vs 0%). Validated in external cohorts, the model retained high discrimination (C statistic 0.88 and 0.80). Larger and prospective studies are needed to further validate this model. CONCLUSIONS:Our clinical model, externally validated, showed improved discriminative ability in predicting pcRM histology when compared with other models. The higher accuracy and reduced number of variables make this a novel and appealing model to use for patient counselling and treatment strategies. PATIENT SUMMARY:Princess Margaret model accurately predicted postchemotherapy benign histology. These results might have clinical impact by avoiding unnecessary retroperitoneal lymph node dissection and consequently changing the paradigm of advanced testicular cancer treatment.

PURPOSE:After chemotherapy, approximately 50% of patients with metastatic testicular germ cell tumors (GCTs) who undergo retroperitoneal lymph node dissections (RPNLDs) for residual masses have fibrosis. Radiomics uses image processing techniques to extract quantitative textures/features from regions of interest (ROIs) to train a classifier that predicts outcomes. We hypothesized that radiomics would identify patients with a high likelihood of fibrosis who may avoid RPLND. PATIENTS AND METHODS:Patients with GCT who had an RPLND for nodal masses > 1 cm after first-line platinum chemotherapy were included. Preoperative contrast-enhanced axial computed tomography images of retroperitoneal ROIs were manually contoured. Radiomics features (n = 153) were used to train a radial basis function support vector machine classifier to discriminate between viable GCT/mature teratoma versus fibrosis. A nested 10-fold cross-validation protocol was used to determine classifier accuracy. Clinical variables/restricted size criteria were used to optimize the classifier. RESULTS:Seventy-seven patients with 102 ROIs were analyzed (GCT, 21; teratoma, 41; fibrosis, 40). The discriminative accuracy of radiomics to identify GCT/teratoma versus fibrosis was 72 ± 2.2% (area under the curve [AUC], 0.74 ± 0.028); sensitivity was 56.2 ± 15.0%, and specificity was 81.9 ± 9.0% ( P = .001). No major predictive differences were identified when data were restricted by varying maximal axial diameters (AUC range, 0.58 ± 0.05 to 0.74 ± 0.03). The prediction algorithm using clinical variables alone identified an AUC of 0.76. When these variables were added to the radiomics signature, the best performing classifier was identified when axial masses were limited to diameter < 2 cm (accuracy, 88.2 ± 4.4; AUC, 0.80 ± 0.05; P = .02). CONCLUSION:A predictive radiomics algorithm had a discriminative accuracy of 72% that improved to 88% when combined with clinical predictors. Additional independent validation is required to assess whether radiomics allows patients with a high predicted likelihood of fibrosis to avoid RPLND.