Values of multiparametric and biparametric MRI in diagnosing clinically significant prostate cancer: a multivariate analysis.

BACKGROUND: To investigate the value of semi-quantitative and quantitative parameters (PI-RADS score, T2WI score, ADC, Ktrans, and Kep) based on multiparametric MRI (mpMRI) or biparametric MRI (bpMRI) combined with prostate specific antigen density (PSAD) in detecting clinically significant prostate cancer (csPCa). METHODS: A total of 561 patients (276 with csPCa; 285 with non-csPCa) with biopsy-confirmed prostate diseases who underwent preoperative mpMRI were included. Prostate volume was measured for calculation of PSAD. Prostate index lesions were scored on a five-point scale on T2WI images (T2WI score) and mpMRI images (PI-RADS score) according to the PI-RADS v2.1 scoring standard. DWI and DCE-MRI images were processed to measure the quantitative parameters of the index lesion, including ADC, Kep, and Ktrans values. The predictors of csPCa were screened by logistics regression analysis. Predictive models of bpMRI and mpMRI were established. ROC curves were used to evaluate the efficacy of parameters and the model in diagnosing csPCa. RESULTS: The independent diagnostic accuracy of PSA density, PI-RADS score, T2WI score, ADCrec, Ktrans, and Kep for csPCa were 80.2%, 89.5%, 88.3%, 84.6%, 58.5% and 61.6%, respectively. The diagnostic accuracy of bpMRI T2WI score and ADC value combined with PSAD was higher than that of PI-RADS score. The combination of mpMRI PI­RADS score, ADC value with PSAD had the highest diagnostic accuracy. CONCLUSIONS: PI-RADS score according to the PI-RADS v2.1 scoring standard was the most accurate independent diagnostic index. The predictive value of bpMRI model for csPCa was slightly lower than that of mpMRI model, but higher than that of PI-RADS score.

Prostate health index (PHI) as a reliable biomarker for prostate cancer: a systematic review and meta-analysis.

OBJECTIVES: Prostate cancer (PCa) represents the second most common solid cancer in men worldwide. In the last decades, the prostate health index (PHI) emerged as a reliable biomarker for detecting PCa and differentiating between non-aggressive and aggressive forms. However, before introducing it in clinical practice, more evidence is required. Thus, we performed a systematic review and meta-analysis for assessing the diagnostic performance of PHI for PCa and for detecting clinically significant PCa (csPCa). METHODS: Relevant publications were identified by a systematic literature search on PubMed and Web of Science from inception to January 11, 2022. RESULTS: Sixty studies, including 14,255 individuals, met the inclusion criteria for our meta-analysis. The pooled sensitivity and specificity of PHI for PCa detection was 0.791 (95%CI 0.739-0.834) and 0.625 (95%CI 0.560-0.686), respectively. The pooled sensitivity and specificity of PHI for csPCa detection was 0.874 (95%CI 0.803-0.923) and 0.569 (95%CI 0.458-0.674), respectively. Additionally, the diagnostic odds ratio was 6.302 and 9.206, respectively, for PCa and csPCa detection, suggesting moderate to good effectiveness of PHI as a diagnostic test. CONCLUSIONS: PHI has a high accuracy for detecting PCa and discriminating between aggressive and non-aggressive PCa. Thus, it could be useful as a biomarker in predicting patients harbouring more aggressive cancer and guiding biopsy decisions.

How to make clinical decisions to avoid unnecessary prostate screening in biopsy-naïve men with PI-RADs v2 score ≤ 3?

PURPOSE: To determine whether patients can avoid systematic prostate biopsy (PBx) if their Prostate Imaging Reporting and Data System version 2 (PI-RADs v2) score is ≤ 3 and how we clinicians make decisions that can maximize benefit. MATERIALS AND METHODS: We reviewed our prospectively maintained database of consecutive men who received transrectal ultrasound-guided 24-core biopsy as well as pre-biopsy multi-parametric magnetic resonance imaging (mp-MRI). Of the 1276 men who were performed PBx in our institution from 2012 to July 2018, 491 patients conformed to the criteria. Negative predictive value (NPV) of negative mp-MRI (defined as PI-RADs < 3) combined prostate-specific antigen density (PSAD) were calculated. Models based on PI-RADs v2 were developed to predict the absence of clinically significant prostate cancer (CSPCa) and prostate cancer (PCa). Nomograms as well as receiver operating curves (ROC) were established to estimate the discrimination. Calibration curves were used to assess the concordance between predictive value and true risk. Decision curves were made to measure the overall net benefit. RESULTS: Prostate cancer and CSPCa detection rates were 21.6%, 7.3% and 36.7%, 23.4% in PIRADs v2 < 3 cohort and PIRADs v2 = 3 cohort, respectively. Men with biopsy-proved CSPCa had higher prostate-specific antigen (PSA), lower prostate volume (PV) and higher PSAD (all p < 0.05 in the two cohorts) than patients with clinically insignificant prostate cancer (CIPCa) or negative results. NPV of negative mp-MRI for detection of PCa was much higher when the PSAD was less than 0.15 (p < 0.001) and 0.2 for CSPCa (p = 0.007). According to multivariate analysis, we developed the model comprising Age, PSAD and PI-RADs v2 to predict the absence of CSPCa and PCa. The area under the curve (AUC) of the model for non-CSPCa was 0.75 (95% CI 0.68-0.80, PSAD cutoff 0.20), better than 0.71 (95% CI 0.65-0.80, PSAD cutoff 0.15). As for model for non-PCa, the AUC was 0.76 (95% CI 0.70-0.80, PSAD cutoff 0.15), higher than 0.71(95% CI 0.67-0.78, PSAD cutoff 0.20). Internally validated calibration curves showed that the model might overestimated the risk of the absence of CSPCa when the threshold was between 53 and 72%, and if the threshold was between 72 and 87%, it might underestimate the risk. As for the absence of PCa, the model might overestimate the risk between 52 and 76%. Decision curves showed that a better clinical net benefit was met when the threshold was 55% for non-PCa and 70% for non-CSPCa. CONCLUSIONS: NPV of negative mp-MRI for detection of CSPCa and PCa was improved with decreasing PSAD. The nomograms based on PI-RADs v2, age and PSAD showed internally validated high discrimination and calibration for the absence of PCa and CSPCa. When the predictive value was greater than 70% for the absence of CSPCa and 55% for the absence of PCa, we could avoid unnecessary PBx to maximize net benefit.

Clinical and Radiological Factors for Predicting Clinically Significant Prostate Cancer in Biopsy-Naive Patients With PI-RADS 3 Lesions.

OBJECTIVE: This study intends to examine the anticipatory power of clinical and radiological parameters in detecting clinically significant prostate cancer in patients demonstrating Prostate Imaging Reporting and Data System 3 lesions. METHODS: This was a retrospective study. The study included participation from 453 patients at the First Affiliated Hospital of Soochow University, sampled between September 2017 through August 2022. Each patient underwent a routine 12-core prostate biopsy followed by a 2 to 5 core fusion-targeted biopsy. We utilized both univariate and multivariate logistic regression analyses to identify the parameters that have a correlation with clinically significant prostate cancer. The predictive ability of these parameters was assessed using the receiver operating characteristic curve, leading to the creation of a nomogram. RESULTS: Clinically significant prostate cancer was detected in 68 out of 453 patients with Prostate Imaging Reporting and Data System 3 lesions (15.01%). Among Prostate Imaging Reporting and Data System 3a and 3b patients, 4.78% (3.09% of the total) and 33.75% (11.92% of the total), respectively, had clinically significant prostate cancer. Systematic biopsy improved prostate cancer and clinically significant prostate cancer detection rates by 7.72% and 3.09%, respectively, compared to targeted biopsy. Without systematic biopsy, there would be an undetected rate of 15% for prostate cancer and 8.13% for clinically significant prostate cancer in Prostate Imaging Reporting and Data System 3b patients. Several clinical parameters, including age, prostate-specific antigen density, lesion volume, apparent diffusion coefficient, and digital rectal examination, were statistically significant in the logistic regression analysis for clinically significant prostate cancer. The individual diagnostic accuracies of these parameters for clinically significant prostate cancer were 0.648, 0.645, 0.75, 0.763, and 0.7, respectively, but their combined accuracy improved to 0.866. A well-fit nomogram based on the identified risk factors was constructed (χ2 = 10.254, P = .248). CONCLUSION: The combination of age, prostate-specific antigen density, lesion volume, apparent diffusion coefficient, and digital rectal examination presented a higher diagnostic value for clinically significant prostate cancer than any single parameter in patients with Prostate Imaging Reporting and Data System 3 lesions. Systematic biopsy proved crucial for biopsy-naive patients with Prostate Imaging Reporting and Data System 3 lesions and should not be omitted.

Using an artificial intelligence model to detect and localize visible clinically significant prostate cancer in prostate magnetic resonance imaging: a multicenter external validation study.

Background: An increasing number of patients with suspected clinically significant prostate cancer (csPCa) are undergoing prostate multiparametric magnetic resonance imaging (mpMRI). The role of artificial intelligence (AI) algorithms in interpreting prostate mpMRI needs to be tested with multicenter external data. This study aimed to investigate the diagnostic efficacy of an AI model in detecting and localizing visible csPCa on mpMRI a multicenter external data set. Methods: The data of 2,105 patients suspected of having prostate cancer from four hospitals were retrospectively collected to develop an AI model to detect and localize suspicious csPCa. The lesions were annotated based on pathology records by two radiologists. Diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values were used as the input for the three-dimensional U-Net framework. Subsequently, the model was validated using an external data set comprising the data of 557 patients from three hospitals. Sensitivity, specificity, and accuracy were employed to evaluate the diagnostic efficacy of the model. Results: At the lesion level, the model had a sensitivity of 0.654. At the overall sextant level, the model had a sensitivity, specificity, and accuracy of 0.846, 0.884, and 0.874, respectively. At the patient level, the model had a sensitivity, specificity, and accuracy of 0.943, 0.776, and 0.849, respectively. The AI-predicted accuracy for the csPCa patients (231/245, 0.943) was significantly higher than that for the non-csPCa patients (242/312, 0.776) (P<0.001). The lesion number and tumor volume were greater in the correctly diagnosed patients than the incorrectly diagnosed patients (both P<0.001). Among the positive patients, those with lower average ADC values had a higher rate of correct diagnosis than those with higher average ADC values (P=0.01). Conclusions: The AI model exhibited acceptable accuracy in detecting and localizing visible csPCa at the patient and sextant levels. However, further improvements need to be made to enhance the sensitivity of the model at the lesion level.

Incidental prostate cancer in patients with bladder urothelial carcinoma: comprehensive analysis of 1,476 radical cystoprostatectomy specimens.

PURPOSE: We identified risk factors and determined the incidence and prognosis of incidental, clinically significant prostatic adenocarcinoma, prostatic urothelial carcinoma and HGPIN in patients treated with radical cystoprostatectomy for urothelial carcinoma of the bladder. MATERIALS AND METHODS: We analyzed the records of 1,476 patients without a history of prostatic adenocarcinoma. We determined the incidence of clinically significant prostatic adenocarcinoma, prostatic urothelial carcinoma and HGPIN in the total cohort and in select patient subgroups. Prostatic urothelial carcinoma was stratified as prostatic stromal and prostatic urethral/duct involvement. Univariate and multivariate analyses were performed with multiple variables. Recurrence-free and overall survival rates were calculated. Median followup was 13.2 years. RESULTS: Of the 1,476 patients 753 (51.0%) had cancer involving the prostate. Prostatic adenocarcinoma, clinically significant prostatic adenocarcinoma, prostatic urothelial carcinoma and HGPIN were present in 37.9%, 8.3%, 21.1% and 51.2% of patients, respectively. Of the 312 patients (21.1%) with prostatic urothelial carcinoma 163 (11.0%) had prostatic urethral/duct involvement only and 149 (10.1%) had prostatic stromal involvement. We identified risk factors for clinically significant prostatic adenocarcinoma, prostatic urothelial carcinoma and HGPIN but the absence of these risk factors did not rule out their presence. Ten-year overall survival in patients with no prostatic urothelial carcinoma, and prostatic urethral/duct and prostatic stromal involvement was 47.1%, 43.3% and 21.7%, respectively (p<0.001). No patient with clinically significant prostatic adenocarcinoma died of prostatic cancer. CONCLUSIONS: More than half of the patients undergoing radical cystoprostatectomy had cancer involving the prostate. Prostatic urothelial carcinoma, particularly with prostatic stromal involvement, was associated with a worse prognosis, while clinically significant prostatic adenocarcinoma did not alter survival. Preoperative clinical and histopathological risk factors are not reliable enough to accurately predict clinically significant prostatic adenocarcinoma and/or prostatic urothelial carcinoma.

Development and external validation of a novel nomogram to predict prostate cancer in biopsy-naïve patients with PSA <10 ng/ml and PI-RADS v2.1 = 3 lesions.

OBJECTIVE: To develop and externally validate a novel nomogram in biopsy-naïve patients with prostate-specific antigen (PSA) <10 ng/ml and PI-RADS v2.1 = 3 lesions. METHODS: We retrospectively collected 307 men that underwent initial biopsy from October 2015 to January 2022 in Cohort 1 (The First Affiliated Hospital of Soochow University). External cohort (Cohort 2, Kunshan Hospital) included 109 men that met our criteria from July 2016 to June 2021. By Slicer-3D Software, the volume of all lesions was divided into two subgroups (PI-RADS v2.1 = 3a and 3b). Logistic regression analysis was performed to screen for variables and construct nomogram by analyzing clinical data from Cohort 1. Receiver operating characteristics curve analysis, calibration plot and decision curve analysis (DCA) were plotted to validate the nomogram in external cohort. RESULTS: A total of 70 (22.8%) patients was diagnosed with prostate cancer in Institution 1. Among them, 34 (11.1%) had clinically significant prostate cancer (csPCa). Age, prostate-specific antigen density, digital rectal examination, PI-RADS v2.1 = 3 subgroups (3a and 3b) and apparent diffusion coefficient (ADC, <750 mm2 /s) were predictive factors for prostate cancer (PCa) and csPCa. High area under the curve of the nomogram was found in Cohort 1 and Cohort 2 for PCa (0.857 vs. 0.850) and for csPCa (0.896 vs. 0.893). Calibration curves showed excellent agreement between the predicted probability and actual risk for the models in internal and external validation. The DCA demonstrated net benefit of our nomogram. CONCLUSION: Until now, this is the first nomogram that predicts PCa and csPCa in biopsy-naïve patients with PSA <10 ng/ml and PI-RADS v2.1 = 3 lesions. Furthermore, PI-RADS v2.1 = 3 subgroups were considered to be an independent risk factor in our model. Our nomogram may assist urologists in biopsy decision making for these so-called "double gray zone" patients.

Improving reader accuracy and specificity with the addition of hybrid multidimensional-MRI to multiparametric-MRI in diagnosing clinically significant prostate cancers.

PURPOSE: Compare reader performance when adding the Hybrid Multidimensional-MRI (HM-MRI) map to multiparametric MRI (mpMRI+HM-MRI) versus mpMRI alone and inter-reader agreement in diagnosing clinically significant prostate cancers (CSPCa). METHODS: All 61 patients who underwent mpMRI (T2-, diffusion-weighted (DWI), and contrast-enhanced scans) and HM-MRI (with multiple TE/b-value combinations) before prostatectomy or MRI-fused-transrectal ultrasound-guided biopsy between August, 2012 and February, 2020, were retrospectively analyzed. Two experienced readers (R1, R2) and two less-experienced readers (less than 6-year MRI prostate experience) (R3, R4) interpreted mpMRI without/with HM-MRI in the same sitting. Readers recorded the PI-RADS 3-5 score, lesion location, and change in score after adding HM-MRI. Each radiologist's mpMRI+HM-MRI and mpMRI performance measures (AUC, sensitivity, specificity, PPV, NPV, and accuracy) based on pathology, and Fleiss' kappa inter-reader agreement was calculated and compared. RESULTS: Per-sextant R3 and R4 mpMRI+HM-MRI accuracy (82% 81% vs. 77%, 71%; p=.006, <.001) and specificity (89%, 88% vs. 84%, 75%; p=.009, <.001) were higher than with mpMRI. Per-patient R4 mpMRI+HM-MRI specificity improved (48% from 7%; p<.001). R1 and R2 mpMRI+HM-MRI specificity per-sextant (80%, 93% vs. 81%, 93%; p=.51,>.99) and per-patient (37%, 41% vs. 48%, 37%; p=.16, .57) remained similar to mpMRI. R1 and R2 per-patient AUC with mpMRI+HM-MRI (0.63, 0.64 vs. 0.67, 0.61; p=.33, .36) remained similar to mpMRI, but R3 and R4 mpMRI+HM-MRI AUC (0.73, 0.62) approached R1 and R2 AUC. Per-patient inter-reader agreement, mpMRI+HM-MRI Fleiss Kappa, was higher than mpMRI (0.36 [95% CI 0.26, 0.46] vs. 0.17 [95% CI 0.07, 0.27]); p=.009). CONCLUSION: Adding HM-MRI to mpMRI (mpMRI+HM-MRI) improved specificity and accuracy for less-experienced readers, improving overall inter-reader agreement.

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.

Synthetic magnetic resonance imaging for primary prostate cancer evaluation: Diagnostic potential of a non-contrast-enhanced bi-parametric approach enhanced with relaxometry measurements.

PURPOSE: Bi-parametric magnetic resonance imaging (bpMRI) with diffusion-weighted images has wide utility in diagnosing clinically significant prostate cancer (csPCa). However, bpMRI yields more false-negatives for PI-RADS category 3 lesions than multiparametric (mp)MRI with dynamic-contrast-enhanced (DCE)-MRI. We investigated the utility of synthetic MRI with relaxometry maps for bpMRI-based diagnosis of csPCa. METHODS: One hundred and five treatment-naïve patients who underwent mpMRI and synthetic MRI before prostate biopsy for suspected PCa between August 2019 and December 2020 were prospectively included. Three experts and three basic prostate radiologists evaluated the diagnostic performance of conventional bpMRI and synthetic bpMRI for csPCa. PI-RADS version 2.1 category 3 lesions were identified by consensus, and relaxometry measurements (T1-value, T2-value, and proton density [PD]) were performed. The diagnostic performance of relaxometry measurements for PI-RADS category 3 lesions in peripheral zone was compared with that of DCE-MRI. Histopathological evaluation results were used as the reference standard. Statistical analysis was performed using the areas under the receiver operating characteristic curve (AUC) and McNemar test. RESULTS: In 102 patients without significant MRI artefacts, the diagnostic performance of conventional bpMRI was not significantly different from that of synthetic bpMRI for all readers (p = 0.11-0.79). The AUCs of the combination of T1-value, T2-value, and PD (T1 + T2 + PD) for csPCa in peripheral zone for PI-RADS category 3 lesions were 0.85 for expert and 0.86 for basic radiologists, with no significant difference between T1 + T2 + PD and DCE-MRI for both expert and basic radiologists (p = 0.29-0.45). CONCLUSION: Synthetic MRI with relaxometry maps shows promise for contrast media-free evaluation of csPCa.

Performing Precise Biopsy in Naive Patients With Equivocal PI-RADS, Version 2, Score 3, Lesions: An MRI-based Nomogram to Avoid Unnecessary Surgical Intervention.

PURPOSE: The primary objective of the present study was to avoid unnecessary prostate biopsy in biopsy-naive patients with Prostate Imaging Reporting and Data System, version 2 (PI-RADS v2), score 3, lesions. MATERIALS AND METHODS: We reviewed our prospectively maintained database from January 2012 to July 2018. Logistic regression analyses were performed to test different clinical factors as predictors of clinically significant prostate cancer (CSPCa) and build nomograms. Calibration curves were used to assess the concordance between the predictive value and the true risk. Decision curves were created to measure the overall net benefit. RESULTS: The prostate cancer (PCa) and CSPCa detection rates were 37.2% (81 of 218) and 23.9% (52 of 218) in the PI-RADS v2, score 3, cohort. More PCa cases (61.7%; 50 of 81) and CSPCa cases (75%; 39 of 52) were found in the peripheral zone than in the transitional zone. Multivariate analysis showed that age, prostate-specific antigen density, lesion region, and apparent diffusion coefficient (ADC) were predictive factors for CSPCa and PCa. Internally validated calibration curves showed that the predicted risk of CSPCa was closer to the actual probability when the threshold was > 60%. Decision curves showed that a better net benefit was achieved when the model was used to guide clinical practice. CONCLUSIONS: More cases of PCa and CSPCa were seen in the peripheral zone than in the transitional zone among patients with PI-RADS v2, score 3. The positive predictive value for a positive ADC (< 900 µm2/s) for the detection of CSPCa and PCa improved with an increasing prostate-specific antigen density. Biopsy can be avoided if the equivocal lesion has a negative ADC (> 900 µm2/s) and was in the transition zone.

Which clinical and radiological characteristics can predict clinically significant prostate cancer in PI-RADS 3 lesions? A retrospective study in a high-volume academic center.

OBJECTIVE: To investigate which clinical and radiological characteristics can predict clinically significant prostate cancer (csPCa) in PI-RADS 3 lesions. To investigate which clinical and radiological characteristics influence the clinician to biopsy a PI-RADS 3 lesion. MATERIALS AND METHODS: mpMRI PI-RADS 3 lesions scored by 1 out of 3 highly specialized radiologists in a single high-volume center during the period March 2015 to August 2017 were investigated. This score was based on T2 weighted and diffusion weighted imaging (DWI) sequences. Clinical characteristics of all patients with PI-RADS 3 lesions were collected from medical records. Radiological characteristics were collected from radiology reports. Some radiological characteristics such as apparent diffusion coefficient (ADC) in a region of interest at the tumor site and ADC at a site contralateral to the tumor site were calculated on DWI sequences. Cox regression analysis was performed to identify which characteristics could predict csPCa in PI-RADS 3 lesions and which characteristics could influence the behavior of a clinician whether or not to biopsy a PI-RADS 3 lesion. RESULTS: csPCa could be detected in 31 out of 131 patients with PI-RADS 3 lesions (22.9%). A lower median prostate volume (p = 0.015) and a lower ratio of ADC of the tumor on ADC of the contralateral prostate (ADCT/ADCCLP) (p < 0.001) significantly predisposed for csPCa in multivariate logistic regression. For peripheral zone lesions, a diagnostic model with biopsy of only those PI-RADS 3 lesions with a prostate volume <44 cc and a ratio of ADCT/ADCCLP < 70% showed a sensitivity for detection of csPCa of 59% with a specificity of 88%. (area under the curve 0.780) A suspicious rectal examination (p = 0.011) and the mentioning of prostatitis on the MRI report (p = 0.020) influenced clinicians to biopsy a PI-RADS 3 lesion positively and negatively respectively. For transition zone lesions, previous negative biopsies (p = 0.044) predisposed for csPCa. CONCLUSION: Prostate volume and the ratio of ADC tumor on ADC of the contralateral prostate have the potential to predict csPCa in PI-RADS 3 lesions with a sensitivity of 59% and specificity of 88%. A suspicious rectal examination and the mentioning of prostatitis on the MRI report influenced the decision of clinicians to biopsy a PI-RADS 3 lesion.

Systematic biopsy combined with cognitive fusion targeted biopsy increases the detec-tion rate of clinically significant prostate cancer / 中国肿瘤临床

Objective:To investigate the ability of separate and combined biopsy methods to distinguish clinically significant prostate cancer(csPCa)from clinically insignificant prostate cancer(incsPCa),we assessed diagnostic positive rates for patients undergoing transperineal pro-state systematic biopsy(SB),cognitive fusion targeted biopsy(CF-TB),and combined biopsy(CB)(i.e.SB combined with CF-TB)under intra-venous anesthesia.Methods:We analyzed clinical data from 151 patients with prostate-specific antigen(PSA)≤50 ng/mL undergoing their first prostate biopsy in Cancer Hospital of Huanxing Chaoyang District Beijing and National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital,Chinese Academy of Medical Sciences and Peking Union Medical College from January 2019 to November 2021.The 3.0 Tesla standard prostate multi-parametric magnetic resonance imaging(mpMRI)examinations found 161 lesions with prostate ima-ging reporting and data system(PI-RADS)scores≥3.With patients under intravenous anesthesia and indwelling catheter,2-4 needle CF-TB biopsies were performed using transperineal ultrasound guidance,followed by 12 needle SB.Patients who underwent SB,CF-TB,and CB were each analyzed by stratification for their respective csPCa and incsPCa detection rates,age,PSA,CF-TB needle count,PI-RADS score,and digital rectal examination results.Results:The median PSA value for all patients was 11.50(0.52-49.37 ng/mL).In total,161 lesions with PI-RADS score≥3 points were found.All 151 patients received 12 needles of SB,while 47,52,and 52 patients received 2,3,and 4 needles of CF-TB,respectively.The respective positivity rates of SB,CF-TB and CB in diagnosing csPCa were 54.3%(82/151),53.0%(80/151)and 58.9%(89/151).Statistical results indicate that the difference in positivity rate between CB and SB is significant(P=0.016)as is the difference between CB and CF-TB positivity rates(P=0.004).The respective positivity rates of SB,CF-TB,and CB in diagnosing incsPCa were 7.9%(12/151)、9.3%(14/151),and 11.3%(17/151).The positivity rate of CB was not significantly different than that of SB or CF-TB(all P>0.05).Stratification plane analysis with age,PSA value,number of CF-TB needles,PI-RADS score,and digital rectal examination results showed that the 2-needle CF-TB scheme was inferior to CB in diagnosing csPCa(P=0.031).There was no significant difference in the csPCa positivity rates of 3-needle and 4-needle CF-TB relative to CB.Conclusions:CB achieves a higher csPCa diagnosis rate without increasing de-tection of incsPCa under transperineal ultrasound guidance.CF-TB with 3-needles per lesion was highly effective in diagnosing csPCa.