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Simple Summary: Medical gas plasma is a new modality in cancer treatment showing favorable results in preclinical and clinical trials; however, the cascade of molecular events underlying selective killing of cancer cells has not been fully elucidated. This study examines the hypothesis that cell death induced by gas plasma is mediated by a universal molecular mechanism. Using high-throughput data from eight transcriptomic studies on patient-derived prostate cultures, melanoma, breast, lymphoma, and lung cancer cells, we implemented two computational methodologies for re-analysis of single cohorts as well as meta-analysis of multi-cohort gene expression data. The results provide converging insights into the induction of immunogenic cell death in human tumors, reinforcing the potential of plasma treatment as an emerging immunotherapeutic approach in oncology. The therapeutic potential of cold physical gas plasma operated at atmospheric pressure in oncology has been thoroughly demonstrated in numerous preclinical studies. The cytotoxic effect on malignant cells has been attributed mainly to biologically active plasma-generated compounds, namely, reactive oxygen and nitrogen species. The intracellular accumulation of reactive oxygen and nitrogen species interferes strongly with the antioxidant defense system of malignant cells, activating multiple signaling cascades and inevitably leading to oxidative stress-induced cell death. This study aims to determine whether plasma-induced cancer cell death operates through a universal molecular mechanism that is independent of the cancer cell type. Using whole transcriptome data, we sought to investigate the activation mechanism of plasma-treated samples in patient-derived prostate cell cultures, melanoma, breast, lymphoma, and lung cancer cells. The results from the standardized single-cohort gene expression analysis and parallel multi-cohort meta-analysis strongly indicate that plasma treatment globally induces cancer cell death through immune-mediated mechanisms, such as interleukin signaling, Toll-like receptor cascades, and MyD88 activation leading to pro-inflammatory cytokine release and tumor antigen presentation. [ABSTRACT FROM AUTHOR]

Simple Summary: Prostate cancer is a common and challenging disease among men, driving researchers to find better ways to understand and manage it. The understanding of two specific types, cribriform and intraductal prostate cancer (IDC-P), has evolved significantly over the years. This review aims to help pathologists differentiate between cribriform prostate cancer and IDC-P, and addresses current recommendations. Recent studies show that including these features in decision-making tools enhances predictions of cancer recurrence, spread, and patient outcomes. Future research should further focus on their pathological and molecular aspects to improve risk stratification, treatment approaches, and patient care. Over the years, our understanding of cribriform and intraductal prostate cancer (PCa) has evolved significantly, leading to substantial changes in their classification and clinical management. This review discusses the histopathological disparities between intraductal and cribriform PCa from a diagnostic perspective, aiming to aid pathologists in achieving accurate diagnoses. Furthermore, it discusses the ongoing debate surrounding the different recommendations between ISUP and GUPS, which pose challenges for practicing pathologists and complicates consensus among them. Recent studies have shown promising results in integrating these pathological features into clinical decision-making tools, improving predictions of PCa recurrence, cancer spread, and mortality. Future research efforts should focus on further unraveling the biological backgrounds of these entities and their implications for clinical management to ultimately improve PCa patient outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: Stereotactic body radiation therapy (SBRT) can be safely delivered for the treatment of localized prostate cancer. High-dose-per-fraction treatments require precise and rapid delivery of the radiation dose to the tumor, and any potential margin expansion must be balanced against the risk of increased toxicity. Since significant intrafraction prostate displacement can have a relevant impact on an extreme-hypofractionated regimen, real-time monitoring has emerged as a strategy to avoid the excessive exposure of healthy tissue or insufficient target coverage, but it requires continuous investment of resources and time. In real-world clinical practice, the utilization of organ motion management devices is not always feasible for all patients. Thus, our strategy has often been to mitigate the impact of intrafraction motion by shortening the delivery time without compromising the quality of the treatment. This was achieved through optimal patient preparation, flattening filter-free (FFF) beams, adequate margins, and a reduction in the number of arcs in the VMAT technique. This study aimed to comprehensively present data on treatment optimization in linac-based SBRT for localized prostate cancer at a single institution. Moreover, the dosimetric quality and treatment efficiency of single-arc (SA) versus dual-arc (DA) VMAT planning and delivery approaches were compared. Re-optimization was performed on twenty low-to-intermediate-risk- (36.25 Gy in 5 fractions) and twenty high-risk (42.7 Gy in 7 fractions) prostate plans initially administered with the DA FFF-VMAT technique in 2021. An SA approach was adopted, incorporating new optimization parameters based on increased planning and clinical experience. Analysis included target coverage, organ-at-risk (OAR) sparing, treatment delivery time, and the pre-treatment verification's gamma analysis-passing ratio. The SA optimization technique has consistently produced superior plans. Rectum and bladder mean doses were significantly reduced, and comparable target coverage and homogeneity were achieved in order to maintain a urethra protection strategy. The mean SA treatment delivery time was reduced by 22%; the mean monitor units increased due to higher plan complexity; and dose measurements demonstrated optimal agreement with calculations. The substantial reduction in treatment delivery time decreased the probability of prostate motion beyond the applied margins, suggesting potential decrease in treatment-related toxicity and improved target coverage in prostate SBRT. Further investigations are warranted to assess the long-term clinical outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: Auto contouring driven by artificial intelligence can improve the workflow of radiotherapy by accelerating the contouring process. However, quality assurance of artificial intelligence-based tools is necessary for ensuring safety and efficacy in a clinical practice. In this study investigated the geometric accuracy of structural contours created by a commercial software for autocontouring based on artificial intelligence using well established metrics. In particular, the impact on the radiotherapy treatment plan quality from the adoption of artificial intelligence generated contours was investigated. Our results show that the combination of automatically generated contours and careful review by a clinical radiation oncologist results in time saving without affecting the quality of treatment plan. In conclusion, after quality checks that involve both geometric accuracy as well as dosimetric impact, contouring based on AI can be safely adopted in clinical practice. Purpose: When autocontouring based on artificial intelligence (AI) is used in the radiotherapy (RT) workflow, the contours are reviewed and eventually adjusted by a radiation oncologist before an RT treatment plan is generated, with the purpose of improving dosimetry and reducing both interobserver variability and time for contouring. The purpose of this study was to evaluate the results of application of a commercial AI-based autocontouring for RT, assessing both geometric accuracies and the influence on optimized dose from automatically generated contours after review by human operator. Materials and Methods: A commercial autocontouring system was applied to a retrospective database of 40 patients, of which 20 were treated with radiotherapy for prostate cancer (PCa) and 20 for head and neck cancer (HNC). Contours resulting from AI were compared against AI contours reviewed by human operator and human-only contours using Dice similarity coefficient (DSC), Hausdorff distance (HD), and relative volume difference (RVD). Dosimetric indices such as Dmean, D0.03cc, and normalized plan quality metrics were used to compare dose distributions from RT plans generated from structure sets contoured by humans assisted by AI against plans from manual contours. The reduction in contouring time obtained by using automated tools was also assessed. A Wilcoxon rank sum test was computed to assess the significance of differences. Interobserver variability of the comparison of manual vs. AI-assisted contours was also assessed among two radiation oncologists for PCa. Results: For PCa, AI-assisted segmentation showed good agreement with expert radiation oncologist structures with average DSC among patients ≥ 0.7 for all structures, and minimal radiation oncology adjustment of structures (DSC of adjusted versus AI structures ≥ 0.91). For HNC, results of comparison between manual and AI contouring varied considerably e.g., 0.77 for oral cavity and 0.11–0.13 for brachial plexus, but again, adjustment was generally minimal (DSC of adjusted against AI contours 0.97 for oral cavity, 0.92–0.93 for brachial plexus). The difference in dose for the target and organs at risk were not statistically significant between human and AI-assisted, with the only exceptions of D0.03cc to the anal canal and Dmean to the brachial plexus. The observed average differences in plan quality for PCa and HNC cases were 8% and 6.7%, respectively. The dose parameter changes due to interobserver variability in PCa were small, with the exception of the anal canal, where large dose variations were observed. The reduction in time required for contouring was 72% for PCa and 84% for HNC. Conclusions: When an autocontouring system is used in combination with human review, the time of the RT workflow is significantly reduced without affecting dose distribution and plan quality. [ABSTRACT FROM AUTHOR]

Simple Summary: Prostate cancer is a serious health problem for men around the world, and it is often caused by inflammation in the prostate gland. The authors of this article explore how a group of drugs called nonsteroidal anti-inflammatory drugs (NSAIDs) can help prevent and treat this disease by reducing inflammation and interfering with the growth and survival of cancer cells. We describe the various mechanisms by which NSAIDs can affect prostate cancer, such as triggering cell death, halting cell division, and cutting off blood supply. We also discuss the evidence from previous studies that support the use of NSAIDs as anti-cancer agents, either alone or in combination with other treatments. NSAIDs have a lot of promise as potential drugs for prostate cancer, but more research to understand their optimal dosage, timing, and safety is needed. Prostate cancer (PC) is the second most common type of cancer and the leading cause of death among men worldwide. Preventing the progression of cancer after treatments such as radical prostatectomy, radiation therapy, and hormone therapy is a major concern faced by prostate cancer patients. Inflammation, which can be caused by various factors such as infections, the microbiome, obesity and a high-fat diet, is considered to be the main cause of PC. Inflammatory cells are believed to play a crucial role in tumor progression. Therefore, nonsteroidal anti-inflammatory drugs along with their effects on the treatment of inflammation-related diseases, can prevent cancer and its progression by suppressing various inflammatory pathways. Recent evidence shows that nonsteroidal anti-inflammatory drugs are effective in the prevention and treatment of prostate cancer. In this review, we discuss the different pathways through which these drugs exert their potential preventive and therapeutic effects on prostate cancer. [ABSTRACT FROM AUTHOR]

Simple Summary: Our study provides an overview of the current state of artificial intelligence applications in urooncology and explores potential future advancements in this field. With remarkable progress already achieved, artificial intelligence has revolutionized urooncology by facilitating image analysis, grading, biomarker research, and treatment planning. We also discuss types of artificial intelligence and their possible applications in the management of cancers such as prostate, kidney, bladder, and testicular. As artificial intelligence technology continues to evolve, it holds immense promise for further advancing urooncology and enhancing the care of patients with cancer. Introduction: Artificial intelligence is transforming healthcare by driving innovation, automation, and optimization across various fields of medicine. The aim of this study was to determine whether artificial intelligence (AI) techniques can be used in the diagnosis, treatment planning, and monitoring of urological cancers. Methodology: We conducted a thorough search for original and review articles published until 31 May 2022 in the PUBMED/Scopus database. Our search included several terms related to AI and urooncology. Articles were selected with the consensus of all authors. Results: Several types of AI can be used in the medical field. The most common forms of AI are machine learning (ML), deep learning (DL), neural networks (NNs), natural language processing (NLP) systems, and computer vision. AI can improve various domains related to the management of urologic cancers, such as imaging, grading, and nodal staging. AI can also help identify appropriate diagnoses, treatment options, and even biomarkers. In the majority of these instances, AI is as accurate as or sometimes even superior to medical doctors. Conclusions: AI techniques have the potential to revolutionize the diagnosis, treatment, and monitoring of urologic cancers. The use of AI in urooncology care is expected to increase in the future, leading to improved patient outcomes and better overall management of these tumors. [ABSTRACT FROM AUTHOR]

Simple Summary: The imaging of prostate cancer has evolved over the last few decades with molecular imaging, especially Positron Emission Tomography (PET) replacing conventional CT and MRI in patients with prostate cancer recurrence. These recent advances in PET imaging have allowed for a better localization of disease recurrence, and a better characterization of disease extent. Beyond localization and characterization, new uses for PET imaging have emerged, with PET agents now playing a role in assessing the response to treatment at the receptor level, and also helping to determine patient prognosis and survival. Prostate cancer is the most common non-dermatologic cancer in men, and one of the leading causes of cancer-related mortality. The incidence of prostate cancer increases precipitously after the age of 65 and demonstrates variable aggressiveness, depending on its grade and stage at diagnosis. Despite recent advancements in prostate cancer treatment, recurrence is seen in 25% of patients. Advancements in prostate cancer Positron Emission Tomography (PET) molecular imaging and recent United States Food and Drug Administration (FDA) approvals have led to several new options for evaluating prostate cancer. This manuscript will review the commonly used molecular imaging agents, with an emphasis on Fluorine-18 fluciclovine (Axumin) and PSMA-ligand agents, including their protocols, imaging interpretation, and pitfalls. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer cells and mesenchymal stem cells (MSCs) secrete C-C motif chemokine ligand 2 (CCL2), a small protein that attracts tumor-associated macrophages (TAMs), which promotes malignant progression, such as drug resistance and metastasis. In addition, MSCs can also be recruited by CCL2; thus, a local milieu consisting of tumor cells, MSCs, and TAMs can promote tumor progression via a CCL2-dependent paracrine. The aim of the current study was to examine the functions of endogenous CCL2 of MSCs in cancer biology. Using a genetic engineering technique, we blocked the CCL2 expression in murine bone marrow-derived MSCs (CCL2 KO MSCs) and analyzed the effects on cancer progression. We found that CCL2 KO MSCs showed anti-tumor function when injected with murine prostate cancer cells into mice. The inhibitory effect was associated with an increase of CD45+CD11b+ mononuclear myeloid cells in tumors. Background: MSCs are known to secrete abundant CCL2, which plays a crucial role in recruiting TAMs, promoting tumor progression. It is important to know whether disrupting MSC-derived CCL2 affects tumor growth. Methods: Murine bone marrow-derived MSCs were characterized by their surface markers and differentiation abilities. Proliferation and migration assays were performed in order to evaluate the functions of MSCs on cancer cells. CCL2 expression in MSCs was reduced by small interfering RNA (siRNA) or completely disrupted by CRISPR/Cas9 knockout (KO) approaches. An immune-competent syngeneic murine model of prostate cancer was applied in order to assess the role of tumor cell- and MSC-derived CCL2. The tumor microenvironment was analyzed to monitor the immune profile. Results: We confirmed that tumor cell-derived CCL2 was crucial for tumor growth and MSCs migration. CCL2 KO MSCs inhibited the migration of the monocyte/macrophage but not the proliferation of tumor cells in vitro. However, the mice co-injected with tumor cells and CCL2 KO MSCs exhibited anti-tumor effects when compared with those given tumor cell alone and with control MSCs, partly due to increased infiltration of CD45+CD11b+Ly6G− mononuclear myeloid cells. Conclusions: Disruption of MSC-derived CCL2 enhances anti-tumor functions in an immune-competent syngeneic mouse model for prostate cancer. [ABSTRACT FROM AUTHOR]

Research in the last decade has clearly revealed a critical role of prostate cancer stem cells (PCSCs) in prostate cancer (PC). Prostate stem cells (PSCs) reside in both basal and luminal layers, and are the target cells of oncogenic transformation, suggesting a role of PCSCs in PC initiation. Mutations in PTEN, TP53, and RB1 commonly occur in PC, particularly in metastasis and castration-resistant PC. The loss of PTEN together with Ras activation induces partial epithelial–mesenchymal transition (EMT), which is a major mechanism that confers plasticity to cancer stem cells (CSCs) and PCSCs, which contributes to metastasis. While PTEN inactivation leads to PC, it is not sufficient for metastasis, the loss of PTEN concurrently with the inactivation of both TP53 and RB1 empower lineage plasticity in PC cells, which substantially promotes PC metastasis and the conversion to PC adenocarcinoma to neuroendocrine PC (NEPC), demonstrating the essential function of TP53 and RB1 in the suppression of PCSCs. TP53 and RB1 suppress lineage plasticity through the inhibition of SOX2 expression. In this review, we will discuss the current evidence supporting a major role of PCSCs in PC initiation and metastasis, as well as the underlying mechanisms regulating PCSCs. These discussions will be developed along with the cancer stem cell (CSC) knowledge in other cancer types. [ABSTRACT FROM AUTHOR]