BACKGROUND: At present, biopsy is essential for the diagnosis of prostate cancer (PCa) before radical prostatectomy (RP). However, with the development of prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) and multiparametric magnetic resonance imaging (mpMRI), it might be feasible to avoid biopsy before RP. Herein, we aimed to explore the feasibility of avoiding biopsy before RP in patients highly suspected of having PCa after assessment of PSMA PET/CT and mpMRI. METHODS: Between December 2017 and April 2022, 56 patients with maximum standardized uptake value (SUVmax) of ≥4 and Prostate Imaging Reporting and Data System (PI-RADS) ≥4 lesions who received RP without preoperative biopsy were enrolled from two tertiary hospitals. The consistency between clinical and pathological diagnoses was evaluated. Preoperative characteristics were compared among patients with different pathological types, T stages, International Society of Urological Pathology (ISUP) grades, and European Association of Urology (EAU) risk groups. RESULTS: Fifty-five (98%) patients were confirmed with PCa by pathology, including 49 (89%) with clinically significant prostate cancer (csPCa, defined as ISUP grade ≥2 malignancy). One patient was diagnosed with high-grade prostatic intraepithelial neoplasia (HGPIN). CsPCa patients, compared with clinically insignificant prostate cancer (cisPCa) and HGPIN patients, were associated with a higher level of prostate-specific antigen (22.9 ng/mL vs . 10.0 ng/mL, P = 0.032), a lower median prostate volume (32.2 mL vs . 65.0 mL, P = 0.001), and a higher median SUVmax (13.3 vs . 5.6, P <0.001). CONCLUSIONS It might be feasible to avoid biopsy before RP for patients with a high probability of PCa based on PSMA PET/CT and mpMRI. However, the diagnostic efficacy of csPCa with PI-RADS ≥4 and SUVmax of ≥4 is inadequate for performing a procedure such as RP. Further prospective multicenter studies with larger sample sizes are necessary to confirm our perspectives and establish predictive models with PSMA PET/CT and mpMRI.
Humans ; Male ; Prostatectomy/methods* ; Prostatic Neoplasms/diagnosis* ; Middle Aged ; Aged ; Positron Emission Tomography Computed Tomography/methods* ; Biopsy ; Multiparametric Magnetic Resonance Imaging ; Prostate-Specific Antigen/metabolism*

Prostate cancer (PCa) ranks as the second most prevalent malignancy among men worldwide. Early diagnosis, personalized treatment, and prognosis prediction of PCa play a crucial role in improving patients' survival rates. The advancement of artificial intelligence (AI), particularly the utilization of deep learning (DL) algorithms, has brought about substantial progress in assisting the diagnosis, treatment, and prognosis prediction of PCa. The introduction of the foundation model has revolutionized the application of AI in medical treatment and facilitated its integration into clinical practice. This review emphasizes the clinical application of AI in PCa by discussing recent advancements from both pathological and imaging perspectives. Furthermore, it explores the current challenges faced by AI in clinical applications while also considering future developments, aiming to provide a valuable point of reference for the integration of AI and clinical applications.
Humans ; Prostatic Neoplasms/diagnosis* ; Male ; Artificial Intelligence ; Deep Learning ; Prognosis

OBJECTIVES: To identify immunosuppressive neutrophil subsets in patients with prostate cancer (PCa) and construct a risk prediction model for prognosis and immunotherapy response of the patients based on these neutrophil subsets. METHODS: Single-cell and transcriptome data from PCa patients were collected from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Neutrophil subsets in PCa were identified through unsupervised clustering, and their biological functions and effects on immune regulation were analyzed by functional enrichment, cell interaction, and pseudo-time series analyses. Lasso-Cox regression was utilized to construct a prognostic risk model based on the immunosuppressive neutrophil subsets, and survival analysis and ROC curve analysis were used to compare the prognosis of PCa patients with high and low risks stratified using this model. The relationship of the prognostic risk model with PCa immune infiltration and immune response was evaluated using CIBERSORT and TIDE scores. RESULTS: PCa tissues showed a significantly greater proportion of infiltrating neutrophils than the adjacent normal tissues (P<0.05). PCa-associated neutrophils could be clustered into two independent cell subsets: Neu_1 and Neu_2. Neu_2 cells exhibited highly enriched immunoregulatory functions and were highly differentiated and mature, with upregulated immunosuppressive cytokines such as TGFB1, ITGB2, and LGALS3. Based on the genetic characteristics of Neu_2 cell subsets, the prognostic risk model was constructed. The patients in the high-risk group identified by the model had a shorter biochemical recurrence time (P<0.05) and a higher proportion of Tregs and M2-TAMs cell infiltration (P<0.05) with a higher risk of immune rejection and poorer immune response scores. CONCLUSIONS PCa-associated neutrophils are highly heterogeneous. The prognostic risk model constructed based on the immunosuppressive neutrophil Neu_2 subset can effectively predict both the survival outcomes and immune response of PCa patients.
Humans ; Male ; Prostatic Neoplasms/diagnosis* ; Prognosis ; Neutrophils/immunology* ; Immunotherapy

Prostate cancer represents a prevalent malignancy within the male genitourinary system. In recent years, its incidence in China has gradually increased, becoming a significant public health issue. While early detection correlates strongly with improved prognosis, the majority of newly diagnosed prostate cancer patients in China are already in intermediate or advanced stages, precluding curative-intent interventions and contributing to marked survival disparities. The progression of prostate cancer is lengthy, typically encompassing diagnosis, treatment, progression, metastasis, and death, accompanied by a decline in quality of life. Personalized treatment plans should be developed based on the disease stage and patient preferences. In non-metastatic prostate cancer, where the tumor is confined to the prostate, surgery and radiotherapy are the primary treatments, supplemented by neoadjuvant and adjuvant therapies to delay metastasis. For metastatic prostate cancer, systemic therapy is prioritized to prolong survival. In metastatic hormone-sensitive prostate cancer, controlling androgen levels is crucial, while treatment options for metastatic castration resistant prostate cancer are relatively limited, necessitating individualized and precise treatment. During prostate cancer management, prostate-specific antigen levels are closely linked to prognosis and require monitoring. Bone metastasis, the most common site in prostate cancer patients, often triggers skeletal-related events, demanding effective prevention and management. Treatment-related adverse reactions are also a clinical challenge, requiring balanced risk-benefit assessments and judicious drug selection to preserve quality of life. Rapid advancements in screening technologies, surgical innovations, drug development, and China-specific epidemiological factors further complicate decision-making in holistic prostate cancer management. To optimize the standardization of prostate cancer diagnosis and treatment in China, the Genitourinary Oncology Committee of Chinese Anti-cancer Association synthesized global guidelines, clinical evidence and clinical expertise, and addressed critical challenges in the whole-course management of prostate cancer to formulate a multidisciplinary consensus. The expert consensus on whole-course management of prostate cancer (2025 edition) establishes standardized protocols to guide clinical practice, improve treatment outcomes, and enhance patient quality of life.
Humans ; Male ; Prostatic Neoplasms/diagnosis* ; Consensus ; Prostate-Specific Antigen/blood* ; Quality of Life ; Prostatic Neoplasms, Castration-Resistant/pathology* ; China ; Bone Neoplasms/secondary* ; Androgen Antagonists/therapeutic use*

The choice of biopsy method is critical in diagnosing prostate cancer (PCa). This retrospective cohort study compared systematic biopsy (SB) or cognitive fusion-targeted biopsy combined with SB (CB) in detecting PCa and clinically significant prostate cancer (csPCa). Data from 2572 men who underwent either SB or CB in Fudan University Shanghai Cancer Center (Shanghai, China) between January 2019 and December 2023 were analyzed. Propensity score matching (PSM) was used to balance baseline characteristics, and detection rates were compared before and after PSM. Subgroup analyses based on prostate-specific antigen (PSA) levels and Prostate Imaging-Reporting and Data System (PI-RADS) scores were performed. Primary and secondary outcomes were the detection rates of PCa and csPCa, respectively. Of 2572 men, 1778 were included in the PSM analysis. Before PSM, CB had higher detection rates for both PCa (62.9% vs 52.4%, odds ratio [OR]: 1.54, P < 0.001) and csPCa (54.9% vs 43.3%, OR: 1.60, P < 0.001) compared to SB. After PSM, CB remained superior in detecting PCa (63.1% vs 47.9%, OR: 1.86, P < 0.001) and csPCa (55.0% vs 38.2%, OR: 1.98, P < 0.001). In patients with PSA 4-12 ng ml -1 (>4 ng ml -1 and ≤12 ng ml -1 , which is also applicable to the following text), CB detected more PCa (59.8% vs 40.7%, OR: 2.17, P < 0.001) and csPCa (48.1% vs 27.7%, OR: 2.42, P < 0.001). CB also showed superior csPCa detection in those with PI-RADS 3 lesions (32.1% vs 18.0%, OR: 2.15, P = 0.038). Overall, CB significantly improves PCa and csPCa detection, especially in patients with PSA 4-12 ng ml -1 or PI-RADS 3 lesions.
Humans ; Male ; Prostatic Neoplasms/diagnosis* ; Propensity Score ; Retrospective Studies ; Middle Aged ; Aged ; Image-Guided Biopsy/methods* ; Prostate-Specific Antigen/blood* ; Prostate/diagnostic imaging*

OBJECTIVE: The aim of this study is to investigate the risk factors of prognosis in patients with early-onset prostate cancer treated without surgery. A nomogram will be constructed and validated to predict overall survival (OS) of patients with early-onset prostate cancer treated without surgery. METHODS: The clinical data was obtained from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database on prostate cancer patients aged 18－55 years who were treated without surgery between 2010 and 2015. The clinical data set was divided into training set and validation set according to 7∶3 ratio, including age, race, marital status, Gleason score, prostate specific antigen (PSA) and other 8 factors. And significant variables were screened by univariate Cox regression analysis. Multivariate Cox regression analysis was used to identify the influence factors. Stepwise regression method was used to select the most influential factors on the total OS, and R software was used to build a nomogram model. The accuracy and prediction ability of the model were verified by drawing receiver operating characteristic (ROC) and Calibration Plot. The clinical benefit of the model was evaluated by decision curve analysis (DCA). RESULTS: A total of 8 212 patients who met the criteria were randomly assigned to the training set (n=5 752) or validation set (n=2 460), with no statistical difference between the two groups (all P>0.05). Six factors were identified through univariate and multivariate Cox regression analysis including marital status, N stage, M stage, radiotherapy, PSA and Gleason score, which were most closely associated with the OS of prostate cancer patients, and a column graph model was constructed based on these factors. The Consistency index (C-index) of the model in the training set and the verification set were 0.802 and 0.794, respectively. And the apparent diffusion coefficient (AUC) was 0.851, 0.855 and 0.855 for training sets 1, 3 and 5 years, and 0.694, 0.860 and 0.832 for verification sets 1, 3 and 5 years. The calibration chart showed a good agreement between the predicted and actual values of the model. In the analysis of decision curve, the model showed good clinical application value. CONCLUSION The prediction model based on marital status, radiotherapy, M stage, N stage, PSA and Gleason score for early-onset prostate cancer patients without surgical treatment has certain reference value which is expected to become an effective tool for clinicians to treat in future prospective studies on large and multi-center samples.
Humans ; Male ; Prostatic Neoplasms/diagnosis* ; Middle Aged ; Nomograms ; SEER Program ; Prognosis ; Adult ; Prostate-Specific Antigen ; Risk Factors ; Proportional Hazards Models ; Neoplasm Grading ; ROC Curve

OBJECTIVE: To explore the application value of joint detection of serum neutrophil-to-lymphocyte ratio (NLR), prostate-specific antigen (PSA) and MMP26 in differentiating prostate cancer (PCa) from benign prostatic hyperplasia (BPH). METHODS: A total of 61 PCa patients (PCa group) and 63 BPH patients (BPH group) who were treated in The Affiliated Huaian Hospital of Xuzhou Medical University from May 2022 to May 2024 were retrospectively included. The relevant clinical data of all subjects were collected with the serum NLR, PSA and MMP26 levels being detected. Multivariate logistic regression analysis was used to analyze the influencing factors in differentiating PCa from BPH. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value of serum NLR, PSA and MMP26 in differentiating PCa from BPH. RESULTS: The levels of TC and LDL-C in the PCa group were higher than those in the BPH group. And the level of HDL-C in the PCa group was lower than that in the BPH group (P<0.05). The serum levels of NLR, PSA and MMP26 in the PCa group were higher than those in the BPH group (P<0.05). The results of multivariate logistic regression analysis showed that NLR, PSA and MMP26 were risk factors for the diagnosis of PCa in patients (P<0.05). The ROC results showed that the area under the curve (AUC) of NLR, PSA MMP26 and joint diagnosis in the identification of PCa was 0.804, 0.800, 0.809 and 0.905, respectively. The comparison results of AUC showed that the joint diagnosis was superior to the single diagnosis (Z=2.262, 2.177, 2.002, P<0.05). CONCLUSION The joint detection of serum NLR, PSA and MMP26 has significant application value in the differentiation of PCa and BPH, which is expected to become an effective tool for early screening and diagnosis of PCa.
Humans ; Male ; Prostatic Hyperplasia/blood* ; Prostate-Specific Antigen/blood* ; Diagnosis, Differential ; Prostatic Neoplasms/blood* ; Retrospective Studies ; Neutrophils ; Lymphocytes ; ROC Curve ; Aged ; Middle Aged

Prostate cancer is the second most common cancer in men, accounting for 14.1% of new cancer cases in 2020. The aggressiveness of prostate cancer is highly variable, depending on its grade and stage at the time of diagnosis. Despite recent advances in prostate cancer treatment, some patients still experience recurrence or even progression after undergoing radical treatment. Accurate initial staging and monitoring for recurrence determine patient management, which in turn affect patient prognosis and survival. Classical imaging has limitations in the diagnosis and treatment of prostate cancer, but the use of novel molecular probes has improved the detection rate, specificity, and accuracy of prostate cancer detection. Molecular probe-based imaging modalities allow the visualization and quantitative measurement of biological processes at the molecular and cellular levels in living systems. An increased understanding of tumor biology of prostate cancer and the discovery of new tumor biomarkers have allowed the exploration of additional molecular probe targets. The development of novel ligands and advances in nano-based delivery technologies have accelerated the research and development of molecular probes. Here, we summarize the use of molecular probes in positron emission tomography (PET), single-photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), optical imaging, and ultrasound imaging, and provide a brief overview of important target molecules in prostate cancer.
Humans ; Male ; Prostatic Neoplasms/diagnosis* ; Molecular Probes ; Molecular Imaging/methods* ; Magnetic Resonance Imaging ; Positron-Emission Tomography ; Tomography, Emission-Computed, Single-Photon ; Ultrasonography ; Optical Imaging ; Biomarkers, Tumor ; Precision Medicine/methods*

Prostate cancer is one of the most common malignant tumors in men and posing a serious threat to men's health. Detection methods such as prostate-specific antigen (PSA), prostate biopsy, and magnetic resonance imaging are widely used for prostate cancer screening, but they have low specificity, high cost, and significant risks. Therefore, there is an urgent need to develop highly specific, low-cost, easily obtained, stable, and reliable biomarkers, and use them as the basis to establish non-invasive screening and diagnostic methods for prostate cancer. This paper reviewed the recent advances in the use of prostate cancer biomarkers and combined detection methods for prostate cancer diagnosis and prognosis assessment and provides an in-depth analysis and comparison of different biomarkers and combined detection methods, as well as points out the directions and challenges for future research. The paper emphasizes the importance of developing efficient, cost-effective and easy-to-implement biomarkers to increase the early diagnosis rate of prostate cancer, improve patient prognosis, and reduce the waste of healthcare resources. This paper provides an important theoretical basis and technical guidance for early diagnosis, precise treatment and prognostic evaluation of prostate cancer, and has important reference value for promoting clinical research and practice of prostate cancer.
Humans ; Male ; Prostatic Neoplasms/diagnosis* ; Biomarkers, Tumor/blood* ; Early Detection of Cancer/methods* ; Prognosis ; Prostate-Specific Antigen/blood* ; Glutamate Carboxypeptidase II/metabolism* ; Antigens, Neoplasm/blood* ; Antigens, Surface ; Serine Endopeptidases

This study investigated whether free prostate-specific antigen (fPSA) performs better than total PSA (tPSA) in predicting prostate volume (PV) in Chinese men with different PSA levels. A total of 5463 men with PSA levels of <10 ng ml^-1 and without prostate cancer diagnosis were included in this study. Patients were classified into four groups: PSA <2.5 ng ml^-1, 2.5-3.9 ng ml^-1, 4.0-9.9 ng ml^-1, and 2.5-9.9 ng ml^-1. Pearson/Spearman's correlation coefficient (r) and receiver operating characteristic (ROC) curves were used to evaluate the ability of tPSA and fPSA to predict PV. The correlation coefficient between tPSA and PV in the PSA <2.5 ng ml^-1 cohort (r = 0.422; P < 0.001) was markedly higher than those of the cohorts with PSA levels of 2.5-3.9 ng ml^-1, 4.0-9.9 ng ml^-1, and 2.5-9.9 ng ml^-1 (r = 0.114, 0.167, and 0.264, respectively; all P ≤ 0.001), while fPSA levels did not differ significantly among different PSA groups. Area under ROC curve (AUC) analyses revealed that the performance of fPSA in predicting PV ≥40 ml (AUC: 0.694, 0.714, and 0.727) was better than that of tPSA (AUC = 0.545, 0.561, and 0.611) in men with PSA levels of 2.5-3.9 ng ml^-1, 4.0-9.9 ng ml^-1, and 2.5-9.9 ng ml^-1, respectively, but not at PSA levels of <2.5 ng ml^-1 (AUC: 0.713 vs 0.720). These findings suggest that the relationship between tPSA and PV may vary with PSA level and that fPSA is more powerful at predicting PV only in the ''gray zone'' (PSA levels of 2.5-9.9 ng ml^-1), but its performance was similar to that of tPSA at PSA levels of <2.5 ng ml^-1.
Male ; Humans ; Prostate-Specific Antigen ; Prostate ; East Asian People ; Prostatic Neoplasms/diagnosis* ; ROC Curve