Prostate cancer is one of the most common and fatal diseases among men, and its early diagnosis can have a significant impact on the treatment process and prevent mortality. Since it does not have apparent clinical symptoms in the early stages, it is difficult to diagnose. In addition, the disagreement of experts in the analysis of magnetic resonance images is also a significant challenge. In recent years, various research has shown that deep learning, especially convolutional neural networks, has appeared successfully in machine vision (especially in medical image analysis). In this research, a deep learning approach was used on multi-parameter magnetic resonance images, and the synergistic effect of clinical and pathological data on the accuracy of the model was investigated. The data were collected from Trita Hospital in Tehran, which included 343 patients (data augmentation and learning transfer methods were used during the process). In the designed model, four different types of images are analyzed with four separate ResNet50 deep convolutional networks, and their extracted features are transferred to a fully connected neural network and combined with clinical and pathological features. In the model without clinical and pathological data, the maximum accuracy reached 88%, but by adding these data, the accuracy increased to 96%, which shows the significant impact of clinical and pathological data on the accuracy of diagnosis.

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Prostate magnetic resonance imaging (MRI) stands as the cornerstone in diagnosing prostate cancer (PCa), offering superior detection capabilities while minimizing unnecessary biopsies. Despite its critical role, global disparities in MRI diagnostic performance persist, stemming from variations in image quality and radiologist expertise. This manuscript reviews the challenges and strategies for enhancing image quality in prostate MRI, spanning patient preparation, MRI unit optimization, and radiology team engagement. Quality assurance (QA) and quality control (QC) processes are pivotal, emphasizing standardized protocols, meticulous patient evaluation, MRI unit workflow, and radiology team performance. Additionally, artificial intelligence (AI) advancements offer promising avenues for improving image quality and reducing acquisition times. The Prostate-Imaging Quality (PI-QUAL) scoring system emerges as a valuable tool for assessing MRI image quality. A comprehensive approach addressing technical, procedural, and interpretative aspects is essential to ensure consistent and reliable prostate MRI outcomes.

OBJECTIVE: Despite growing evidence for bilateral pelvic radiotherapy (whole pelvis RT, WPRT) there is almost no data on unilateral RT (hemi pelvis RT, HPRT) in patients with nodal recurrent prostate cancer after prostatectomy. Nevertheless, in clinical practice HPRT is sometimes used with the intention to reduce side effects compared to WPRT. Prostate-specific membrane antigen positron emission tomography / computed tomography (PSMA-PET/CT) is currently the best imaging modality in this clinical situation. This analysis compares PSMA-PET/CT based WPRT and HPRT.
METHODS: A propensity score matching was performed in a multi-institutional retrospective dataset of 273 patients treated with pelvic RT due to nodal recurrence (214 WPRT, 59 HPRT). In total, 102 patients (51 in each group) were included in the final analysis. Biochemical recurrence-free survival (BRFS) defined as prostate specific antigen (PSA) < post-RT nadir + 0.2ng/ml, metastasis-free survival (MFS) and nodal recurrence-free survival (NRFS) were calculated using the Kaplan-Meier method and compared using the log rank test.
RESULTS: Median follow-up was 29 months. After propensity matching, both groups were mostly well balanced. However, in the WPRT group there were still significantly more patients with additional local recurrences and biochemical persistence after prostatectomy. There were no significant differences between both groups in BRFS (p = .97), MFS (p = .43) and NRFS (p = .43). After two years, BRFS, MFS and NRFS were 61%, 86% and 88% in the WPRT group and 57%, 90% and 82% in the HPRT group, respectively. Application of a boost to lymph node metastases, a higher RT dose to the lymphatic pathways (> 50 Gy EQD2α/β=1.5 Gy) and concomitant androgen deprivation therapy (ADT) were significantly associated with longer BRFS in uni- and multivariate analysis.
CONCLUSIONS: Overall, this analysis presents the outcome of HPRT in nodal recurrent prostate cancer patients and shows that it can result in a similar oncologic outcome compared to WPRT. Nevertheless, patients in the WPRT may have been at a higher risk for progression due to some persistent imbalances between the groups. Therefore, further research should prospectively evaluate which subgroups of patients are suitable for HPRT and if HPRT leads to a clinically significant reduction in toxicity.

OBJECTIVE: Tumour perfusion is a nutrient-agnostic biomarker for cancer metabolic rate. Use of tumour perfusion for cancer growth assessment has been limited by complicated image acquisition, image analysis and limited field-of-view scanners. Long axial field-of-view (LAFOV) PET scan using [15O]H2O, allows quantitative assessment of whole-body tumour perfusion. We created a tool for automated creation of quantitative parametric whole-body tumour perfusion images in metastatic cancer.
METHODS: Ten metastatic prostate cancer patients underwent dynamic LAFOV [15O]H2O PET (Siemens, Quadra) followed by [18F]PSMA-1007 PET. Perfusion was measured as [15O]H2O K1 (mL/min/mL) with a single-tissue compartment model and an automatically captured cardiac image-derived input function. Parametric perfusion images were automatically calculated using the basis-function method with initial voxel-wise delay estimation and a leading-edge approach. Subsequently, perfusion of volumes-of-interest (VOI) can be directly extracted from the parametric images. We used a [18F]PSMA-1007 SUV 4 fixed threshold for tumour delineation and transferred these VOIs to the perfusion map.
RESULTS: For 8 primary tumours, 64 lymph node metastases, and 85 bone metastases, median tumour perfusion were 0.19 (0.15-0.27) mL/min/mL, 0.16 (0.13-0.27) mL/min/mL, and 0.26 (0.21-0.39), respectively. The correlation between calculated perfusion from time-activity-curves and parametric images was excellent (r = 0.99, p < 0.0001).
CONCLUSIONS: LAFOV PET imaging using [15O]H2O enables truly quantitative parametric images of whole-body tumour perfusion, a potential biomarker for guiding personalized treatment and monitoring treatment response.

BACKGROUND: The Cancer Health Awareness through screeNinG and Education (CHANGE) initiative delivers cancer awareness education with an emphasis on modifiable risk factors and navigation to screening for prostate, breast, and colorectal cancers to residents of public housing communities who experience significant negative social determinants of health.
METHODS: Residents of five communities participated. Community advisory board members were recruited and provided feedback to local environmental change projects, recruitment, and community engagement at each site. At each site, four education sessions were provided by trained facilitators on cancer risk factors and etiology, racial disparities, eligibility for cancer screening, and participation in clinical trials. Attendance, knowledge, attitudes and beliefs about cancer, and height, weight, and waist circumference were measured at baseline and 1-week post-CHANGE sessions.
RESULTS: 90 residents (60% 65 and older years old, 33% male, 60% High School education, 93% AA) participated in the program. 95% completed post-intervention evaluation. Participants were eligible for breast (n = 12), prostate (n = 15), and colorectal screening (n = 25) based on American Cancer Society guidelines, and 22 for tobacco cessation; 21 participants accepted navigation assistance for these services. At post-test, participants significantly increased in knowledge and behaviors around obesity/overweight risk for cancer, nutrition, and physical activity. Colorectal, prostate, and breast cancer knowledge scores also increased, but were not significant.
CONCLUSIONS: CHANGE participants demonstrated improved health knowledge and intentions to improve their modifiable health behaviors. Participants reported being motivated and confident in seeking preventive care and satisfaction with community engagement efforts. Replication of this project in similar communities may improve knowledge and health equity among underserved populations.

Transcription factors (TFs) are essential proteins regulating gene expression by binding to specific nucleotide sequences upstream of genes. Among TF families, the forkhead box (FOX) proteins, characterized by a conserved DNA-binding domain, play vital roles in various cellular processes, including cancer. The FOXA subfamily, encompassing FOXA1, FOXA2, and FOXA3, stands out for its pivotal role in mammalian development. FOXA1, initially identified in the liver, exhibits diverse expression across multiple organ tissues and plays a critical role in cell proliferation, differentiation, and tumor development. Its structural composition includes transactivation domains and a DNA-binding domain, facilitating its function as a pioneer factor, which is crucial for chromatin interaction and the recruitment of other transcriptional regulators. The involvement of FOXA1 in sex hormone-related tumors underscores its significance in cancer biology. This review provides an overview of multifaceted roles of FOXA1 in normal development and its implications in the pathogenesis of hormone-related cancers, particularly breast cancer and prostate cancer.

Advancing chimeric antigen receptor (CAR)-engineered T cells for the treatment of solid tumors is a major focus in the field of cellular immunotherapy. Several hurdles have hindered similar CAR T cell clinical responses in solid tumors as seen in hematological malignancies. These challenges include on-target off-tumor toxicities, which have inspired efforts to optimize CARs for improved tumor antigen selectivity and overall safety. We recently developed a CAR T cell therapy targeting prostate stem cell antigen (PSCA) for prostate and pancreatic cancers, showing improved preclinical antitumor activity and T cell persistence by optimizing the intracellular co-stimulatory domain. Similar studies were undertaken to optimize HER2-directed CAR T cells with modifications to the intracellular co-stimulatory domain for selective targeting of breast cancer brain metastasis. In the present study, we evaluate various nonsignaling extracellular spacers in these CARs to further improve tumor antigen selectivity. Our findings suggest that length and structure of the extracellular spacer can dictate the ability of CARs to selectively target tumor cells with high antigen density, while sparing cells with low antigen density. This study contributes to CAR construct design considerations and expands our knowledge of tuning solid tumor CAR T cell therapies for improved safety and efficacy.

Urinary extracellular vesicles (uEVs) are enriched with glycosylated proteins which have been extensively studied as putative biomarkers of urological cancers. Here, we characterized the glycosylation and integrin profile of EVs derived from urological cancer cell lines. We used fluorescent europium-doped nanoparticles coated with lectins and antibodies to identify a biomarker combination consisting of integrin subunit alpha 3 (ITGA3) and fucose. In addition, we used the same cancer cell line-derived EVs as analytical standards to assess the sensitivity of the ITGA3-UEA assay. The clinical performance of the ITGA3-UEA assay was analysed using urine samples of various urological pathologies including diagnostically challenging benign prostatic hyperplasia (BPH), prostate cancer (PCa) and bladder cancer (BlCa). The assay can significantly discriminate BlCa from all other patient groups: PCa (9.2-fold; p = 0.00038), BPH (5.5-fold; p = 0.004) and healthy individuals (and 23-fold; p = 0.0001). Our results demonstrate that aberrantly fucosylated uEVs and integrin ITGA3 can be detected with fucose-specific lectin UEA in a simple bioaffinity assay for the detection of BlCa directly from unprocessed urine.

Extracellular vesicles (EVs) are important mediators of intercellular communication involved in local and long-range signalling of cancer metastasis. The onset of invasion is the key step of the metastatic cascade, but the secretion of EVs has remained unexplored at that stage due to technical challenges. In this study, we present a platform to track EVs over the course of invasive development of human prostate cancer cell (PC3) tumoroids utilizing in vivo-mimicking extracellular matrix-based 3D cultures. Using this EV production method, combined with proteomic profiling, we show that PC3 tumoroids secrete EVs with previously undefined protein cargo. Intriguingly, an increase in EV amounts and extensive changes in the EV protein composition were detected upon invasive transition of the tumoroids. The changes in EV protein cargo were counteracted by chemical inhibition of invasion. These results reveal the impact of the tumoroids\' invasive status on EV secretion and cargo, and highlight the necessity of in vivo-mimicking conditions for uncovering novel cancer-derived EV components.