Clinical significance of serum high sensitive C-reactive protein/albumin ratio in primary prostate biopsy.

Objective: The purpose of this study was to investigate the clinical significance of serum high sensitive C-reactive protein/albumin ratio in primary prostate biopsy. Methods: Retrospective 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. Result: Patients 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%). Conclusion: Prostate 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.

Texture analysis based on PI-RADS 4/5-scored magnetic resonance images combined with machine learning to distinguish benign lesions from prostate cancer.

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.

Clinical association between pre-treatment levels of plasma fibrinogen and bone metastatic burden in newly diagnosed prostate cancer patients.

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.

Prostate-specific Antigen Density Variation Rate as a Potential Guideline Parameter for Second Prostate Cancer Detection Biopsy.

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.