Search Results (247)

Metastatic castration-resistant prostate cancer (mCRPC) is a prevalent malignancy marked by molecular heterogeneity, which contributes to resistance to standard therapies and poor clinical prognosis. Advances in genomic and transcriptomic profiling have identified key drivers such as alterations in AR, TP53, PTEN, and RB1, which also enable cancer cells to circumvent therapies. Despite such advances, the underlying mechanisms involved in mCRPC drug resistance are complex, creating an urgent need for novel therapies to improve clinical outcomes. To address this clinical problem, strategies focused on targeting underlying molecular and metabolic supportive pathways using nano-delivery systems of diverse drugs could be promising in both CRPC and mCRPC therapy. This review provides an overview of the current understanding of the genomic and microenvironmental landscape of mCRPC and explores emerging classification frameworks aimed at improving patient outcomes. We highlight the potential of integrative multi-omics approaches to inform precision oncology and guide the development of more effective, personalized treatments for prostate cancer therapy. Full article

Background: Transperineal magnetic resonance (MRI)/ultrasound (US) fusion-guided prostate biopsy has emerged as a promising alternative to the transrectal approach by improving lesion targeting and reducing infectious complications. However, real-world data addressing factors that influence the detection of clinically significant prostate cancer (csPCa), including imaging characteristics and procedural experience, remain limited. Objective: To evaluate the diagnostic performance, safety profile, and independent predictors of csPCa detection in patients who underwent transperineal MR/US fusion-guided prostate biopsy, with particular emphasis on PIRADS category, prostate-specific antigen (PSA) level, and procedural learning curve. Methods: In this study, patient data were prospectively recorded in a routinely maintained institutional database, while the present analysis was conducted retrospectively. A total of 136 patients with clinical suspicion of prostate cancer—defined as elevated prostate-specific antigen (PSA), abnormal digital rectal examination, or PIRADS ≥3 on multiparametric MRI—underwent transperineal MR/US fusion-guided biopsy between January 2023 and October 2024. Results: Prostate cancer was detected in 45.5% of patients, whereas csPCa was identified in 32.3%. The PIRADS category emerged as the strongest independent predictor of csPCa detection, with PIRADS-5 lesions showing a significantly greater likelihood of csPCa than PIRADS-3 lesions (OR 6.70, p = 0.006). The PSA level was also independently associated with csPCa detection (OR 1.06 per ng/mL increase, p = 0.033). Although csPCa detection rates increased across learning curve groups, procedural experience was not an independent predictor after adjustment. The procedure demonstrated a favorable safety profile, with a low rate of infectious and noninfectious complications despite minimal use of antibiotic prophylaxis. The multivariable model showed moderate explanatory power and acceptable overall classification accuracy. Conclusions: Transperineal MR/US fusion-guided prostate biopsy provides reliable detection of clinically significant prostate cancer with a low complication rate and consistent performance across different stages of institutional experience. The PIRADS category and PSA level remain key determinants of csPCa detection, supporting the integration of MRI-based risk stratification into contemporary prostate cancer diagnostic methods. Full article

Background: Precise staging of prostate cancer is vital for treatment planning and prognosis. While [⁶⁸Ga]Ga-PSMA-11 PET-CT has demonstrated high diagnostic accuracy in detecting metastatic disease, the interpretation of indeterminate or potentially benign PSMA-avid bone lesions remains a clinical challenge in routine practice. Methods: We conducted a retrospective single-centre study involving 214 patients who underwent [⁶⁸Ga]Ga-PSMA-11 PET-CT between January 2021 and January 2024. Patients with prior known bone metastases or alternative PSMA radiotracers were excluded. Only those with follow-up imaging were included for diagnostic accuracy analysis. Follow-up modalities included PSMA PET-CT, CT, MRI, and bone scintigraphy. Final classification (metastatic or benign) was based on radiological and clinical assessment. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated using follow-up imaging as the reference standard. Lesions classified as indeterminate were analysed separately and excluded from diagnostic performance calculations. Results: Of the 214 included patients, 142 had follow-up imaging. Among 80 patients with bone lesions initially reported as metastatic, 74 (92.5%) were confirmed. Among 28 patients initially reported as having benign bone lesions, 26 (92.9%) remained benign on follow-up. Thirty-four patients with indeterminate lesions were reviewed; four were ultimately metastatic. Excluding indeterminate cases, sensitivity, specificity, PPV, and NPV were 97.4%, 86.7%, 94.9%, and 92.9%, respectively. Diagnostic discordance was primarily associated with benign uptake in the ribs, iliac bones, pubic rami and degenerative changes. Conclusions: [⁶⁸Ga]Ga-PSMA-11 PET-CT shows excellent sensitivity and positive predictive value for detecting metastatic bone disease in prostate cancer. However, benign lesions may also exhibit uptake, emphasising the importance of integrating imaging results with PSA levels, Gleason scores, and TNM staging. Prospective studies are needed to validate these findings and assess their impact on long-term outcomes. Full article

Background: Subtype classification for breast cancer (BC) patients is important for risk-stratification. Unfortunately, this parameter is not always able to discriminate between high- and low-risk diseases. Glutamate Carboxypeptidase-II (GCPII), also known as prostate-specific membrane antigen (PSMA), could be an important biomarker of aggressiveness, given that it has been reported to be expressed in BC tumor cells and even more in endothelial cells of tumor vessels. Methods: We analyzed 22 Luminal A, 47 Luminal B, 9 HER2-positive (HER2+), and 23 triple-negative (TN) BC to assess whether PSMA, Ki67 expression, and tumor-infiltrating lymphocytes (TILs) were different in BC subtypes. Results: Median PSMA and Ki67 values were significantly higher in TNBC than in Luminal A and B tumors. We saw a correlation between PSMA and Ki67 expression, especially in HER2+ tumors (p = 0.035), while an inverse correlation between PSMA and TILs was observed in Luminal A (p = 0.028). Conclusions: Our results suggest that PSMA could be used as a biomarker in BC, given that it is highly expressed in more aggressive tumors. These findings open the way to a clinical investigation for the possible use of PSMA as a theranostic biomarker in BC patients with PSMA positive PET scan. Full article

Bone scintigraphy is a widely available and cost-effective modality for detecting skeletal metastases in prostate cancer, yet visual interpretation can be challenging due to heterogeneous uptake patterns, benign mimickers, and a high reporting workload, motivating robust computer-aided decision support. In this study, we present an experimental evaluation of fourteen convolutional neural network (CNN) architectures for binary metastasis classification in planar bone scintigraphy using a unified protocol. Fourteen models, CNN (baseline), AlexNet, VGG16, VGG19, ResNet18, ResNet34, ResNet50, ResNet50-attention, DenseNet121, DenseNet169, DenseNet121-attention, WideResNet50_2, EfficientNet-B0, and ConvNeXt-Tiny, were trained and tested on 600 scan images (300 normal, 300 metastatic) from the Jordanian Royal Medical Services under identical preprocessing and augmentation with stratified five-fold cross-validation. We report mean ± SD for AUC-ROC, accuracy, precision, sensitivity (recall), F1-score, specificity, and Cohen’s κ, alongside calibration via the Brier score and deployment indicators (parameters, FLOPs, model size, and inference time). DenseNet121 achieved the best overall balance of diagnostic performance and reliability, reaching AUC-ROC 96.0 ± 1.2, accuracy 89.2 ± 2.2, sensitivity 83.7 ± 3.4, specificity 94.7 ± 2.2, F1-score 88.5 ± 2.5, κ = 0.783 ± 0.045, and the strongest calibration (Brier 0.080 ± 0.013), with stable fold-to-fold behaviour. DenseNet121-attention produced the highest AUC-ROC (96.3 ± 1.1) but exhibited greater variability in specificity, indicating less consistent false-alarm control. Complexity analysis supported DenseNet121 as deployable (~7.0 M parameters, ~26.9 MB, ~92 ms/image), whereas heavier models yielded only limited additional clinical value. These results support DenseNet121 as a reliable backbone for automated metastasis detection in planar scintigraphy, with future work focusing on external validation, threshold optimisation, interpretability, and model compression for clinical adoption. Full article

Background: Prostate-specific membrane antigen (PSMA) is markedly overexpressed in prostate cancer (PCa), and there is growing evidence to support its usefulness in initial diagnostic assessments. This study compares the diagnostic performance of PSMA positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (mpMRI) in evaluating seminal vesicle invasion (SVI), extraprostatic extension (EPE), and pelvic lymph node involvement before radical prostatectomy. Methods: A retrospective, single-institution analysis was performed. From a cohort of 325 patients who underwent radical prostatectomy between June 2022 to November 2024, 85 had undergone preoperative PSMA PET/CT for intermediate- and high-risk disease at biopsy, forming our study group. Two blinded specialists, one in radiology and one in nuclear medicine, independently interpreted the scans, using histopathological results as the reference standard. The primary outcome was diagnostic accuracy for T- and N-stage classification, while the secondary outcomes included the correct identification of the index lesion and comparative performance for each modality. Results: The study cohort comprised patients with intermediate-to-high-risk prostate cancer (median age: 66 years; median PSA level: 11.6 ng/mL; median PSA density: 0.3 ng/mL/cm³). Forty-eight patients presented with an ISUP grade of 3 or higher on biopsy. PSMA PET/CT was more sensitive than MRI for detecting EPE (72.2% vs. 46.9%) and nodal metastases (91.7% vs. 8.3%). Furthermore, PSMA PET/CT demonstrated significantly higher concordance with histopathological findings in index tumor localization (76.5% vs. 67.9%, p < 0.001). An exploratory analysis revealed a potential age-dependent pattern, but this requires confirmation in larger studies. Conclusions: In this select cohort, PSMA PET/CT demonstrated greater accuracy than MRI for locoregional staging in patients with intermediate-to-high-risk prostate cancer (PCa). However, the generalizability of these findings is limited by the retrospective design and potential selection bias. These results suggest that PSMA PET/CT may have a valuable role in the initial staging workflow, but this needs to be confirmed in larger, prospective studies. An exploratory analysis suggested a potential age-dependent pattern, but this requires confirmation in larger studies. Full article

Extraprostatic extension (EPE) is an important prognostic factor in prostate cancer and influences nerve-sparing decisions during radical prostatectomy. Multiparametric MRI (mpMRI) is the standard for local staging, but its sensitivity for EPE remains limited, and its interpretation is subject to inter-reader variability. In this narrative review, we aim to create an overview of contemporary strategies for the preoperative detection of EPE. We searched PubMed, Embase, Web of Science, and Google Scholar, focusing on studies published between 2015 and 2025 including articles evaluating clinical parameters, mpMRI features, nomograms, radiomics, machine learning, and deep learning models for EPE prediction. The analyzed literature was compared with respect to diagnostic performance, validation strategy, and clinical applicability of individual methods. Clinical parameters and traditional nomograms provide moderate accuracy for EPE detection. mpMRI improves staging, with tumor–capsule contact length as the most important single imaging marker. Radiomics-based and machine-learning models matched and occasionally outperform conventional approaches, achieving AUC values ranging from 0.75 to 0.85. Deep-learning models demonstrated similar performance by directly analyzing imaging data, although most lacked external validation and were sensitive to dataset heterogeneity. Several radiomics and deep learning models demonstrated performance comparable to, and in selected studies exceeding, expert radiologist assessment. Binary EPE classification has limited clinical value, while side-specific and graded EPE assessment offers a more clinically relevant approach. Translation of these tools into routine practice will require multimodal, side-specific, and externally validated models supported by automated segmentation and explainable artificial intelligence frameworks. Full article

Background/Objectives: The ratio of percentage of prostate cancer (PCa) biopsy-positive cores (BPC) to prostate volume as the index density factor (Id-BPC) was used to predict the risk of high tumour grades in the surgical specimens of European Association of Urology (EAU) intermediate-risk patients treated with robotic surgery. Methods: From January 2013 to December 2021, we evaluated 651 patients without any prior treatment for PCa. In the surgical specimen, tumour grades were classified as indolent (International Society of Urological Pathologists Classification (ISUP) 1), significant (ISUP 2/3), and lethal (ISUP 4/5). Associations with the risk of significant and lethal cancers were assessed by the multinomial logistic regression model. Results: Tumour grade was clinically significant (ISUP 2/3) in 522 (80.2%) cases and lethal (ISUP 4/5) in 99 (15.2%), while the results were not significant (ISUP 1) in 30 (4.6%) subjects. The association of Id-BPC was always stronger than BPC for ISUP 1 vs. 4/5 (OR = 0.284; 95% CI: 0.128–0.632; p = 0.002), ISUP 2/3 vs. 4/5 (OR = 0.744; 95% CI: 0.586–0.946; p = 0.016), and ISUP 1 vs. 2/3 (OR = 0.382; 95% CI: 0.176–0.828; p = 0.015), and this trend held after adjusting for clinical factors. Conclusions: Accordingly, Id-BPC was positively associated with lethal disease, as, when it increased or decreased, it was more or less likely, respectively, to find ISUP 4/5 in the surgical specimens of the operated subjects, who could have been stratified according to Id-BPC risk levels. Full article

Prostate cancer is diagnosed through a comprehensive study of histopathology slides, which takes time and requires professional interpretation. To minimize this load, we developed a semi-supervised learning technique that combines transformer-based representation learning and a custom TransUNet classifier. To capture a wide range of morphological structures without manual annotation, our method pretrains DINOv2 on 10,000 unlabeled prostate tissue patches. After receiving the transformer-derived features, a bespoke CNN-based decoder uses residual upsampling and carefully constructed skip connections to merge data from many spatial scales. Expert pathologists identified only 20% of the patches in the whole dataset; the remaining unlabeled samples were contributed by using a consistency-driven learning method that promoted reliable predictions across various augmentations. The model received precision and recall scores of 91.81% and 89.02%, respectively, and an accuracy of 93.78% on an additional test set. These results exceed the performance of a conventional U-Net and a baseline encoder–decoder network. All things considered, the localized CNN (Convolutional Neural Network) decoding and global transformer attention provide a reliable method for prostate cancer classification in situations with little annotated data. Full article

Advanced prostate cancer, particularly castration-resistant disease, remains challenging to treat due to intratumoral heterogeneity, immune exclusion, and a suppressive tumor microenvironment. Within this ecosystem, cancer-associated fibroblasts shape tumor–stroma communication, but their marked heterogeneity and plasticity complicate classification and make indiscriminate fibroblast depletion potentially ineffective or even harmful. This review summarizes recent progress in fibroblast origins, functional subtypes, and fibroblast-driven mechanisms that promote tumor progression and therapy resistance, as well as emerging therapeutic opportunities in prostate cancer. We conducted a structured literature search of PubMed, ScienceDirect, and major publisher platforms (including Nature and SpringerLink) from database inception to 15 February 2025, supplemented by targeted manual screening of reference lists. Evidence from single-cell/spatial-omics and mechanistic studies indicates that prostate tumors contain multiple fibroblast programs that occupy distinct niches yet can interconvert. Across these studies, it was found that these fibroblasts contribute to immune suppression, extracellular matrix remodeling and stromal barrier formation, angiogenesis, and metabolic support, collectively limiting drug penetration and reinforcing immune evasion; therapeutic pressure can further rewire fibroblast states and resistance-associated signaling. Overall, the literature supports a shift toward function- and subtype-directed intervention rather than “one-size-fits-all” targeting, with promising directions including precision targeting and reversible reprogramming, rational combination strategies, and localized delivery approaches that reduce stromal barriers while preserving tissue homeostasis in high-risk and treatment-refractory prostate cancer. Full article

Background: Cancer poses a significant health risk to humans, with prostate cancer (PCa) being the second most common and deadly form among men, following lung cancer. Each year, it affects over a million individuals and presents substantial diagnostic challenges due to variations in tissue appearance and imaging quality. In recent decades, various techniques utilizing Magnetic Resonance Imaging (MRI) have been developed for identifying and classifying PCa. Accurate classification in MRI typically requires the integration of complementary feature types, such as deep semantic representations from Convolutional Neural Networks (CNNs) and handcrafted descriptors like Histogram of Oriented Gradients (HOG). Therefore, a more robust and discriminative feature integration strategy is crucial for enhancing computer-aided diagnosis performance. Objectives: This study aims to develop a multi-stage hybrid learning model that combines deep and handcrafted features, investigates various feature reduction and classification techniques, and improves diagnostic accuracy for prostate cancer using magnetic resonance imaging. Methods: The proposed framework integrates deep features extracted from convolutional architectures with handcrafted texture descriptors to capture both semantic and structural information. Multiple dimensionality reduction methods, including singular value decomposition (SVD), were evaluated to optimize the fused feature space. Several machine learning (ML) classifiers were benchmarked to identify the most effective diagnostic configuration. The overall framework was validated using k-fold cross-validation to ensure reliability and minimize evaluation bias. Results: Experimental results on the Transverse Plane Prostate (TPP) dataset for binary classification tasks showed that the hybrid model significantly outperformed individual deep or handcrafted approaches, achieving superior accuracy of 99.74%, specificity of 99.87%, precision of 99.87%, sensitivity of 99.61%, and F1-score of 99.74%. Conclusions: By combining complementary feature extraction, dimensionality reduction, and optimized classification, the proposed model offers a reliable and generalizable solution for prostate cancer diagnosis and demonstrates strong potential for integration into intelligent clinical decision-support systems. Full article

Background: Precise delineation of the rectum is crucial in treatment planning for cancers in the pelvic region, such as prostate and cervical cancers. Manual segmentation is also still time-consuming and suffers from inter-observer variability. Since there are meaningful differences in rectal anatomy between males and females, incorporating sex-specific anatomical patterns can be used to enhance the performance of segmentations. Furthermore, recent deep learning advancements have provided promising solutions for automatically classifying patient sex from CT scans and leveraging this information for enhancing the accuracy of rectal segmentation. However, their clinical utility requires comprehensive validation against real-world standards. Methods: In this study, a two-stage deep learning pipeline was developed using CT scans from 186 patients with either prostate or cervical cancer. First, a CNN model automatically classified the patient’s biological sex from CT images in order to capture anatomical variations dependent on sex. Second, a sex-aware U-Net model performed automated rectal segmentation, allowing the network to adjust its feature representation based on the anatomical differences identified in stage one. The internal validation had an 80/20 train–test split, and 15% of the training portion was held out for validation to ensure balanced distribution regarding sex and diagnosis. Model performance was evaluated using spatial similarity metrics, including the Dice Similarity Coefficient (DSC), Hausdorff Distance, and Average Surface Distance. Additionally, a radiation oncologist conducted a retrospective clinical evaluation using a 3-point Likert scale. Statistical significance was examined using Wilcoxon signed-rank tests, Welch’s t-tests, and Mann–Whitney U test. Results: The sex-classification model attained an accuracy of 94.6% (AUC = 0.98, 95% CI: 0.96–0.99). Incorporation of predicted sex into the segmentation pipeline improved anatomical consistency of U-Net outputs. Mean DSC values were 0.91 (95% CI: 0.89–0.92) for prostate cases and 0.89 (95% CI: 0.87–0.91) for cervical cases, with no significant difference between groups (p = 0.12). Surface distance metrics calculated on resampled isotropic voxels showed mean HD values of 3.4 ± 0.8 mm and ASD of 1.2 ± 0.3 mm, consistent with clinically acceptable accuracy. On clinical evaluation, 89.2% of contours were rated as excellent, while 9.1% required only minor adjustments. Automated segmentation reduced the average contouring time from 12.7 ± 2.3 min manually to 4.3 ± 0.9 min. Conclusions: The proposed sex-aware deep learning framework offers accurate, robust segmentation of the rectum in pelvic CT imaging by explicitly modeling sex-specific differences in anatomical characteristics. This physiologically informed approach enhances segmentation performance and supports reliable integration of AI-based delineation into radiotherapy workflows to improve both contouring efficiency and clinical consistency. Full article

The complex relationship between human cytomegalovirus (HCMV) and cancer has been of interest since the 1960s. As a highly prevalent human β-herpesvirus, HCMV establishes lifelong latency in CD34+ myeloid progenitor cells and has been implicated as an oncomodulatory virus in various cancers, including glioblastoma multiforme, breast, prostate, colorectal, and ovarian cancer (OC). Recently, discussions have emerged regarding the classification of HCMV as an eighth oncovirus due to the persistence of its nucleic acids and proteins in many tumour types. As one of the deadliest gynaecological cancers, OC is often characterised as the ‘silent killer’ with less than half of women surviving for 5 years, a rate that drops below 20% when detected at advanced stages. Reported effects of HCMV vary between cancers, likely due to differences in tumour type, viral strain, and disease stage. While HCMV infection has been linked to poor OC patient outcomes, its impact on the OC tumour microenvironment (TME) and immune system remains less understood. Investigating HCMV’s potential oncogenic role could provide critical insights into OC progression. This review discusses recent developments on HCMV’s multifaceted roles in OC, including strain heterogeneity, immunomodulation of the TME, dysregulation of inflammatory signalling pathways, and potential therapeutic approaches targeting HCMV in anti-cancer immunotherapies. Full article

Pancreatic neuroendocrine neoplasms (pNENs) are the second most common type of pancreatic cancer after pancreatic ductal adenocarcinoma. Germline mutations in DNA repair genes drive several hereditary and sporadic cancers; however, their role in pNENs remains poorly defined. This pilot study aimed to assess the frequency and clinical relevance of germline DNA repair gene mutations in patients with pNENs, both with and without a family history of cancer. Germline DNA from 57 Polish patients with pNENs was analyzed using targeted next-generation sequencing to identify variants in a panel of DNA repair genes. Variant classification followed the American College of Medical Genetics and Genomics/Association for Molecular Pathology guidelines. Germline mutations were identified in 14 patients (24.6%), both with and without a family history of malignancy. Two patients carried pathogenic variants in BRCA2 and CHEK2, while seven carried variants of uncertain significance (VUS). The identified variants have been implicated in various cancer types, including breast, ovarian, prostate, gastric, colorectal, and pancreatic cancers. These findings indicate that germline mutations in DNA repair genes may contribute to the pathogenesis of pNENs, even in patients without a family history. Broader germline testing and population-specific studies are needed to clarify the genetic landscape and clinical implications of these alterations. Full article

Background/Objective: Biochemical recurrence (BCR) after radical prostatectomy (RP) for prostate cancer indicates disease progression. Although type 2 diabetes mellitus (T2D) shows a paradoxical association with prostate cancer risk, the prognostic role of T2D-related genetic variants remains unclear. Methods: We analyzed 113 common T2D susceptibility-related single-nucleotide polymorphisms (SNPs) in 644 Taiwanese men with localized prostate cancer (D’Amico risk classification: 12% low, 34% intermediate, and 54% high) treated with RP. Associations between SNPs and BCR were assessed using Cox regression, adjusting for key clinicopathological factors. Functional annotation was performed using HaploReg and FIVEx, while The Cancer Genome Atlas transcriptomic data were analyzed for C2 calcium-dependent domain-containing 4A (C2CD4A) expression. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were applied to explore related biological pathways. Results: C2CD4A SNP rs4502156 was independently associated with a reduced risk of BCR (hazard ratio = 0.80, p = 0.035). The protective C allele correlated with higher C2CD4A expression. Low C2CD4A expression is associated with advanced pathological stages, higher Gleason scores, and disease progression. GSEA revealed negative enrichment of mitotic and chromatid segregation pathways in high-C2CD4A-expressing tumors, with E2F targets being the most suppressed. GSVA confirmed an inverse correlation between C2CD4A expression and E2F pathway activity, with CDKN2C as a co-expressed functional gene. Conclusions: The T2D-related variant rs4502156 in C2CD4A independently predicts a lower risk of BCR, potentially via suppression of the E2F pathway, and may serve as a germline biomarker for postoperative risk stratification. Full article