Background: There is a high incidence of metabolic syndrome (MS) and prostate disease in elderly males over 60 years old. This study aimed to investigate the influence of MS on prostate-specific antigen (PSA) levels, prostate volume (PV) and PSA density (PSAD) in this population. Methods: We retrospectively analyzed 3918 males aged 60 and above who underwent physical examinations in our hospital from January 2022 to December 2024. The subjects were divided into MS group and non-MS group. PSA mass (PSA x plasma volume) was calculated to adjust plasma volume. PSA, PSA mass, PV and PSAD were compared between the 2 groups. To evaluate the impact of MS on the four parameters, linear regression analysis was used. Results: Compared with non-MS group, subjects of the MS group had significantly older age (p = 0.017), larger plasma volume (p < 0.001), larger PV (p < 0.001) and lower PSAD (p = 0.013). However, there was no statistical difference in PSA and PSA mass between the two groups (p = 0.339 and 0.919). In the multivariate linear regression, age, plasma volume and presence of MS were proved as independent factors for PV; age, Body Mass Index (BMI) and plasma volume were independent factors for PSAD. MS was proved as a significant factor for PSAD in univariate regression, but showed no statistical significance in multivariate regression analysis. Among the components of MS, central obesity was proved as independent influencing factor for both PV and PSAD. Conclusions: MS was independently associated with PV in elderly males over 60 years, but had no significant correlation with PSA. These results differed from previous research, as we selected an elder population in this study.

BackgroundComputed tomography (CT) Hounsfield units (HUs) of pathologically confirmed metastatic inguinal lymph nodes (ILNs) were proved to be higher than negative ones. We designed this study to explore the clinical value of CT HU for diagnosing palpable ILN metastasis in patients with penile cancer.MethodsA total of 32 patients with penile cancer, including 84 palpable ILNs, were recruited in this study. They all performed 5-mm layer pelvic contrast-enhanced CT (CE-CT) before treatment. The palpable ILNs were matched with CT image. By using radiologic software PACS, the layer with a maximum cross-sectional area of target lymph node was selected, and the short axis was defined as diameter. We outlined the edge of target lymph nodes, and the software automatically calculated its area, maximum CT HU, and average CT HU. All target ILNs were biopsied by surgery to confirm the presence of metastasis.ResultsCompared with non-metastatic ILNs, metastatic ILNs had larger diameter, area, maximum non-contrast CT (NC-CT) HU, maximum arterial-phase CE-CT (ACE-CT) HU, average NC-CT HU, and average ACE-CT HU, with statistically significant differences (P < 0.05). Receiver operating characteristic analysis showed the all six parameters (maximum NC-CT HU, maximum ACE-CT HU, average NC-CT HU, average ACE-CT HU, diameter, and area) had significant diagnostic value for ILN metastasis, with an area under the curve of 0.847, 0.853, 0.900, 0.919, 0.809, and 0.789, respectively. The average ACE-CT HU (cutoff: 40.5) had the highest accuracy as 0.857, and maximum NC-CT HU (cutoff: 51.5) had the highest sensitivity of 0.897.ConclusionILN CT HU was clinically valuable for the diagnosis of palpable ILN metastasis in patients with newly diagnosed penile cancer.

Introduction:Accurate prediction of bone metastasis at diagnosis is crucial for optimizing management in prostate cancer (PCa) patients. While clinical parameters like PSA and Gleason score are established predictors, their accuracy is suboptimal. Systemic inflammation, reflected in biomarkers like the high-sensitivity C-reactive protein-to-albumin ratio (HAR), fibrinogen (FIB), and hemoglobin (HB), has emerged as a key player in cancer progression, yet its integration into clinical predictive tools remains underexplored. Methods:In this retrospective study of 803 newly diagnosed PCa patients, we developed and validated two nomograms for predicting bone metastasis. A baseline clinical model was constructed using total prostate-specific antigen (TPSA) and biopsy Gleason grade groups. An enhanced comprehensive model integrated these clinical parameters with inflammatory markers (HAR, FIB, HB). Model performance was rigorously assessed through discrimination (ROC analysis, AUC), calibration (calibration curves, Hosmer-Lemeshow test), and clinical utility (Decision Curve Analysis). Internal validation was performed via bootstrapping. Results:Multivariate analysis confirmed TPSA, FIB, HB, HAR, and Gleason grade groups as independent predictors of bone metastasis. The comprehensive model demonstrated significantly superior discriminative ability, achieving an AUC of 0.874 (95% CI: 0.845-0.902) compared to 0.830 (95% CI: 0.798-0.863) for the clinical model (Delong's test, P < 0.01). This translated to a net improvement in reclassification (NRI: 8.96%) and overall predictive performance (IDI: 10.3%). The model was well-calibrated and provided a positive net benefit across a wide range of clinical threshold probabilities. Conclusion:We present a novel, internally validated nomogram that synergistically combines inflammatory and clinical markers to accurately predict bone metastasis in PCa at initial diagnosis. This practical and cost-effective tool has the potential to aid clinicians in risk stratification, guide personalized diagnostic imaging decisions, and ultimately help reduce unnecessary bone scans, particularly in resource-conscious settings. Our findings underscore the pivotal role of the systemic inflammatory response in PCa metastasis.

ObjectiveThe purpose of this study was to investigate the clinical significance of serum high sensitive C-reactive protein/albumin ratio in primary prostate biopsy.MethodsRetrospective analysis was done on the clinical data of 1679 patients who had their first transrectal or perineal prostate biopsy at our situation from 2010 to 2018. Prostate cancer (PCa) and benign prostatic hyperplasia (BPH) were the pathologic diagnoses in 819 and 860 cases, respectively. A comparison was made between the HAR differences between PCa and BPH patients as well as the positive prostate biopsy rate differences between groups with increased and normal HAR. The results of the prostate biopsy were examined using logistic regression, and a model for predicting prostate cancer was created. The receiver characteristic curve (ROC) was used to determine the model’s prediction effectiveness. The clinical models integrated into HAR were evaluated for their potential to increase classification efficacy using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). According to the Gleason score (GS) categorization system, prostate cancer patients were separated into low, middle, and high GS groups. The differences in HAR between the various groups were then compared. The prevalence of high GSPCa and metastatic PCa in normal populations and the prevalence of higher HAR in prostate cancer patients were compared using the chi-square test.ResultPatients with PCa had a median HAR (upper quartile to lower quartile) of 0.0379 (10-3), patients with BPH had a median HAR (0.0137 (10-3)), and the difference was statistically significant (p<0.05). Patients with increased HAR and the normal group, respectively, had positive prostate biopsy rates of 52% (435/839)and 46% (384/840), and the difference was statistically significant (p<0.05). Logistic regression analysis showed that HAR (OR=3.391, 95%CI 2.082 ~ 4.977, P < 0.05), PSA density (PSAD) (OR=7.248, 95%CI 5.005 ~ 10.495, P < 0.05) and age (OR=1.076, 95%CI 1.056 ~ 1.096, P < 0.05) was an independent predictor of prostate biopsy results. Two prediction models are built: a clinical model based on age and PSAD, and a prediction model that adds HAR to the clinical model. The two models’ ROC had area under the curves (AUC) of 0.814 (95%CI 0.78-0.83) and 0.815 (95%CI 0.79-0.84), respectively. When compared to a single blood total PSA (tPSA) with an AUC of 0.746 (95%CI 0.718-0.774), they were all superior. Nevertheless, there was no statistically significant difference (p<0.05) between the two models. We assessed the prediction model integrated into HAR’s capacity to increase classification efficiency using NRI and IDI, and we discovered that NRI>0, IDI>0, and the difference was statistically significant (P>0.05).There was a statistically significant difference in HAR between various GS groups for individuals who had prostate cancer as a consequence of biopsy (p<0.05). The incidence of high GS and metastatic patients was statistically significantly greater (p<0.05) in the HAR elevated group (90.1%and 39.3%, respectively) than in the HAR normal group (84.4% and 12.0%).ConclusionProstate biopsy results that were positive were impacted by HAR, an independent factor that increased with the rate of PCa discovery. Patients with elevated HAR had a greater risk of high GS as well as metastatic PCa among those with recently diagnosed prostate cancer through prostate biopsy.

This study aimed to investigate the optimal screening interval of a prostate specific antigen (PSA) screening program for men aged 40-70 with a baseline PSA < 2 ng/mL in China. 8-year period clinical data of Chinese males who underwent physical examination annually in our hospital were retrospectively collected. 397 healthy males were included.Total PSA (tPSA) and free PSA (fPSA) were collected, and the free/total PSA ratio (f/t PSA) was calculated. According to the baseline PSA value, study population was divided into 2 groups: 0-0.99 ng/mL and 1-1.99 ng/mL. Prostate biopsy indicates at tPSA > 10 ng/mL, or 4-10 ng/mL (gray area) and f/t PSA < 0.16. Kaplan-Meier survival analysis was used to calculate the relevant cumulative incidence rate. Over the eight-year screening period, 27 people (6.8%) had abnormal PSA that met prostate biopsy criteria. 7 cases of prostate cancer were detected (detection rate 25.9%) among the 27 patients who performed a biopsy. In the 0-0.99 ng/mL and 1-1.99 ng/mL group, 4.1% (13/317) and 17.5% (14/80) achieved biopsy criteria within 8 years, with a statistically significant difference (p p < 0.001). In both groups, abnormal PSA began appearing in the sixth year. According to stratifying the cohort age and PSA, abnormal PSA levels began to appear in all subgroups by the sixth year, except for men aged < 50 years plus baseline PSA < 1 ng/mL, where they appeared by the seventh year. Furthermore, in the group of baseline 40-49 years and baseline PSA < 2 ng/mL, the probability of meeting biopsy indications during eight years was very low (1.4%, 2/143). Chinese men aged 50-70 with baseline PSA < 2 ng/mL should undergo for PSA retest in the sixth year, while aged 40-49 with a baseline PSA < 2 ng/mL do not need PSA screening within eight years.

Background The global morbidity and mortality of prostate cancer (PCa) increase sharply every year. Early diagnosis is essential; it determines survival and outcome. So, this study extracted the texture features of apparent diffusion coefficient images in multiparametric magnetic resonance imaging (mp-MRI) and built machine learning models based on radiomics texture analysis (TA) to determine its ability to distinguish benign from PCa lesions using the Prostate Imaging Reporting and Data System (PI-RADS) 4/5 score. Methods We enrolled 103 patients who underwent mp-MRI examinations and transrectal ultrasound and magnetic resonance fusion imaging (TRUS-MRI) targeted prostate biopsy and obtained pathological confirmation at our hospital from August 2017 to January 2020. We used ImageJ software to obtain texture feature parameters based on apparent diffusion coefficient (ADC) images, then standardized texture feature parameters, and used LASSO regression to reduce multiple feature parameters; 70% of the cases were randomly selected from the PCa group and the benign prostate hyperplasia group as the training set. The remaining 30% was used as the test set. The machine learning classification model for identifying benign and malignant prostate lesions was constructed using the feature parameters after dimensionality reduction. The clinical indicators were statistically analyzed, and we constructed a machine learning classification model based on clinical indicators of benign and malignant prostate lesions. Finally, we compared the model’s performance based on radiomics texture features and clinical indicators to identify benign and malignant prostate lesions in PI-RADS 4/5 score. Results The area under the curve (AUC) of the R-logistic model test set was 0.838, higher than the R-SVM and R-AdaBoost classification models. At this time, the corresponding R-logistic classification model formula is as follow: Y_radiomics=9.396-7.464*median ADC-0.584*kurtosis+0.627*skewness+0.576*MRI lesions volume; analysis of clinical indicators shows that the corresponding C-logistic classification model formula is as follows: Y_clinical =-2.608+0.324*PSA-3.045*Fib+4.147*LDL-C, the AUC value of the model training set was 0.860, smaller than the training set R-logistic classification model AUC value of 0.936. Conclusions Radiomics combined with the machine learning classifier model has strong classification performance in identifying benign and PCa in PI-RADS 4/5 score. Various treatments and outcomes for PCa patients can be applied clinically.

目的:探讨全前列腺表观弥散系数图纹理特征与前列腺癌根治术后病理Gleason评分(gleason score,GS)升高(gleason upgrading,GU)是否有相关性.方法:回顾性分析苏州大学附属第一医院2016年1月—2019年11月术前穿刺病理GS 6分的56例腹腔镜前列腺癌根治患者,计算出全前列腺和全前列腺基于阈值的ADC平均值、中位数、25和75百分位数、峰度、偏度和熵等ADC纹理参数.比较术后病理GS升高(GU)组和GS未升高(gleason non-upgrading,GN)组ADC纹理参数的差异;评价各参数和GU的相关性,ROC曲线评价各参数的诊断效能.结果:56例术前GS 6分患者根治术后GS 6分28例(50.00％),GU组的全前列腺ADC熵为(9.87±0.29),显著高于G N组的(9.65±0.43)(t=2.197,P=0.032<0.05);G U组的基于阈值的A D C平均值为(0.801±0.047)×10-3?m m2/s,显著低于GN组的(0.842±0.042)×10-3?mm2/s(t=3.413,P=0.001<0.05),全前列腺ADC熵和基于阈值的ADC平均值预测GU的AUC值分别为0.657和0.786.Logistic多因素回归分析显示全前列腺ADC熵高和基于阈值的ADC平均值低是GU的独立危险因素.结论:全前列腺ADC熵和基于阈值的ADC平均值与GU有明显相关性.

Background: To extract the texture features of Apparent Diffusion Coefficient (ADC) images in Mp-MRI and build a machine learning model based on radiomics texture analysis to determine its ability to distinguish benign from prostate cancer (PCa) lesions using PI-RADS 4/5 score.Materials and methods: First, use ImageJ software to obtain texture feature parameters based on ADC images; use R language to standardize texture feature parameters, and use Lasso regression to reduce the dimensionality of multiple feature parameters; then, use the feature parameters after dimensionality reduction to construct image-based groups. Learn R-Logistic, R-SVM, R-AdaBoost to identify the machine learning classification model of prostate benign and malignant nodules. Secondly, the clinical indicators of the patients were statistically analyzed, and the three clinical indicators with the largest AUC values were selected to establish a classification model based on clinical indicators of benign and malignant prostate nodules. Finally, compare the performance of the model based on radiomics texture features and clinical indicators to identify benign and malignant prostate nodules in PI-RADS 4/5.Results: The experimental results show that the AUC of the R-Logistic model test set is 0.838, which is higher than the R-SVM and R-AdaBoost classification models. At this time, the corresponding R-Logistic classification model formula is: Y_radiomics=9.396-7.464*median ADC-0.584 *kurtosis+0.627*skewness+0.576*MRI lesions volume; analysis of clinical indicators shows that the 3 indicators with the highest discrimination efficiency are PSA, Fib, LDL-C, and the corresponding C-Logistic classification model formula is: Y_clinical =-2.608 +0.324*PSA-3.045*Fib+4.147*LDL-C, the AUC value of the model training set is 0.860, which is smaller than the training set R-Logistic classification model AUC value of 0.936.Conclusion: The machine learning classifier model is established based on the texture features of radiomics. It has a good classification performance in identifying benign and malignant nodules of the prostate in PI-RADS 4/5. This has certain potential and clinical value for patients with prostate cancer to adopt different treatment methods and prognosis.

目的:评估全病灶表观弥散系数(ADC)图像的熵值对前列腺癌(PCa)的临床诊断价值及效能.方法:选取我院2017年8月—2020年1月接受经直肠超声与磁共振融合成像(TURS-MRI)靶向前列腺穿刺的并取得病理报告的110例患者作为研究对象,分为前列腺癌组和前列腺增生组.首先,所有患者穿刺前行多参数磁共振(mp-MRI)检查,并结合T2图像、扩散加权成像(DWI)和DCE图像以确定病灶大小及层面;其次,根据ADC图像对所有病灶进行勾画并计算其ADC熵值;再次,利用独立样本t检验评价两组患者的熵值差异是否显著;最后,采用受试者操作特征曲线(ROC)计算曲线下面积(AUC)来评定ADC熵值的效能.结果:前列腺增生组患者和前列腺癌组患者的全病灶ADC熵值比较差异有统计学意义(P＜0.01);ROC曲线下面积为90.20％,前列腺癌诊断的截点值为6.23.结论:全病灶ADC熵值的诊断效能较高,也即全病灶ADC熵值对前列腺癌的诊断具有一定的价值,可为临床诊治提供帮助.

目的:评估全病灶ADC定量分析对前列腺成像报告和数据系统版本2(PI-RADS v2)评分4/5分病灶患者的前列腺癌(PCa)诊断价值.方法:选取经直肠超声与磁共振融合(TURS-MRI)靶向前列腺穿刺患者中mp-MRI存在PI-RADS v2评分4/5分病灶的55例为研究对象.根据T2、DWI和ADC图确定病灶位置及层面,提取所有层面病灶每个像素ADC值,计算第25百分位(P25)ADC值、第75百分位(P75)ADC值、平均ADC值和中位ADC值,根据病理结果分为前列腺癌组(PCa组)和良性病灶组(BPH组);采用独立样本t检验比较两组之间有无差异;采用受试者操作特征(ROC)曲线计算曲线下面积(AUC),评定每个指标诊断PCa的效能.结果:PCa组P25 ADC值、P75 ADC值、平均ADC值、中位ADC值均低于BPH组,差异有统计学意义(P<0.05).ROC曲线结果分析显示,平均ADC值的ROC曲线下面积最大为0.813,当约登指数最大时,诊断PCa的平均ADC截点值为0.89×10-3mm2/s,敏感度和特异度分别为60.4％和100％.结论:全病灶ADC定量分析有助于PI-RADS v2评分诊断PCa.全病灶平均ADC值诊断PCa的AUC最大,提示全病灶平均ADC值在PI-RADS v2评分4/5分患者中的辅助诊断作用值得进一步研究.

To investigate the clinical effect of neoadjuvant chemotherapy on locally advanced prostate cancer, a case of prostate cancer in our hospital in 2019 was retrospectively analyzed. Preoperative PSA was 37.4ng/ml, clinical stage was T 4N 1M 0, and bone scan was negative. In order to reduce the difficulty of operation and postoperative complications, we treated the patients with neoadjuvant endocrine therapy plus 4 courses of neoadjuvant chemotherapy (docetaxel). Laparoscopic radical prostatectomy was successfully performed in the patient, and there was no biochemical recurrence after 1 year follow-up.

Abstract Background: Due to the different treatments for low-volume metastatic prostate cancer (PCa) as well as high-volume ones, evaluation of bone metastatic status is clinically significant. In this study, we evaluated the correlation between pre-treatment plasma fibrinogen and the burden of bone metastasis in newly diagnosed PCa patients. Methods: A single-center retrospective analysis, focusing on prostate biopsies of newly diagnosed PCa patients, was performed. A total of 261 patients were enrolled in this study in a 4-year period. All subjects were submitted to single-photon emission computerized tomography-computed tomography to confirm the status of bone metastasis and, if present, the number of metastatic lesions would then be calculated. Clinical information such as age, prostate-specific antigen (PSA), fibrinogen, clinical T stage, and Gleason score were collected. Patients were divided into three groups: (i) a non-metastatic group, (ii) a high volume disease (HVD) group (>3 metastases with at least one lesion outside the spine), and (iii) a low volume disease (LVD) group (metastatic patients excluding HVD ones). The main statistical methods included non-parametric Mann-Whitney test, Spearman correlation, receiver operating characteristic (ROC) curves, and logistic regression. Results: Fibrinogen positively correlated with Gleason score (r = 0.180, P = 0.003), PSA levels (r = 0.216, P < 0.001), and number of metastatic lesions (r = 0.296, P < 0.001). Compared with the non-metastatic and LVD groups, the HVD group showed the highest PSA (104.98 ng/mL, median) and fibrinogen levels (3.39 g/L, median), as well as the largest proportion of Gleason score >7 (86.8%). Both univariate (odds ratio [OR] = 2.16, 95% confidential interval [CI]: 1.536–3.038, P < 0.001) and multivariate (OR = 1.726, 95% CI: 1.206–2.472, P = 0.003) logistic regressions showed that fibrinogen was independently associated with HVD. The ROC curve suggested that fibrinogen acts as a predictor of HVD patients, yielding a cut-off of 3.08 g/L, with a sensitivity of 0.684 and a specificity of 0.760 (area under the curve = 0.739, 95% CI: 0.644–0.833, P < 0.001). Conclusions: Pre-treatment plasma fibrinogen is positively associated with bone metastatic burden in PCa patients. Our results indicate that fibrinogen might be a potential predictor of HVD.

目的 探讨外周血中性粒细胞计数(NC)和前列腺组织学炎症与良性前列腺增生(BPH)患者血清前列腺特异抗原(PSA)水平的关系.方法 回顾性分析行经直肠超声引导下前列腺穿刺活检和病理诊断为BPH的530例患者的临床资料.采用一元线性回归模型和多元线性回归模型,分析外周血中性粒细胞计数、前列腺组织学炎症和其他临床参数与血清PSA水平的关系.根据外周血中性粒细胞计数的四分位间距将BPH患者分为四组(中性粒细胞计数<25％ 、25％ ≤中性粒细胞计数<50％ 、50％ ≤中性粒细胞计数<75％ 、中性粒细胞计数≥75％),比较各组伴和不伴组织学炎症患者间血清PSA水平的差异.结果 一元线性回归模型显示,外周血中性粒细胞计数和组织学炎症均与血清PSA水平呈正相关(P<0.05);多元线性回归模型显示,外周血中性粒细胞计数是血清PSA水平的独立预测因子(P<0.05).四组伴和不伴组织学炎症患者之间血清PSA水平均无统计学差异(P>0.05).结论 BPH患者外周血中性粒细胞计数是血清PSA水平的独立预测因子,炎症可能引起BPH患者血清PSA水平的升高.

Objective To validate and compare the predictive accuracy of four prostate cancer models designed to predict the likelihood of a positive initial transrectal biopsy.Methods Clinical data of 813 consecutive patients between January 2010 and September 2014 who had undergone a transrectal ultrasound (TRUS) guided prostate biopsy at our institution were reviewed,431 patients fulfilling all criteria for four predictive models were enrolled for the final analysis.The risk of each individual positive biopsy was calculated using either of the four models.The predictive accuracy of each model was measured using area under the receiver operating characteristic curve (AUC),and the comparison of AUCs was performed by Z test.Results Of 431 participants,the statistical analysis of age,prostate-specific antigen (PSA),digital rectal examination (DRE),prostate volume and TRUS findings were all significantly different (P ＜ 0.05),except percentage of free prostate-specific antigen (％ fPSA) (P =0.242).AUCs were 0.774 (95％ CI 0.726-0.822),0.765 (95％ CI0.714-0.816),0.813 (95％ CI0.767-0.858),0.795 (95％ CI0.749-0.842) and 0.736 (95％ CI 0.684-0.788) for the North-American prostate cancer prevention trial derived cancer risk calculator (PCPT-CRC) model,Montreal model,domestic model 1,domestic model 2 and PSA alone,respectively.There was no significant difference among AUCs of the four models,and a 7.7％ increased predictive accuracy was observed for the domestic model 1 compared to unlimited PSA alone(P ＜0.05).When serum PSA ranging from 4 to 10 ng/ml,AUCs were 0.688 (95％ CI 0.560-0.816),0.818 (95％ CI0.719-0.918),0.830 (95％ CI0.740-0.919),0.853(95％ CI0.771-0.935) and 0.565(95％ CI 0.419-0.710) for the four models and PSA alone,respectively.Domestic model 2 owned the highest predictive accuracy and a 28.8％ increased predictive accuracy was observed for the domestic model 2 compared to PSA alone (P ＜ 0.05).Conclusions External validation and comparison of the four models reveals that all of the four models have acceptable predictive accuracy in our cohort.There is no difference of predictive accuracy between foreign and domestic models according to the AUC results.However,domestic model 1 is superior to unlimited PSA alone,and domestic model 2 has the highest predictive accuracy when serum PSA ranging from 4 to 10 ng/ml.

Objective To assess the correlation between high sensitive C-reactive protein and bone metastasis of patients with newly diagnosed prostate cancer.Methods From Jan.2010 to Dec.2015,a total of 294 consecutive patients with newly diagnosed prostate cancer by prostate biopsy were enrolled in this study.The median age was 70(65-75) years.There were 90(30.6％) patients with a positive DRE (digital rectal examination).The median prostate volume,PSA and PSAD were 36.5 ml(25.2-53.1 ml),32.95 ng/ml(14.49-82.89 ng/ml) and 0.90 ng/(ml · cm3) [0.44-1.95 ng/(ml · cm3)],respectively.There were 37 (12.6％) patients with a Gleason score ≤ 6,97 (33.0％) with a Gleason score of 7 and 160 (54.4％) with a Gleason score ≥ 8.Clinical stage was also evaluated,including 94(32.0％) diagnosed as T1 stage,132(44.9％) T2 stage,50(17.0％) T3 stage,and 18(6.1％) T4 stage.Regional lymph node metastases were found in 29 (9.9％) patients.All patients underwent bone scan and 59 patients showed bone metastases.One patient showed pulmonary metastases by computed tomography (CT).The difference of hs-CRP level between patients with bone metastasis and without bone metastasis was analyzed by MannWhitney U test.The difference of bone metastasis rate between the patients with elevated hs-CRP level (hsCRP ＞3.0 mg/L) and normal hs-CRP (hs-CRP≤3.0 mg/L) level was analyzed by Chi-squared test.Logistic regression was used to evaluate the effect of hs-CRP,prostate specific antigen (PSA),prostate specific antigen density (PSAD),Gleason score and clinical stage on bone metastasis.Areas under operating characteristic curves (AUC) were used to compare the predictive value of hs-CRP,PSA and PSAD.Restlts The hs-CRP level of the 294 patients ranged from 0.77 mg/L to 6.33 mg/L,with a median of 1.80 mg/L.The median (interquartile range) of hs-CRP was 6.90 mg/L (1.95-13.74 mg/L) in patients with bone metastasis which is higher than 1.43 mg/L (0.70-4.32 mg/L) in those without bone metastasis (P ＜ 0.05).The level of PSA,Gleason score and clinical stage were also significantly different between the two groups (P ＜ 0.05).The rate of bone metastasis in patients with elevated hs-CRP was 37.2％ (42/113),higher than that of patients with normal hs-CRP(P ＜ 0.001).According to the logistic regression analysis,hs-CRP (OR =1.149,95％ CI 1.080-1.222,P ＜ 0.05),PSA (OR =1.013,95％ CI 1.002-1.023,P ＜ 0.05) and Gleason score(OR =2.515,95％ CI 1.198-5.279,P ＜ 0.05) were significant independent predictors for bone metastasis.AUC of hs-CRP was 0.720 and the cutoff value was 3.1 mg/L.Conclusions High hs-CRP is significantly correlated with bone metastasis.Measurement of hs-CRP plays an important role in predicting bone metastasis among patients with newly diagnosed prostate cancer.

Background: The diagnostic value of current prostate-specific antigen (PSA) tests is challenged by the poor detection rate of prostate cancer (PCa) in repeat prostate biopsy. In this study, we proposed a novel PSA-related parameter named PSA density variation rate (PSADVR) and designed a clinical trial to evaluate its potential diagnostic value for detecting PCa on a second prostate biopsy. Methods: Data from 184 males who underwent second ultrasound-guided prostate biopsy 6 months after the first biopsy were included in the study. The subjects were divided into PCa and non-PCa groups according to the second biopsy pathological results. Prostate volume, PSA density (PSAD), free-total PSA ratio, and PSADVR were calculated according to corresponding formulas at the second biopsy. These parameters were compared using t-test or Mann-Whitney U-test between PCa and non-PCa groups, and receiver operating characteristic analysis were used to evaluate their predictability on PCa detection. Results: PCa was detected in 24 patients on the second biopsy. Mean values of PSA, PSAD, and PSADVR were greater in the PCa group than in the non-PCa group (8.39 μg/L vs. 7.16 μg/L, 0.20 vs. 0.16, 14.15% vs. −1.36%, respectively). PSADVR had the largest area under the curve, with 0.667 sensitivity and 0.824 specificity when the cutoff was 10%. The PCa detection rate was significantly greater in subjects with PSADVR >10% than PSADVR ≤10% (28.6% vs. 6.5%, P< 0.001). In addition, PSADVR was the only parameter in this study that showed a significant correlation with mid-to-high-risk PCa (r = 0.63, P = 0.03). Conclusions: Our results demonstrated that PSADVR improved the PCa detection rate on second biopsies, especially for mid-to-high-risk cancers requiring prompt treatment.

Objective To explore the clinical value of the concentration of Gal-3in prostatic fluid in the diagnosis of prostate cancer(PC).Methods Transrectal ultrasound-guided prostate biopsy was performed in 31 men suspected with prostate cancer.On the basis of pathological diagnosis,the patients were assigned into two groups of A(diagnosed as PC,12cases)and B(diagnosed as benign prostatic hyperplasia,19 cases).The concentration of Gal-3in prostatic fluid was detected by polyclonal antibody ELISA,which was compared between two groups.Results The concentration of Gal-3in prostatic fluid was higher in group A than that in group B(3703.3μg/ml vs.1028.4μg/ml)(P<0.01),which in group A was higher in PC patients with bone metastasis(4cases)than that in those without(8cases)[(5265.6±345.2)μg/ml vs.(2292.1±1325.4)μg/ml)](P<0.05).Taking4167μg/ml as a cutoff value of Gal-3in prostatic fluid,the sensitivity and speciality for the diagnosis of PC were 100% and 80%,respectively.The Gal-3expression in prostate tissues was higher in group A than that in group B(P<0.01).Conclusion The concentration of Gal-3in prostatic fluid can be taken as a valuable indicator in the diagnosis of PC and evaluation of its metastasis.

谢赣生医师:首先简要介绍一下病史.患者,男,63岁,因"体检发现右肾结石10年,右侧腰背胀痛伴低热1个月"入院.患者10年前行体检时,B超发现右肾结石(大小不详),未予处理.一年半前体检时腹部CT平扫提示右肾多发结石、右肾积水、肾皮质变薄,见图1,当地医院予中药调理,期间未查肾功能或行其他影像检查.一个月前患者感右侧腰背部胀痛,伴发热,体温37.5～38.5 ℃,感乏力、纳差.一周前小便出现浑浊,偶有絮状组织块排出,清晨偶见肉眼血尿,伴尿急、尿频.于当地医院门诊查尿常规,结果示:白细胞4+、红细胞+、尿蛋白2+.

[Abstract] Objective To define the age-specific normal reference values of prostate specific antigen (PSA) and related parameters in Chinese middle-aged and elderly men.Methods From April 2007 to November 2011,serum PSAs of over 22 055 men aged more than 40 years old in our medical examination center were statistically analyzed.The men was divided into five groups by a 10-year-old interval.Total PSA (tPSA),free PSA (fPSA) and prostate ultrasound results were recorded.The free-total PSA ratio (f/t),PSA density (PSAD) and PSA velocity (PSAV) were calculated.By convention,the 95th percentile (P95)was used as the upper limit value,and the 5th percentile (P5) as the lower limit value.Results The tPSAs were positively correlated with age (r=0.349,P＜0.001).f/t was negatively correlated with age (r=-0.154,P＜0.01).Although f/t was significantly different (P＜0.001) among each age group,P5 of all groups were 0.18.PSAD was significantly different (P＜0.001) between men over and under 70 years,with P95 as 0.09 and 0.15,respectively.PSAD had a positive correlation with age (r =0.263,P＜0.01).The significant difference of PSAV raised between men over and under 60 years,with P95 as 0.21 and 0.58,respectively.PSAV was positively correlated with age (r=0.130,P＜0.01).Conclusions PSA,PSAD and PSAV are positively correlated with age,while f/t is negatively correlated with age.The normal range of f/tis 0.18-1.00 for Chinese men over 40 years old.PSAD's normal ranges are ＜0.09 and ＜0.15 in Chinese men over and under 70 years,respectively.The normal range of PSAV are ＜0.21 and ＜0.58 for Chinese men over and under 60 years,respectively.