Testicular cancer is relatively uncommon and accounts for ,1% of all male tumors. However, it is the most common solid tumor in men between the ages of 20 and 34 years, and the global incidence has been steadily rising over the past several decades. Several risk factors for testicular cancer have been identified, including personal or family history of testicular cancer and cryptorchidism. Testicular germ cell tumors (GCTs) comprise 95% of malignant tumors arising in the testes and are categorized into 2 main histologic subtypes: seminoma and nonseminoma. Although nonseminoma is the more clinically aggressive tumor subtype, 5-year survival rates exceed 70% with current treatment options, even in patients with advanced or metastatic disease. Radical inguinal orchiectomy is the primary treatment for most patients with testicular GCTs. Postorchiectomy management is dictated by stage, histology, and risk classification; treatment options for nonseminoma include surveillance, systemic therapy, and nervesparing retroperitoneal lymph node dissection. Although rarely occurring, prognosis for patients with brain metastases remains poor, with .50% of patients dying within 1 year of diagnosis. This selection from the NCCN Guidelines for Testicular Cancer focuses on recommendations for the management of adult patients with nonseminomatous GCTs.

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Androgen deprivation therapy increases obesity, decreases insulin sensitivity and adversely alters lipid profiles. It may be associated with a greater incidence of diabetes and cardiovascular disease. The benefits of androgen deprivation therapy should be weighed against these and other potential harms. Little is known about the optimal strategy to mitigate the adverse metabolic effects of androgen deprivation therapy. Thus, we recommend an emphasis on existing strategies for screening and treatment that have been documented to reduce the risk of diabetes and cardiovascular disease in the general population.

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BACKGROUND Increasing prevalence of metastatic disease has been accompanied by increasing rates of surgical intervention. Current tools have poor to fair predictive performance for intermediate (90-d) and long-term (1-yr) mortality. OBJECTIVE To develop predictive algorithms for spinal metastatic disease at these time points and to provide patient-specific explanations of the predictions generated by these algorithms. METHODS Retrospective review was conducted at 2 large academic medical centers to identify patients undergoing initial operative management for spinal metastatic disease between January 2000 and December 2016. Five models (penalized logistic regression, random forest, stochastic gradient boosting, neural network, and support vector machine) were developed to predict 90-d and 1-yr mortality. RESULTS Overall, 732 patients were identified with 90-d and 1-yr mortality rates of 181 (25.1%) and 385 (54.3%), respectively. The stochastic gradient boosting algorithm had the best performance for 90-d mortality and 1-yr mortality. On global variable importance assessment, albumin, primary tumor histology, and performance status were the 3 most important predictors of 90-d mortality. The final models were incorporated into an open access web application able to provide predictions as well as patient-specific explanations of the results generated by the algorithms. The application can be found at https://sorg-apps.shinyapps.io/spinemetssurvival/ CONCLUSION Preoperative estimation of 90-d and 1-yr mortality was achieved with assessment of more flexible modeling techniques such as machine learning. Integration of these models into applications and patient-centered explanations of predictions represent opportunities for incorporation into healthcare systems as decision tools in the future.