Perilesional Targeted Biopsy Combined with MRI-TRUS Image Fusion-Guided Targeted Prostate Biopsy: An Analysis According to PI-RADS Scores.

A prostate-targeted biopsy (TB) core is usually collected from a site where magnetic resonance imaging (MRI) indicates possible cancer. However, the extent of the lesion is difficult to accurately predict using MRI or TB alone. Therefore, we performed several biopsies around the TB site (perilesional [p] TB) and analyzed the association between the positive cores obtained using TB and pTB and the Prostate Imaging Reporting and Data System (PI-RADS) scores. This retrospective study included patients who underwent prostate biopsies. The extent of pTB was defined as the area within 10 mm of a TB site. A total of 162 eligible patients were enrolled. Prostate cancer (PCa) was diagnosed in 75.2% of patients undergoing TB, with a positivity rate of 50.7% for a PI-RADS score of 3, 95.8% for a PI-RADS score of 4, and 100% for a PI-RADS score of 5. Patients diagnosed with PCa according to both TB and pTB had significantly higher positivity rates for PI-RADS scores of 4 and 5 than for a PI-RADS score of 3 (p < 0.0001 and p = 0.0009, respectively). Additional pTB may be performed in patients with PI-RADS ≥ 4 regions of interest for assessing PCa malignancy.

Clinical and prostate multiparametric magnetic resonance imaging findings as predictors of general and clinically significant prostate cancer risk: A retrospective single-center study.

Background: To evaluate the predictive values of Prostate Imaging Reporting and Data System version 2 (PI-RADS v2), prostate-specific antigen (PSA) level, PSA density (PSAD), digital rectal examination findings, and prostate volume, individually and in combination, for the detection of prostate cancer (PCa) in biopsy-naive patients. Methods: We retrospectively analyzed 630 patients who underwent transrectal systematic prostate biopsy following prostate multiparametric magnetic resonance imaging. A standard 12-core biopsy procedure was performed. Univariate and multivariate analyses were performed to determine the significant predictors of clinically significant cancer but not PCa. Results: The median age, PSA level, and PSAD were 70 years, 8.6 ng/mL, and 0.18 ng/mL/mL, respectively. A total of 374 (59.4%) of 630 patients were biopsy-positive for PCa, and 241 (64.4%) of 374 were diagnosed with clinically significant PCa (csPCa). The PI-RADS v2 score and PSAD were independent predictors of PCa and csPCa. The PI-RADS v2 score of 5 regardless of the PSAD value, or PI-RADS v2 score of 4 plus a PSAD of <0.3 ng/mL/mL, was associated with the highest csPCa detection rate (36.1%-82.1%). Instead, the PI-RADS v2 score of <3 and PSAD of <0.3 ng/mL/mL yielded the lowest risk of csPCa. Conclusion: The combination of the PI-RADS v2 score and PSAD could prove to be a helpful and reliable diagnostic tool before performing prostate biopsies. Patients with a PI-RADS v2 score of <3 and PSAD of <0.3 ng/mL/mL could potentially avoid a prostate biopsy.

Combination of PI-RADS score and PSAD can improve the diagnostic accuracy of prostate cancer and reduce unnecessary prostate biopsies.

Objectives: The purpose of this study is to evaluate the diagnostic accuracy of the clinical variables of patients with prostate cancer (PCa) and to provide a strategy to reduce unnecessary biopsies. Patients and methods: A Chinese cohort that consists of 833 consecutive patients who underwent prostate biopsies from January 2018 to April 2022 was collected in this retrospective study. Diagnostic ability for total PCa and clinically significant PCa (csPCa) was evaluated by prostate imaging-reporting and data system (PI-RADS) score and other clinical variables. Univariate and multivariable logistic regression analyses were performed to figure out the independent predictors. Diagnostic accuracy was estimated by plotting receiver operating characteristic curves. Results: The results of univariate and multivariable analyses demonstrated that the PI-RADS score (P < 0.001, OR: 5.724, 95% CI: 4.517-7.253)/(P < 0.001, OR: 5.199, 95% CI: 4.039-6.488) and prostate-specific antigen density (PSAD) (P < 0.001, OR: 2.756, 95% CI: 1.560-4.870)/(P < 0.001, OR: 4.726, 95% CI: 2.661-8.396) were the independent clinical factors for predicting total PCa/csPCa. The combination of the PI-RADS score and PSAD presented the best diagnostic performance for the detection of PCa and csPCa. For the diagnostic criterion of "PI-RADS score ≥ 3 or PSAD ≥ 0.3", the sensitivity and negative predictive values were 94.0% and 93.1% for the diagnosis of total PCa and 99.2% and 99.3% for the diagnosis of csPCa, respectively. For the diagnostic criterion "PI-RADS score >3 and PSAD ≥ 0.3", the specificity and positive predictive values were 96.8% and 92.6% for the diagnosis of total PCa and 93.5% and 82.4% for the diagnosis of csPCa, respectively. Conclusions: The combination of the PI-RADS score and PSAD can implement the extraordinary diagnostic performance of PCa. Many patients may safely execute active surveillance or take systematic treatment without prostate biopsies by stratification according to the PI-RADS score and the value of PSAD.

Combining prostate-specific antigen density with prostate imaging reporting and data system score version 2.1 to improve detection of clinically significant prostate cancer: A retrospective study.

Globally, Prostate cancer (PCa) is the second most common cancer in the male population worldwide, but clinically significant prostate cancer (CSPCa) is more aggressive and causes to more deaths. The authors aimed to construct the risk category based on Prostate Imaging Reporting and Data System score version 2.1 (PI-RADS v2.1) in combination with Prostate-Specific Antigen Density (PSAD) to improve CSPCa detection and avoid unnecessary biopsy. Univariate and multivariate logistic regression and receiver-operating characteristic (ROC) curves were performed to compare the efficacy of the different predictors. The results revealed that PI-RADS v2.1 score and PSAD were independent predictors for CSPCa. Moreover, the combined factor shows a significantly higher predictive value than each single variable for the diagnosis of CSPCa. According to the risk stratification model constructed based on PI-RADS v2.1 score and PSAD, patients with PI-RADS v2.1 score of ≤2, or PI-RADS V2.1 score of 3 and PSA density of <0.15 ng/mL2, can avoid unnecessary of prostate biopsy and does not miss clinically significant prostate cancer.

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.

[PI-RADS score of prostate multi-parametric magnetic resonance imaging predicts pathological features of prostate cancer: An imaging to whole-mount pathology matching study].

OBJECTIVE: To explore the value of the prostate imaging reporting and data system (PI-RADS) score of prostate multi-parametric magnetic resonance imaging (mpMRI) in predicting the pathological features of PCa based on matching images and whole-mount pathology images. METHODS: This retrospective study included 318 cases of PCa treated by radical prostatectomy in our hospital from August 2016 to December 2018, with preoperative mpMRI images and complete whole-mount pathological sections. We obtained PI-RADS scores on the mpMRI lesions corresponding to the cancer lesions, evaluated the Gleason scores, pT stages, pN stages and cribriform structure, and compared them between different groups using Chi-square test or Fisher's exact test. We evaluated the efficiency of the PI-RADS score in distinguishing different pathological features by ROC curve analysis, and obtained the corresponding area under the curve (AUC) and 95% confidence interval (CI). RESULTS: The 318 patients averaged 69 years of age, with a median preoperative PSA level of 11.0 µg/L and a median tumor diameter of 1.8 cm. The PI-RADS score was significantly correlated with the Gleason score, pT stage, pN stage and cribriform structure (all P < 0.01), with AUCs of 0.773 (95% CI: 0.704－0.843) for distinguishing Gleason scores (3+3 vs >3+3), 0.748 (95% CI: 0.694－0.803) for distinguishing pT stages (T2 vs >T2), 0.700 (95% CI: 0.598－0.802) for distinguishing pN stages (N0 vs N1), and 0.831 (95% CI: 0.786－0.876) for distinguishing the cribriform structure (negative vs positive). CONCLUSION: The preoperative PI-RADS score of mpMRI in PCa patients is significantly correlated with postoperative pathological features, and therefore can be used for risk stratification of the malignancy.

Evaluating the efficacy of a low-cost cognitive MRI-targeted prostate biopsy protocol: is there still a role for lower volume centers in the Prostate Imaging Reporting and Data System (PI-RADS) version 2 era?

PURPOSE: MRI-targeted biopsy has improved prostate biopsy yield. However, cost constraints have made it difficult for many institutions to implement the newer methods. We evaluated the performance of a low-cost cognitive-targeting biopsy protocol based on 1.5 T multiparametric MRI graded with Prostate Imaging Reporting and Data System (PI-RADS) version 2 to examine the role for these institutions moving forward. METHODS: Retrospective analysis of 251 consecutive patients with prostate-specific antigen (PSA) under 50 who underwent MRI and subsequent prostate biopsy at a single facility. In addition to systematic biopsy, targeted cores were obtained with cognitive recognition under ultrasound. A control group of 267 consecutive patients with PSA under 50 biopsied without prior MRI was analyzed. RESULTS: Prostate biopsy preceded by MRI had a significantly higher probability of detecting both prostate cancer (68.1% vs. 51.3%) and clinically significant prostate cancer (57.4% vs. 39.7%) (p values < 0.01). Combination of systematic and targeted biopsy outperformed either regimen alone. PSA density and PI-RADS score were identified as independent risk factors, and a proposed diagnostic model (PSA density ≥ 0.25 or PI-RADS score ≥ 4) showed sensitivity of 88.6%, specificity of 55%, PPV of 81.2%, NPV of 68.8%, and accuracy of 78.0%. CONCLUSIONS: Both pre-biopsy MRI and cognitive-targeted biopsy contributed to improvement of cancer yield. Future alterations of possible benefit included increasing target cores per lesion, and combining PI-RADS score and PSA density as indicators for biopsy. Similar protocols may represent an on-going role for lower volume centers in the diagnosis of prostate cancer.

The characteristics and independent associated factors of positive surgical margin after radical prostatectomy / 中华泌尿外科杂志

Objective To analyze the associated factors of positive surgical margin after radical prostatectomy.Methods A retrospective analysis was conducted on 320 patients who underwent radical prostatectomy from June 2007 to June 2017,whose age was 45-80 years(mean 67.9) and PSA was 0.05-123.19 ng/ml (mean 14.4ng/ml).The patients were divided into groups by age,PSA,PI-RADS score,clinical stage,biopsy Gleason score and operation approach.Chi-square test was used for single factor analysis and binary logistic regression analysis for multivariate analysis to evaluate the correlation between clinical and pathological data and positive cutting edge.Result Among the total 320 patients,there were 94 (29.4％) patients had positive surgical margin after radical prostatectomy.There were 26 (21.0％) positive surgical margin located at ventral sites,18(14.5％) located at dorsal sites,21 (16.9％) located at base,and 59(47.6％) located at tip.The positive rate of surgical margin was different in different positive areas of MRI (P ＜0.01),among which the MRI showed cancer located in the tip of prostate had the highest positive rate (47.6％) of surgical margin after prostatectomy.Univariate risk factor analysis was performed which showed that PSA(P =0.023),positive needle percentage (P =0.001),biopsy pathologic Gleason score(P =0.029),PI-RADS score (P =0.022) and prostate cancer risk score (P =0.006) had significant correlation with positive surgical margin.The age (P =0.257),clinical stage (P =0.161) and operation approch (P =0.260) had no significant correlation.Then multivariate analysis showed that PI-RADS score (P =0.023) and positive needle percentage (P =0.047) could be used as independent predictors of positive surgical margin.Conclusions PI-RADS score and percentage of positive biopsy needles were independent risk factors for positive surgical margin after prostatectomy.There was highest positive rate of surgical margin when MRI showed cancer located at the tip of prostate.