Combination of prostate volume and apparent diffusion coefficient can stratify patients with a PI-RADS score of 3 to reduce unnecessary prostate biopsies.

BACKGROUND: Nowadays, there are many patients who undergo unnecessary prostate biopsies after receiving a prostate imaging reporting and data system (PI-RADS) score of 3. Our purpose is to identify cutoff values of the prostate volume (PV) and minimum apparent diffusion coefficient (ADCmin) to stratify those patients to reduce unnecessary prostate biopsies. METHODS: Data from 224 qualified patients who received prostate biopsies from January 2019 to June 2023 were collected. The Mann-Whitney U test was used to compare non-normal distributed continuous variables, which were recorded as median (interquartile ranges). The correlation coefficients were calculated using Spearman's rank correlation analysis. Categorical variables are recorded by numbers (percentages) and compared by χ2 test. Both univariate and multivariate logistic regression analysis were used to determine the independent predictors. The receiver-operating characteristic curve and the area under the curve (AUC) were used to evaluate the diagnostic performance of clinical variables. RESULTS: Out of a total of 224 patients, 36 patients (16.07%) were diagnosed with clinically significant prostate cancer (csPCa), whereas 72 patients (32.14%) were diagnosed with any grade prostate cancer. The result of multivariate analysis demonstrated that the PV (p < 0.001, odds ratio [OR]: 0.952, 95% confidence interval [95% CI]: 0.927-0.978) and ADCmin (p < 0.01, OR: 0.993, 95% CI: 0.989-0.998) were the independent factors for predicting csPCa. The AUC values of the PV and ADCmin were 0.779 (95% CI: 0.718-0.831) and 0.799 (95% CI: 0.740-0.849), respectively, for diagnosing csPCa. After stratifying patients by PV and ADCmin, 24 patients (47.06%) with "PV < 55 mL and ADCmin < 685 µm2/s" were diagnosed with csPCa. However, only one patient (1.25%) with PV ≥ 55 mL and ADCmin ≥ 685 µm2/s were diagnosed with csPCa. CONCLUSIONS: In this study, we found the combination of PV and ADCmin can stratify patients with a PI-RADS score of 3 to reduce unnecessary prostate biopsies. These patients with "PV ≥ 55 mL and ADCmin ≥ 685 µm2/s" may safely avoid prostate biopsies.

[Predictive effect of the dual-parametric MRI modified maximum diameter of the lesions with PI-RADS 4 and 5 on the clinically significant prostate cancer].

OBJECTIVE: To assess the rationality of the maximum lesion diameter of 15 mm in prostate imaging reporting and data system (PI-RADS) as a criterion for upgrading a lesion from category 4 to 5 and improve it to enhance the prediction of clinically significant prostate cancer (csPCa). METHODS: In this study, the patients who underwent prostate magnetic resonance imaging (MRI) and prostate biopsy at Peking University First Hospital from 2019 to 2022 as a development cohort, and the patients in 2023 as a validation cohort were reviewed. The localization and maximum diameter of the lesion were fully evaluated. The area under the curve (AUC) and the cut-off value of the maximum diameter of the lesion to predict the detection of csPCa were calculated from the receiver operating characteristics (ROC) curve. Confounding factors were reduced by propensity score matching (PSM). Diagnostic efficacy was compared in the validation cohort. RESULTS: Of the 589 patients in the development cohort, 358 (60.8%) lesions were located in the peripheral zone and 231 (39.2%) were located in the transition zone, and 496 (84.2%) patients detected csPCa. The median diameter of the lesions in the peripheral zone was smaller than that in the transition zone (14 mm vs. 19 mm, P < 0.001). In the ROC analysis of the maximal diameter on the csPCa prediction, there was no statistically significant difference between the peri-pheral zone (AUC=0.709) and the transition zone (AUC=0.673, P=0.585), and the cut-off values were calculated to be 11.5 mm for the peripheral zone and 16.5 mm for the migrating zone. By calcula-ting the Youden index for the cut-off values in the validation cohort, we found that the categorisation by lesion location led to better predictive results. Finally, the net reclassification index (NRI) was 0.170. CONCLUSION: 15 mm as a criterion for upgrading the PI-RADS score from 4 to 5 is reasonable but too general. The cut-off value for peripheral zone lesions is smaller than that in transitional zone. In the future consideration could be given to setting separate cut-off values for lesions in different locations.

[Clinical significance of prostatic exosomal protein and PSA in detecting prostate cancer with the PSA gray zone and PI-RADS-3 lesions].

OBJECTIVE: To explore the clinical value of prostatic exosomal protein (PSEP) and PSA in the diagnosis of PCa with PSA in the gray zone (4－10 µg/L) and Prostate Imaging Reporting and Data System category 3 (PI-RADS-3) lesions. METHODS: From 2019 to 2022, 211 patients with the PSA gray zone and PI-RADS-3 lesions underwent prostate multi-parameter MRI, prostate needle biopsy or transurethral resection/enucleation of the prostate. We collected the baseline urine samples from the patients, examined the content of PSEP in the urine by ELISA and evaluated the performance of PSEP and PSA in the diagnosis of PCa. RESULTS: Among the total number of patients, 57 were confirmed with PCa (the positive group) and the other 154 with benign prostate conditions (the negative group) by biopsy pathology. The free PSA level (fPSA), free to total PSA ratio (f/tPSA) and PSEP content were dramatically lower in the positive than in the negative group (all P< 0.01). Uni- and multivariate analyses showed f/tPSA and PSEP to be independent factors for predicting PCa with the PSA gray zone and PI-RADS-3 lesions, with the AUC values of 0.70 and 0.78, best cutoff values of 0.18 and 1.45 µg/L, sensitivity of 84.21% and 70.18%, and specificity of 58.44% and 77.27%, respectively (P< 0.01). The multivariate model with combined use of f/tPSA and PSEP (AUC: 0.82, best cutoff value: 0.31, sensitivity: 82.46%, specificity: 75.32%) outperformed either f/tPSA or PSEP alone in the diagnosis of PCa with the PSA gray zone and PI-RADS-3 lesions (P< 0.01, P = 0.04). CONCLUSION: For patients with the PSA gray zone and PI-RADS-3 lesions, f/tPSA and PSEP are significant predictors of PCa. The multivariate model of PSEP combined with f/tPSA can replace f/tPSA in the detection of PCa to improve diagnostic performance and avoid unnecessary prostate biopsy.

Performance of Artificial Intelligence-Aided Diagnosis System for Clinically Significant Prostate Cancer with MRI: A Diagnostic Comparison Study.

BACKGROUND: The high level of expertise required for accurate interpretation of prostate MRI. PURPOSE: To develop and test an artificial intelligence (AI) system for diagnosis of clinically significant prostate cancer (CsPC) with MRI. STUDY TYPE: Retrospective. SUBJECTS: One thousand two hundred thirty patients from derivation cohort between Jan 2012 and Oct 2019, and 169 patients from a publicly available data (U-Net: 423 for training/validation and 49 for test and TrumpeNet: 820 for training/validation and 579 for test). FIELD STRENGTH/SEQUENCE: 3.0T/scanners, T2 -weighted imaging (T2 WI), diffusion-weighted imaging, and apparent diffusion coefficient map. ASSESSMENT: Close-loop AI system was trained with an Unet for prostate segmentation and a TrumpetNet for CsPC detection. Performance of AI was tested in 410 internal and 169 external sets against 24 radiologists categorizing into junior, general and subspecialist group. Gleason score >6 was identified as CsPC at pathology. STATISTICAL TESTS: Area under the receiver operating characteristic curve (AUC-ROC); Delong test; Meta-regression I2 analysis. RESULTS: In average, for internal test, AI had lower AUC-ROC than subspecialists (0.85 vs. 0.92, P < 0.05), and was comparable to junior (0.84, P = 0.76) and general group (0.86, P = 0.35). For external test, both AI (0.86) and subspecialist (0.86) had higher AUC than junior (0.80, P < 0.05) and general reader (0.83, P < 0.05). In individual, it revealed moderate diagnostic heterogeneity in 24 readers (Mantel-Haenszel I2  = 56.8%, P < 0.01), and AI outperformed 54.2% (13/24) of readers in summary ROC analysis. In multivariate test, Gleason score, zonal location, PI-RADS score and lesion size significantly impacted the accuracy of AI; while effect of data source, MR device and parameter settings on AI performance is insignificant (P > 0.05). DATA CONCLUSION: Our AI system can match and to some case exceed clinicians for the diagnosis of CsPC with prostate MRI. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

The role of the size and number of index lesion in the diagnosis of clinically significant prostate cancer in patients with PI-RADS 4 lesions who underwent in-bore MRI-guided prostate biopsy.

PURPOSE: To evaluate the contribution of the size and number of the sampled lesions to the diagnosis of clinically significant prostate cancer (CSPC) in patients who had PI-RADS 4 lesions. METHODS: In this retrospective study, a total of 159 patients who had PI-RADS 4 lesions and underwent In-bore MRI-Guided prostate biopsy were included. Patients with a lesion classified as Grade Group 2 and above were considered to have CSPC. Univariate and multivariate regression analyses were used to evaluate the factors affecting the diagnosis of prostate cancer (PCa) and CSPC. RESULTS: A great majority (86.8%) of the patients were biopsy-naïve. About three-fourths (71.7%) had PCa, and half (54.1%) had CSPC. When the patients were divided into three groups according to the index lesion size (< 5 mm, 5-10 mm, and > 10 mm), the prevalence of PCa was 64.3, 67.5, and 82.4% and the prevalence of CSPC was 42.9, 51.2, and 64.7%, respectively. In multivariate analysis, age, index lesion size, prostate volume (< 50 ml) and being biopsy-naïve were found significant for PCa, while age and prostate volume (< 50 ml) were significant for CSPC. CONCLUSION: The number of lesions was found to be insignificant in predicting PCa and CSPC. While the size of PI-RADS 4 lesions was significant in predicting PCa, it had no significance in detecting CSPC.

The function of Prostate Health Index in detecting clinically significant prostate cancer in the PI-RADS 3 population: a multicenter prospective study.

PURPOSE: The purpose of this study is to identify patients in the prostate imaging reporting and data system (PI-RADS) 3 population who need biopsy by using prostate health index (PHI) and other clinical parameters in order to avoid unnecessary biopsies. METHODS: A total of 302 patients from four hospital were enrolled, and 92 patients with PI-RADS 3 were included finally. All patients were biopsy-naïve and had suspicion of prostate cancer (PCa) with PSA level in 4-20 ng/ml and a normal digital rectal exam. Univariable and stepwise forward multivariable logistic regression analyses were used to evaluated the risk factors. The sensitivity, specificity, and positive and negative predictive values of different cut-off value of PHI were calculated for the diagnosis of clinically significant prostate cancer (CSPCa). RESULTS: The overall patient's mean age was 65.65 ± 9.55 years, median PSA was 7.68 (5.28-12.07) ng/ml and median PHI was 43.80 (33.09-64.69). PCa was identified in 32.61% (30/92) of PI-RADS 3 and CSPCa was identified in 28.26% (26/92) of PI-RADS 3. The risk factors for detecting PCa and CSPCa in multivariable regression analysis were age and PHI. When the biopsy was restricted to those PHI ≥ 43.5, 42.39% unnecessary biopsied could avoid. The sensitivity, specificity, positive predictive value and negative predictive value for the detection of CSPCa in the PHI ≥ 43.5 were 92.31%, 63.64%, 50% and 95.45% respectively. CONCLUSION: The inclusion of PHI in the diagnosis of the PI-RADS 3 population may avoid many unnecessary biopsies. The multivariable models could increase the detection of cancer.

Management Strategy for Prostate Imaging Reporting and Data System Category 3 Lesions.

PURPOSE OF REVIEW: Prostate Imaging Reporting and Data System (PI-RADS) category 3 lesions present a clinical dilemma due to their uncertain nature, which complicates the development of a definitive management strategy. These lesions have an incidence rate of approximately 22-32%, with clinically significant prostate cancer (csPCa) accounting for about 10-30%. Therefore, a thorough evaluation is warranted. RECENT FINDINGS: This review highlights the need for radiology peer review, including the confirmation of dynamic contrast-enhanced (DCE) compliance, as the initial step. Additional MRI models such as VERDICT or Tofts need to be verified. Current evidence shows that imaging and clinical indicators can be used for risk stratification of PI-RADS 3 lesions. For low-risk lesions, a safety net monitoring approach involving annual repeat MRI can be employed. In contrast, lesions deemed potentially risky based on prostate-specific antigen density (PSAD), 68 Ga-PSMA PET/CT, MPS, Proclarix, or AI/machine learning models should undergo biopsy. It is recommended to establish a multidisciplinary team that takes into account factors such as age, PSAD, prostate, and lesion size, as well as previous biopsy pathological findings. Combining expert opinions, clinical-imaging indicators, and emerging methods will contribute to the development of management strategies for PI-RADS 3 lesions.

Development and validation of a nomogram based on biparametric MRI PI-RADS v2.1 and clinical parameters to avoid unnecessary prostate biopsies.

BACKGROUND: Biparametric MRI (bpMRI) is a faster, contrast-free, and less expensive MRI protocol that facilitates the detection of prostate cancer. The aim of this study is to determine whether a biparametric MRI PI-RADS v2.1 score-based model could reduce unnecessary biopsies in patients with suspected prostate cancer (PCa). METHODS: The patients who underwent MRI-guided biopsies and systematic biopsies between January 2020 and January 2022 were retrospectively analyzed. The development cohort used to derive the prediction model consisted of 275 patients. Two validation cohorts included 201 patients and 181 patients from 2 independent institutions. Predictive models based on the bpMRI PI-RADS v2.1 score (bpMRI score) and clinical parameters were used to detect clinically significant prostate cancer (csPCa) and compared by analyzing the area under the curve (AUC) and decision curves. Spearman correlation analysis was utilized to determine the relationship between International Society of Urological Pathology (ISUP) grade and clinical parameters/bpMRI score. RESULTS: Logistic regression models were constructed using data from the development cohort to generate nomograms. By applying the models to the all cohorts, the AUC for csPCa was significantly higher for the bpMRI PI-RADS v2.1 score-based model than for the clinical model in both cohorts (p < 0.001). Considering the test trade-offs, urologists would agree to perform 10 fewer bpMRIs to avoid one unnecessary biopsy, with a risk threshold of 10-20% in practice. Correlation analysis showed a strong correlation between the bpMRI score and ISUP grade. CONCLUSION: A predictive model based on the bpMRI score and clinical parameters significantly improved csPCa risk stratification, and the bpMRI score can be used to determine the aggressiveness of PCa prior to biopsy.

The Value of Prostate-Specific Antigen Density in Combination with Lesion Diameter for the Accuracy of Prostate Cancer Prediction in Prostate Imaging-Reporting and Data System 3 Prostate Lesions.

INTRODUCTION: The aim of the study was to investigate the value of prostate-specific antigen density (PSAD) and lesion diameter (LD) combination in prostate cancer (PCa) detection. METHODS: 181 patients who were detected to have prostate imaging-reporting and data system (PI-RADS) 3 lesions in mpMRI and underwent prostate biopsies were included in the study. Demographic, clinical, and pathological data of all patients were evaluated. The patients were divided into four groups according to PSAD and LD status (PSAD <0.15 ng/mL/cc + LD <1 cm, PSAD <0.15 ng/mL/cc + LD ≥1 cm, PSAD ≥0.15 ng/mL/cc + LD <1 cm, and PSAD ≥0.15 ng/mL/cc + LD ≥1 cm). Diagnostic ability for PCa and clinical significant PCa (csPCa) was evaluated by PSAD and LD. RESULTS: PSAD ≥0.15 ng/mL/cc (OR = 6; 95% Cl = 2.847-12.647; p < 0.001), LD ≥1 cm (OR = 7.341; 95% confidence interval [CI] = 2.91-18.52; p < 0.001), and combination of PSAD ≥0.15 ng/mL/cc and LD ≥1 cm (OR = 10.023; 95% CI = 4.32-23.252; p < 0.001) were associated with PCa detection rates. The most sensitivity, specificity, negative, and positive predictive values were found in PSAD ≥0.15 ng/mL/cc + LD ≥1 cm group for both PCa and csPCa detection (48.8%, 92%, 85.2%, and 65.6% for any PCa detection; 66.7%, 85.2%, 97.3%, and 24.2% for csPCa detection, respectively). CONCLUSION: The presence of PSAD ≥0.15 ng/mL/cc or LD ≥1 cm in mpMRI of patients with PI-RADS 3 lesions is associated significantly with the finding of PCa and particularly with the detection of csPCa.

The Value of the Prostate Health Index and Derived Indexes in the Diagnosis of Prostate Cancer with PI-RADS-3 Lesions.

INTRODUCTION: The purpose of this article was to evaluate the diagnostic value of prostate health index (PHI) and its derivatives in prostate cancer (PCa) with prostate imaging reporting and data system (PI-RADS)-3 lesions. METHODS: Patients with benign prostatic hyperplasia (BPH) (n = 155) were included in the BPH group, while all patients with PCa (n = 49) were enrolled in the PCa group. Between the groups, the serum concentrations of total prostate-specific antigen (TPSA), percent-free prostate-specific antigen (%fPSA), prostate health index (PHI), prostate health index density (PHID), and prostate-specific antigen density (PSAD) were compared. RESULTS: On average, 49 (24%) of 204 men had PCa on biopsy, with 81.63% of those cases being clinically serious. Age, prostate volume, TPSA, and PSAD did not significantly differ between the PCa group and the BPH group. In contrast, [-2]pro prostate-specific antigen (p2PSA) (17.10 ± 4.77 vs. 13.93 ± 3.22, p < 0.001), PHI (33.88 ± 8.81 vs. 25.83 ± 5.63, p < 0.001), and PHID (0.52 ± 0.15 vs. 0.38 ± 0.11, p < 0.001) showed a statistically significant difference between the two groups. Compared to conventional PSA, PHI (AUC = 0.786, 95% CI: 0.705-0.867) and PHID (AUC = 0.763, 95% CI: 0.684-0.843) were considerably better predictors of all PCa. The TPSA, %fPSA, p2PSA, PHI, PHID, and PSAD areas under the receiver operating characteristic for clinically significant PCa (csPCa) were 0.587, 0.650, 0.696, 0.823, 0.796, and 0.614, respectively. Out of all the various parameters, PHI and PHID performed very well in this cohort's biopsy outcome prediction. CONCLUSION: PHI offers the best diagnostic value for detecting PCa in cases of PI-RADS-3 lesions. Additionally, PHID raised the possibility of csPCa PI-RADS-3 lesions. However, more investigation is required to confirm our results by using multicenter collaboration.

[Analysis of the relationship between PI-RADS scores and the pathological results of targeted biopsy based on MRI].

Objective: To analyze the relationship between Prostate Imaging Reporting and Data System (PI-RADS) scores and the pathological results of transperineal magnetic resonance-ultrasound fusion guided biopsy. Methods: The clinical data, magnetic resonance imaging (MRI) results and prostate puncture biopsies of 517 patients who were assigned to PI-RADS score of 4 or 5 and underwent transperineal magnetic resonance-ultrasound fusion guided biopsy at The First Affiliated Hospital of Nanjing Medical University from June 2019 to March 2022 were retrospectively analyzed. Patients were divided into the PI-RADS 4 and PI-RADS 5 groups according to their PI-RADS scores and were stratified by their prostate specific antigen (PSA) values (PSA<10 ng/ml vs. PSA 10-20 ng/ml). The pathological negative rates from the biopsy, the distribution of the grade groups according to the grading system by World Health Organization/International Society of Urological Pathology (WHO/ISUP), the detection rates of prostate cancer (PCa) and clinically significant prostate cancer (CsPCa)between the groups were compared. Results: 369 patients with a PI-RADS score of 4 and 148 patients with a PI-RADS score of 5 were included in our research. The overall detection rates of PCa and CsPCa were 77.8% (402/517) and 66.7% (345/517), respectively. In the PI-RADS 4 group, patients with prostate negative biopsies or in WHO/ISUP 1, 2, 3, 4, or 5 grade groups accounted for 28.2%, 12.7%, 20.1%, 17.1%, 18.4% and 3.5%, respectively, whereas in the PI-RADS 5 group the rates were 7.4%, 6.8%, 22.3%, 22.3%, 26.4%, and 14.9%, respectively. The difference was statistically significant (P<0.001). The detection rates of PCa and CsPCa in the PI-RADS 4 group [71.8% (265/369) vs. 59.1% (218/369), P<0.001] were lower than those of the PI-RADS 5 group [92.6% (137/148) vs. 85.8% (127/148), P<0.001]. In the PI-RADS 4 group, the proportion of patients classified into WHO/ISUP 4-5 grade groups was lower than that of patients in the PI-RADS 5 group [22.0% (81/369) vs 41.2% (61/148) (P<0.001)]. The detection rates of PCa and CsPCa in the PSA<10 ng/ml stratification were less than that in the PSA 10-20 ng/ml stratification[74.1% (281/379) vs. 87.7% (121/138), P=0.001], and [60.9% (231/379) vs. 82.6% (114/138), P<0.001]. For patients with PSA<10 ng/ml, the detection rates of PCa and CsPCa in the PI-RADS 4 group were less than those in the PI-RADS5 group [70.9% (217/306) vs. 87.7% (64/73), P=0.003], and [56.2% (172/306) vs. 80.8% (59/73), P<0.001]. For those with a PSA value of 10-20 ng/ml, the detection rates of PCa and CsPCa in the PI-RADS 4 group were less than those in the PI-RADS 5 group [76.2% (48/63) vs. 97.3% (73/75), P<0.001], and [73.0% (46/63) vs. 90.7% (68/75), P=0.006]. There were statistically significant differences in the proportions of patients with prostate negative biopsy and those falling into WHO/ISUP grade groups 1, 2, 3, 4, or 5 (P<0.001) between the PI-RADS 4 group and the PI-RADS 5 group in both stratifications. Conclusions: In this study, the detection rates of CsPCa and PCa in the PI-RADS 4 group were less than those in the PI-RADS 5 group. With the increase of PI-RADS scores, the detection rate of high-grade PCa increased. The same results held for patients with PSA<10 ng/ml or with PSA 10-20 ng/ml.

[Diagnostic efficacy of prostate cancer using targeted biopsy with 6-core systematic biopsy for patients with PI-RADS 5].

OBJECTIVE: To investigate the diagnostic efficacy of targeted biopsy (TBx), systematic biopsy (SBx), TBx+6-core SBx in prostate cancer (PCa) / clinically significant prostate cancer (cs-PCa) for patients with prostate imaging reporting and data system (PI-RADS) score of 5, and thereby to explore an optimal sampling scheme. METHODS: The data of 585 patients who underwent multiparametric magnetic resonance imaging (mpMRI) with at least one lesion of PI-RADS score 5 at Peking University First Hospital from January 2019 to June 2022 were retrospectively analyzed. All patients underwent mpMRI / transrectal ultrasound (TRUS) cognitive guided biopsy (TBx+SBx). With the pathological results of combined biopsy as the gold standard, we compared the diagnostic efficacy of TBx only, SBx only, and TBx+6-core SBx for PCa/csPCa. The patients were grouped according to mpMRI T-stage (cT2, cT3, cT4) and the detection rates of different biopsy schemes for PCa/csPCa were compared using Cochran's Q and McNemar tests. RESULTS: Among 585 patients with a PI-RADS score of 5, 560 (95.7%) were positive and 25(4.3%) were negative via TBx+SBx. After stratified according to mpMRI T-stage, 233 patients (39.8%) were found in cT2 stage, 214 patients (36.6%) in cT3 stage, and 138 patients (23.6%) in cT4 stage. There was no statistically significant difference in the detection rate of PCa/csPCa between TBx+6-core SBx and TBx+SBx (all P>0.999). Also, there was no statistically significant difference in the detection rate of PCa/csPCa between TBx and TBx+SBx in the cT2, cT3, and cT4 subgroups (PCa: P=0.203, P=0.250, P>0.999; csPCa: P=0.700, P=0.250, P>0.999). The missed diagnosis rate of SBx for PCa and csPCa was 2.1% (12/560) and 1.8% (10/549), and that of TBx for PCa and csPCa was 1.8% (10/560) and 1.4% (8/549), respectively. However, the detection rate of TBx+6-core SBx for PCa and csPCa was 100%. Compared with TBx+SBx, TBx and TBx+6-core SBx had a fewer number of cores and a higher detection rate per core (P < 0.001). CONCLUSION: For patients with a PI-RADS score of 5, TBx and TBx+6-core SBx showed the same PCa/csPCa detection rates and a high detection rates per core as that of TBx+SBx, which can be considered as an optimal scheme for prostate biopsy.

[Application of dynamic contrast enhanced status in multiparametric magnetic resonance imaging for prostatic cancer with PI-RADS 4 lesion].

OBJECTIVE: To evaluate the diagnostic value of dynamic contrast enhanced (DCE) of multiparametric magnetic resonance imaging (mpMRI) for prostate imaging reporting and data system (PI-RADS) 4 lesion in prostate peripheral zone. METHODS: The clinical data of patients with PI-RADS 4 lesion in prostate peripheral zone who underwent prostate biopsy from January 2018 to September 2021 in Peking University First Hospital were retrospectively included. According to DCE status, the patients were divided into the conventional group (4 points for diffusion-weighted imaging) and the comprehensive group (3 points for diffusion-weighted imaging + 1 point for DCE positive). Pearson's chi-square test or Fisher's exact test for comparison was conducted between prostate cancer and non-cancer patients. Univariate and multivariate Logistic regression were performed to analyze the correlation of positive biopsy with age, total prostate specific antigen (PSA), free PSA/total PSA (f/tPSA), prostate volume (PV), PSA density (PSAD) and DCE status. RESULTS: Among the 267 prostate biopsy patients, 217 cases were diagnosed as prostatic cancer (81.27%) and 50 cases were non-cancer (18.73%). Statistical analysis between the prostatic cancer group and the non-cancer group showed that there were significant differences in age, tPSA, PV and PSAD (all P < 0.05), but no significant differences in f/tPSA between the two groups. About different PI-RADS 4 lesion groups, the conventional group and the comprehensive group showed significant difference in biopsy results (P=0.001), and the conventional group had a higher positive rate. The PV of comprehensive group was larger than that of the conventional group. Among the prostate cancer patients diagnosed by biopsy, statistical analysis between the conventional group and comprehensive group showed that there were not significant differences in International Society of Urological Pathology (ISUP) grade and distinguishing clinically significant prostate cancer (all P > 0.05). Logistic univariate analysis showed that the diagnosis of prostate cancer was related to age, tPSA, f/tPSA, PV and DCE group status (all P < 0.05). Multivariate analysis showed that age, tPSA, PV and DCE group status (all P < 0.05) were independent risk factors for the diagnosis of prostatic cancer. CONCLUSION: tPSA, f/tPSA, PV and PSAD are the indicators to improve the diagnosis of prostatic cancer with PI-RADS 4 lesion in peripheral zone lesions. DCE status is worth considering, so that we can select patients for biopsy more accurately, reduce the rate of missed diagnosis of prostate cancer as well as avoid unnecessary prostate puncture.

A Critical Analysis of the Magnetic Resonance Imaging Lesion Diameter Threshold for Adverse Pathology Features.

To investigate the relationship between lesion size determined using multiparametric magnetic resonance imaging (mpMRI) and histopathological findings of specimens obtained after mpMRI fusion biopsy and radical prostatectomy (RP). We retrospectively analysed 290 patients with PCa who underwent an MRI fusion biopsy. We measured the diameter of suspicious tumour lesions on diffusion-weighted mpMRI and stratified the cohort into two groups. Group A included patients with a suspicious tumour lesion 10 mm and Group B included those with a suspicious tumour lesion > 10 mm. In Group B, the PI-RADS score determined in mpMRI was higher than Group A, and there was a statistically significant difference between the two groups in terms of clinical T-stage. The PCa detection rate and the number of positive cores were statistically significantly higher in Group B than in Group A. In addition, there was a statistically significant difference between the two groups in relation to the biopsy, the International Society of Urological Pathology (ISUP) grade values, and the presence of clinically significant PCa. In Group B, pathological T-stage and extraprostatic extension (EPE) and surgical margin (SM) positivity were found to be higher among the patients who underwent RP. In the multivariate analysis, the mpMRI lesion size being > 10 mm was found to be an independent predictive factor for SM and EPE positivity. The clinical results of this study support the modification of the lesion size threshold as 10 mm for use in the differentiation of PI-RADS scores 4 and 5.

Clinical implementation of pre-biopsy magnetic resonance imaging pathways for the diagnosis of prostate cancer.

OBJECTIVE: To assess the outcomes of pre-biopsy magnetic resonance imaging (MRI) pathways, as a tool in biopsy-naïve men with suspicion of prostate cancer, in routine clinical practice. Secondary outcomes included a comparison of transrectal MRI-directed biopsy (TR-MRDB) and transperineal (TP)-MRDB in men with suspicious MRI. PATIENTS AND METHODS: We retrospectively assessed a two-centre cohort of consecutive biopsy-naïve men with suspicion of prostate cancer who underwent a Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) compliant pre-biopsy MRI in a single, high-volume centre between 2015 and 2019 (Centre 1). Men with suspicious MRI scans underwent TR-MRDB in Centre 1 and TP-MRDB with additional random biopsies (RB) in Centre 2. The MRI and histopathology were assessed in the same institution (Centre 1). Outcomes included: (i) overall detection rates of Grade Group (GG) 1, GG ≥2, and GG ≥3 cancer in men with suspicious MRI; (ii) Biopsy-avoidance due to non-suspicious MRI; and (iii) Cancer detection rates and biopsy-related complications between TR- and TP-MRDB. To reduce confounding bias for MRDB comparisons, inverse probability weighting (IPW) was performed for age, digital rectal examination, prostate-specific antigen (PSA), prostate volume, PSA density, and PI-RADS category. RESULTS: Of the 2597 men included, the overall GG 1, GG ≥2, and GG ≥3 prevalence was 8% (210/2597), 27% (697/2597), and 15% (396/2597), respectively. Biopsy was avoided in 57% (1488/2597) of men. After IPW, the GG 1, GG ≥2 and GG ≥3 detection rates after TR- and TP-MRDB were comparable at 24%, 57%, and 32%; and 18%, 64%, and 38%, respectively; with mean differences of -5.7% (95% confidence interval [CI] -13% to 1.4%), 6.1% (95% CI -2.1% to 14%), and 5.7% (95% CI -1.7% to 13%). Complications were similar in TR-MRDB (0.50%) and TP-MRDB with RB (0.62%; mean difference 0.11%, 95% CI -0.87% to 1.1%). CONCLUSION: This high-volume, two-centre study shows pre-biopsy MRI as a decision tool is implementable in daily clinical practice. Compared to recent trials, a substantially higher biopsy avoidance rate was achieved without compromising GG ≥2/GG ≥3 detection and coinciding with lower over detection rates of GG 1 cancer. Prostate cancer detection and complication rates were comparable for TR- and TP-MRDB.

Head-to-head comparison of biparametric versus multiparametric MRI of the prostate before robot-assisted transperineal fusion prostate biopsy.

PURPOSE: Prostate biparametric magnetic resonance imaging (bpMRI) including T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) might be an alternative to multiparametric MRI (mpMRI, including dynamic contrast imaging, DCE) to detect and guide targeted biopsy in patients with suspected prostate cancer (PCa). However, there is no upgrading peripheral zone PI-RADS 3 to PI-RADS 4 without DCE in bpMRI. The aim of this study was to evaluate bpMRI against mpMRI in biopsy-naïve men with elevated prostate-specific antigen (PSA) scheduled for robot-assisted-transperineal fusion-prostate biopsy (RA-TB). METHODS: Retrospective single-center-study of 563 biopsy-naïve men (from 01/2015 to 09/2018, mean PSA 9.7 ± 6.5 ng/mL) with PI-RADSv2.1 conform mpMRI at 3 T before RA-TB. Clinically significant prostate cancer (csPCa) was defined as ISUP grade ≥ 2 in any core. Two experienced readers independently evaluated images according to PI-RADSv2.1 criteria (separate readings for bpMRI and mpMRI sequences, 6-month interval). Reference standard was histology from RA-TB. RESULTS: PI-RADS 2 was scored in 5.1% of cases (3.4% cancer/3.4% csPCa), PI-RADS 3 in 16.9% (32.6%/3.2%), PI-RADS 4 in 57.6% (66.1%/58.3%) and PI-RADS 5 in 20.4% of cases (79.1%/74.8%). For mpMRI/bpMRI test comparison, sensitivity was 99.0%/97.1% (p < 0.001), specificity 47.5%/61.2% (p < 0.001), PPV 69.5%/75.1% (p < 0.001) and NPV 97.6%/94.6% (n.s.). csPCa was considered gold standard. 35 cases without cancer were upgraded to PI-RADS 4 (mpMRI) and six PI-RADS 3 cases with csPCa were not upgraded (bpMRI). CONCLUSION: In patients planned for RA-TB with elevated PSA and clinical suspicion for PCa, specificity was higher in bpMRI vs. mpMRI, which could solve constrains regarding time and contrast agent.

Usefulness of grayscale values of hypoechoic lesions matched with target lesions observed on magnetic resonance imaging for the prediction of clinically significant prostate cancer.

BACKGROUND: To analyze grayscale values for hypoechoic lesions matched with target lesions evaluated using prebiopsy magnetic resonance imaging (MRI) according to the Prostate Imaging-Reporting and Data System (PI-RADS). METHODS: We collected data on 420 target lesions in patients who underwent MRI/transrectal ultrasound fusion-targeted biopsies between January 2017 and September 2020. Images of hypoechoic lesions that matched the target lesions on MRI were stored in a picture archiving and communication system, and their grayscale values were estimated using the red/green/blue scoring method through an embedded function. We analyzed imaging data using grayscale values. RESULTS: Of the 420 lesions, 261 (62.1%) were prostate cancer lesions. There was no difference in the median grayscale values between benign and prostate cancer lesions. However, grayscale ranges (41.8-98.5 and 42.6-91.8) were significant predictors of prostate cancer and clinically significant prostate cancer (csPC) in multivariable logistic regression analyses. Area under the curve for detecting csPC using grayscale values along with conventional variables (age, prostate-specific antigen levels, prostate volume, previous prostate biopsy results, and PI-RADS scores) was 0.839, which was significantly higher than that for detecting csPC using only conventional variables (0.828; P = 0.036). Subgroup analysis revealed a significant difference for PI-RADS 3 lesions between grayscale values for benign and cancerous lesions (74.5 vs. 58.8, P = 0.008). Grayscale values were the only significant predictive factor (odds ratio = 4.46, P = 0.005) for csPC. CONCLUSIONS: Distribution of grayscale values according to PI-RAD 3 scores was potentially useful, and the grayscale range (42.6-91.8) was a potential predictor for csPC diagnosis.

Interobserver Agreement and Accuracy in Interpreting mpMRI of the Prostate: a Systematic Review.

PURPOSE OF REVIEW: To present the latest evidence related to interobserver agreement and accuracy; evaluate the strengths, weaknesses, and implications of use; and outline opportunities for improvement and future development of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) for detection of prostate cancer (PCa) on multiparametric magnetic resonance imaging (mpMRI). RECENT FINDINGS: Our review of currently available evidence suggests that recent improvements to the PI-RADS system with PI-RADS v2.1 slightly improved interobserver agreement, with generally high sensitivity and moderate specificity for the detection of clinically significant PCa. Recent evidence additionally demonstrates substantial improvement in diagnostic specificity with PI-RADS v2.1 compared with PI-RADS v2. However, results of studies examining the comparative performance of v2.1 are limited by small sample sizes and retrospective cohorts, potentially introducing selection bias. Some studies suggest a substantial improvement between v2.1 and v2, while others report no statistically significant difference. Additionally, in PI-RADS v2.1, the interpretation and reporting of certain findings remain subjective, particularly for category 2 lesions, and reader experience continues to vary significantly. These factors further contribute to a remaining degree of interobserver variability and findings of improved performance among more experienced readers. PI-RADS v2.1 appears to show at least minimal improvement in interobserver agreement, diagnostic performance, and both sensitivity and specificity, with greater improvements seen among more experienced readers. However, given the decrescent nature of these improvements and the limited power of all studies examined, the clinical impact of this progress may be marginal. Despite improvements in PI-RADS v2.1, practitioner experience in interpreting mpMRI of the prostate remains the most important factor in prostate cancer detection.

[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.

Characterisation of prostate cancer using texture analysis for diagnostic and prognostic monitoring.

Automated classification of significant prostate cancer (PCa) using MRI plays a potential role in assisting in clinical decision-making. Multiparametric MRI using a machine-aided approach is a better step to improve the overall accuracy of diagnosis of PCa. The objective of this study was to develop and validate a framework for differentiating Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) grades (grade 2 to grade 5) of PCa using texture features and machine learning (ML) methods with diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC). The study cohort included an MRI dataset of 59 patients with clinically proven PCa. Regions of interest (ROIs) for a total of 435 lesions were delineated from the segmented peripheral zones of DWI and ADC. Six texture methods comprising 98 texture features in total (49 each of DWI and ADC) were extracted from lesion ROIs. Random forest (RF) and correlation-based feature selection methods were applied on feature vectors to select the best features for classification. Two ML classifiers, support vector machine (SVM) and K-nearest neighbour, were used and validated by 10-fold cross-validation. The proposed framework achieved high diagnostic performance with a sensitivity of 85.25% ± 3.84%, specificity of 95.71% ± 1.96%, accuracy of 84.90% ± 3.37% and area under the receiver-operating characteristic curve of 0.98 for PI-RADS v2 grades (2 to 5) classification using the RF feature selection method and Gaussian SVM classifier with combined features of DWI + ADC. The proposed computer-assisted framework can distinguish between PCa lesions with different aggressiveness based on PI-RADS v2 standards using texture analysis to improve the efficiency of PCa diagnostic performance.