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Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.

Testosterone is the most important hormone in male health. Aging is characterized by testosterone deficiency due to decreasing testosterone levels associated with low testicular production, genetic factors, adiposity, and illness. Low testosterone levels in men are associated with sexual dysfunction (low sexual desire, erectile dysfunction), reduced skeletal muscle mass and strength, decreased bone mineral density, increased cardiovascular risk and alterations of the glycometabolic profile. Testosterone replacement therapy (TRT) shows several therapeutic effects while maintaining a good safety profile in hypogonadal men. TRT restores normal levels of serum testosterone in men, increasing libido and energy level and producing beneficial effects on bone density, strength and muscle as well as yielding cardioprotective effects. Nevertheless, TRT could be contraindicated in men with untreated prostate cancer, although poor findings are reported in the literature. In addition, different potential side effects, such as polycythemia, cardiac events and obstructive sleep apnea, should be monitored. The aim of our review is to provide an updated background regarding the pros and cons of TRT, evaluating its role and its clinical applicability in different domains.

Although appreciable attempts in screening and diagnostic approaches have been achieved, prostate cancer (PCa) remains a widespread malignancy, representing the second leading cause of cancer-related death in men. Drugs currently used in PCa therapy initially show a potent anti-tumor effect, but frequently induce resistance and PCa progresses toward metastatic castration-resistant forms (mCRPC), virtually incurable. Liquid biopsy has emerged as an attractive and promising strategy complementary to invasive tissue biopsy to guide PCa diagnosis and treatment. Liquid biopsy shows the ability to represent the tumor microenvironment, allow comprehensive information and follow-up the progression of the tumor, enabling the development of different treatment strategies as well as permitting the monitoring of therapy response. Liquid biopsy, indeed, is endowed with a significant potential to modify PCa management. Several blood biomarkers could be analyzed for diagnostic, prognostic and predictive purposes, including circulating tumor cells (CTCs), extracellular vesicles (EVs), circulating tumor DNA (ctDNA) and RNA (ctRNA). In addition, several other body fluids may be adopted (i.e., urine, sperm, etc.) beyond blood. This review dissects recent advancements and future perspectives of liquid biopsies, highlighting their strength and weaknesses in PCa management.

Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.

Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.

Mucins are a family of high-molecular-weight glycoproteins. MUC1 is widely studied for its role in distinct types of cancers. In many human epithelial malignancies, MUC1 is frequently overexpressed, and its intracellular activities are crucial for cell biology. MUC1 overexpression can enhance cancer cell proliferation by modulating cell metabolism. When epithelial cells lose their tight connections, due to the loss of polarity, the mucins become dispersed on both sides of the epithelial membrane, leading to an abnormal mucin interactome with the membrane. Tumor-related MUC1 exhibits certain features, such as loss of apical localization and aberrant glycosylation that might cause the formation of tumor-related antigen epitopes. Renal cell carcinoma (RCC) accounts for approximately 3% of adult malignancies and it is the most common kidney cancer. The exact role of MUC1 in this tumor is unknown. Evidence suggests that it may play a role in several oncogenic pathways, including proliferation, metabolic reprogramming, chemoresistance, and angiogenesis. The purpose of this review is to explore the role of MUC1 and the meaning of its overexpression in epithelial tumors and in particular in RCC.

The incidence of testicular cancer is steadily increasing over the past several decades in different developed countries. If on one side better diagnosis and treatment have shone a light on this disease, on the other side, differently from other malignant diseases, few risk factors have been identified. The reasons for the increase in testicular cancer are however unknown while risk factors are still poorly understood. Several studies have suggested that exposure to various factors in adolescence as well as in adulthood could be linked to the development of testicular cancer. Nevertheless, the role of environment, infections, and occupational exposure are undoubtedly associated with an increase or a decrease in this risk. The aim of this narrative review is to summarize the most recent evidence regarding the risk factors associated with testicular cancer, starting from the most commonly evaluated (cryptorchidism, family history, infections) to the newer identified and hypothesized risk factors.

Another study showed that the estimated lifetime risk of PCa is as high as 20% in BRCA2 and 9.5% in BRCA1 mutation carriers [13]. [...]BRCA germline mutations have been associated with more aggressive disease and poorer clinical outcomes, reporting higher Gleason score, nodal involvement and metastatic disease at diagnosis. Clinical outcomes appear to be even worse when radiotherapy was chosen as the primary treatment, with 57% and 39% of BRCA1-2 carriers free from metastasis at 5 and 10 years compared to the 91% and 80% of non-carriers [15]. [...]the PROREPAIR-B study has shown that patients with metastatic PCa and germline BRCA2 mutation become resistant to androgen-deprivation therapy faster than non-carriers (13.2 months versus 28 months), with a halved CSS [16]. [...]the TALAPRO-1 phase II trial (NCT03148795) is currently evaluating Talazoparib in mCRPC patients, reporting an initial ORR of 43.9% for BRCA1-2 mutations carriers compared to 11.8% for ATM mutations carriers.

In the past decades, several treatments have been proposed for the management of metastatic renal cell carcinoma (mRCC). Among these, cytoreductive nephrectomy (CN) represents a controversial and open issue in the era of targeted therapy and novel immunotherapy with immune checkpoint inhibitors. Two important studies, CARMENA and SURTIME, analyzed therapy with sunitinib with or without CN, and immediate CN followed by sunitinib versus a deferred CN after three cycles of sunitinib, respectively. CARMENA showed the non-inferiority of sunitinib alone versus sunitinib plus CN, whereas SURTIME showed no difference in progression-free survival (PFS), but a better median OS among patients with deferred CN. Therefore, more prospective clinical trials and appropriate patient identification are necessary to support CN in this new scenario. This review provides a snapshot of the current evidence for CN in mRCC, discusses the management strategies, and offers perspectives on the direction of future research.

Artificial intelligence is highly regarded as the most promising future technology that will have a great impact on healthcare across all specialties. Its subsets, machine learning, deep learning, and artificial neural networks, are able to automatically learn from massive amounts of data and can improve the prediction algorithms to enhance their performance. This area is still under development, but the latest evidence shows great potential in the diagnosis, prognosis, and treatment of urological diseases, including bladder cancer, which are currently using old prediction tools and historical nomograms. This review focuses on highly significant and comprehensive literature evidence of artificial intelligence in the management of bladder cancer and investigates the near introduction in clinical practice.