Reviews in urology最新文献

Active surveillance (AS) is a safe and reasonable management strategy for many patients with small renal masses (SRM) suspicious for a clinical T1a renal cell carcinoma based on excellent metastasis-free and cancer-specific survival. However, the expansion of robotic extirpation of SRM has outpaced the adoption of AS, resulting in the possibility of overtreatment for select patients with SRM, especially the elderly and comorbid. In this review of AS for SRM, with a focus on the Delayed Intervention and Surveillance for Small Renal Masses (DISSRM) Registry, we detail the rationale behind AS, review lessons learned from the past decades of literature, and offer suggestions for appropriate patient selection and follow-up. An improved understanding of the data supporting AS will empower physicians and patients to more comfortably pursue AS to avoid over-treatment and provide individualized care to patients with SRM.

With increasing treatment options available, the management of locally advanced and metastatic prostate cancer (PCa) is growing more complex, nuanced, and individualized. Strategies for combining surgery, radiation, chemotherapy, and androgen deprivation therapy (ADT) continue to evolve, as do ADT and immunotherapy options. Additionally, multiple adjunctive agents for metastatic PCa have been recently approved or are pending approval. As the number of locally advanced and metastatic prostate cancers being diagnosed rises, so does the need to consider patients' clinical situations and personal preferences. This review discusses current and potential future approaches to managing locally advanced and metastatic PCa.

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients.

Orchialgia is a common urologic complaint with a myriad of etiologies. Workup for orchialgia requires a broad differential diagnosis and a thorough understanding of relevant anatomy. We report the case of a 43-year-old man who presented to a urologist with right testicular pain. Following a negative workup, the patient received a spermatic cord block for therapeutic and diagnostic purposes. Two months after the block, the patient returned with new complaints of ipsilateral inner thigh paresthesias, suggesting a pathologic process proximal to the genital branch of the genitofemoral nerve. A subsequent MRI of the lumbosacral spine revealed a paraspinal mass involving nerve roots at L1-2. We highlight the utility of the spermatic cord block and its role in the diagnosis of a paraspinal tumor as an uncommon cause of orchialgia.

Exogenous testosterone administration decreases intratesticular testosterone (ITT) significantly, an essential hormone for spermatogenesis. Therefore, treatment of patients with hypogonadotropic hypogonadism (HH) who desire infertility can be challenging. These patients are treated with recombinant follicle-stimulating hormone (FSH), clomiphene citrate, and human chorionic gonadotropin (hCG) to increase their ITT. However, there is no approved serum biomarker for ITT and it can only be measured via invasive testicular biopsy or aspiration. Previous authors have speculated that serum 17-hydroxyprogestrone (17-OHP) can be used as serum biomarker for ITT. In our case report, we demonstrate increase in 17-OHP associated with spermatogenesis after commencing treatment for infertility in patient with HH.

This article examines via multivariate analysis the associations between demographic factors and systemic diseases on stone risk parameters in a stone-forming population. A retrospective chart review of adult stone formers who completed 24-hour urine collections from April 2004 through August 2015 was performed. Data was collected on age, sex, race, body mass index (BMI), and diagnoses of diabetes and hypertension. CT imaging and renal/abdominal ultrasonography (within ± 66 mo) were reviewed for diagnosis of fatty liver disease. Statistical analysis included Pearson and Spearman correlation analysis, and linear and logistic regression analyses, both univariate and multivariate. Five hundred eighty-nine patients were included. Numerous urinary parameters were significant in association with demographic factors or systemic diseases in a multivariate analysis. Older age was associated with decreased calcium (Ca) excretion (P = 0.0214), supersaturation of calcium oxalate (SSCaOx; P = 0.0262), supersaturation of calcium phosphate (SSCaP; P < 0.0001), and urinary pH (P = 0.0201). Men excreted more Ca (P = 0.0015) and oxalate (Ox; P = 0.0010), had lower urine pH (P = 0.0269), and higher supersaturation of uric acid (SSUA; P < 0.0001) than women. Blacks had lower urine volume (P = 0.0023), less Ca excretion (P = 0.0142), less Ox excretion (P = 0.0074), and higher SSUA (P = 0.0049). Diabetes was associated with more Ox excretion (P < 0.0001), lower SSCaP (P = 0.0068), and lower urinary pH (P = 0.0153). There were positive correlations between BMI and Ca excretion (P = 0.0386), BMI and Ox excretion (P = 0.0177), and BMI and SSUA (P = 0.0045). These results demonstrate that demographic factors and systemic disease are independently associated with numerous risk factors for kidney stones. The mechanisms responsible for these associations and disparities (racial differences) need to be further elucidated.