Impact of prostate MRI image quality on diagnostic performance for clinically significant prostate cancer (csPCa).

OBJECTIVES: With the widespread clinical application of prostate magnetic resonance imaging (MRI), there has been an increasing demand for lesion detection and accurate diagnosis in prostate MR, which relies heavily on satisfactory image quality. Focusing on the primary sequences involved in Prostate Imaging Reporting and Data System (PI-RADS), this study have evaluated common quality issues in clinical practice (such as signal-to-noise ratio (SNR), artifacts, boundaries, and enhancement). The aim of the study was to determine the impact of image quality on clinically significant prostate cancer (csPCa) detection, positive predictive value (PPV) and radiologist's diagnosis in different sequences and prostate zones. METHODS: This retrospective study included 306 patients who underwent prostate MRI with definitive pathological reports from February 2021 to December 2022. All histopathological specimens were evaluated according to the recommendations of the International Society of Urological Pathology (ISUP). An ISUP Grade Group ≥ 2 was considered as csPCa. Three radiologists from different centers respectively performed a binary classification assessment of image quality in the following ten aspects: (1) T2WI in the axial plane: SNR, prostate boundary conditions, the presence of artifacts; (2) T2WI in the sagittal or coronal plane: prostate boundary conditions; (3) DWI: SNR, delineation between the peripheral and transition zone, the presence of artifacts, the matching of DWI and T2WI images; (4) DCE: the evaluation of obturator artery enhancement, the evaluation of dynamic contrast enhancement. Fleiss' Kappa was used to determine the inter-reader agreement. Wilson's 95% confidence interval (95% CI) was used to calculate PPV. Chi-square test was used to calculate statistical significance. A p-value < 0.05 was considered statistically significant. RESULTS: High-quality images had a higher csPCa detection rate (56.5% to 64.3%) in axial T2WI, DWI, and DCE, with significant statistical differences in SNR in axial T2WI (p 0.002), the presence of artifacts in axial T2WI (p 0.044), the presence of artifacts in DWI (p < 0.001), and the matching of DWI and T2WI images (p < 0.001). High-quality images had a higher PPV (72.5% to 78.8%) and showed significant statistical significance in axial T2WI, DWI, and DCE. Additionally, we found that PI-RADS 3 (24.0% to 52.9%) contained more low-quality images compared to PI-RADS 4-5 (20.6% to 39.3%), with significant statistical differences in the prostate boundary conditions in axial T2WI (p 0.048) and the presence of artifacts in DWI (p 0.001). Regarding the relationship between csPCa detection and image quality in different prostate zones, this study found that significant statistical differences were only observed between high- (63.5% to 75.7%) and low-quality (30.0% to 50.0%) images in the peripheral zone (PZ). CONCLUSION: Prostate MRI quality may have an impact on the diagnostic performance. The poorer image quality is associated with lower csPCa detection rates and PPV, which can lead to an increase in radiologist's ambiguous diagnosis (PI-RADS 3), especially for the lesions located at PZ.

A novel model incorporating quantitative contrast-enhanced ultrasound into PI-RADSv2-based nomogram detecting clinically significant prostate cancer.

The diagnostic accuracy of clinically significant prostate cancer (csPCa) of Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) is limited by subjectivity in result interpretation and the false positive results from certain similar anatomic structures. We aimed to establish a new model combining quantitative contrast-enhanced ultrasound, PI-RADSv2, clinical parameters to optimize the PI-RADSv2-based model. The analysis was conducted based on a data set of 151 patients from 2019 to 2022, multiple regression analysis showed that prostate specific antigen density, age, PI-RADSv2, quantitative parameters (rush time, wash-out area under the curve) were independent predictors. Based on these predictors, we established a new predictive model, the AUCs of the model were 0.910 and 0.879 in training and validation cohort, which were higher than those of PI-RADSv2-based model (0.865 and 0.821 in training and validation cohort). Net Reclassification Index analysis indicated that the new predictive model improved the classification of patients. Decision curve analysis showed that in most risk probabilities, the new predictive model improved the clinical utility of PI-RADSv2-based model. Generally, this new predictive model showed that quantitative parameters from contrast enhanced ultrasound could help to improve the diagnostic performance of PI-RADSv2 based model in detecting csPCa.

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.

Deep Learning Based on ResNet-18 for Classification of Prostate Imaging-Reporting and Data System Category 3 Lesions.

RATIONALE AND OBJECTIVES: To explore the classification and prediction efficacy of the deep learning model for benign prostate lesions, non-clinically significant prostate cancer (non-csPCa) and clinically significant prostate cancer (csPCa) in Prostate Imaging-Reporting and Data System (PI-RADS) 3 lesions. MATERIALS AND METHODS: From January 2015 to December 2021, lesions diagnosed with PI-RADS 3 by multi-parametric MRI or bi-parametric MRI were retrospectively included. They were classified as benign prostate lesions, non-csPCa, and csPCa according to the pathological results. T2-weighted images of the lesions were divided into a training set and a test set according to 8:2. ResNet-18 was used for model training. All statistical analyses were performed using Python open-source libraries. The receiver operating characteristic curve (ROC) was used to evaluate the predictive effectiveness of the model. T-SNE was used for image semantic feature visualization. The class activation mapping was used to visualize the area focused by the model. RESULTS: A total of 428 benign prostate lesion images, 158 non-csPCa images and 273 csPCa images were included. The precision in predicting benign prostate disease, non-csPCa and csPCa were 0.882, 0.681 and 0.851, and the area under the ROC were 0.875, 0.89 and 0.929, respectively. Semantic feature analysis showed strong classification separability between csPCa and benign prostate lesions. The class activation map showed that the deep learning model can focus on the area of the prostate or the location of PI-RADS 3 lesions. CONCLUSION: Deep learning model with T2-weighted images based on ResNet-18 can realize accurate classification of PI-RADS 3 lesions.

Creating a novel multiparametric magnetic resonance imaging-based biopsy strategy for reducing unnecessary prostate biopsies: a retrospective cohort study.

Background: The overdiagnosis of prostate cancer (PCa) caused by unnecessary prostate biopsy has become a worldwide problem that urgently requires a solution. We aimed to reduce the unnecessary prostate biopsies and increase the detection rate of clinically significant PCa (csPCa) by creating a novel multiparametric magnetic resonance imaging (mpMRI)-based strategy. Methods: A total of 1,194 eligible patients who underwent transperineal prostate biopsies from January 2018 to December 2022 were included in this retrospective study. Of these patients, 1,080 who received prostate biopsies from January 2018 to July 2022 were regarded as cohort 1 for primary analysis, and 114 patients who received prostate biopsies from August 2022 to December 2022 were collected in cohort 2 for validation. All the mpMRI images were quantitatively evaluated by the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v. 2.1). The diagnostic performances were assessed through the receiver operating characteristic (ROC) curve and area under the curve (AUC) and were compared with the DeLong test. Cancer diagnosis-free survival analysis was performed using the Kaplan-Meier method and log-rank test. The primary endpoint of this study was clinically significant PCa with an International Society of Urological Pathology (ISUP) grade ≥2. Results: In cohort 1, the results of ROC curves demonstrated that the PI-RADS score had a higher diagnostic accuracy (AUC =0.898 for any-grade PCa; AUC =0.917 for csPCa) than did the other clinical variables (P<0.001). Under the novel mpMRI-based biopsy strategy, all patients with PI-RADS 1 can safely avoid prostate biopsy. For patients with PI-RADS 2, prostate biopsy should be considered for patients with prostate-specific antigen density (PSAD) ≥0.3 ng/mL2 and prostate volume <65 mL. As for patients with PI-RADS 3, structured surveillance programs can be a viable option if PSAD <0.3 ng/mL2 and prostate volume ≥65 mL. Finally, patients with a PI-RADS score of 4 and 5 should undergo prostate biopsy due to the high probability of clinically significant PCa. In the validation analysis of cohort 2, 48 patients were placed into a biopsy-spared group with no csPCa cases, while 66 patients were placed in a biopsy-needed group, with an csPCa detection rate of 50.0%. Overall, the novel strategy demonstrated a sensitivity, specificity, positive predictive value, and negative predictive value of 98.9%, 57.5%, 50.5%, and 99.2%, respectively, for diagnosing csPCa. Conclusions: An mpMRI-based biopsy strategy can effectively avoid about 40% of prostate biopsies and maintain a high detection rate for clinically significant PCa. It can further provide valuable guidance for patients and physicians in considering the necessity of prostate biopsy.

Opportunistic Prostate Cancer Screening with Biparametric Magnetic Resonance Imaging (VISIONING).

BACKGROUND: This study investigates the use of biparametric magnetic resonance imaging (bpMRI) as primary opportunistic screening for prostate cancer (PCa) without using a prostate-specific antigen (PSA) cut-off. OBJECTIVE: The primary endpoint was to assess the efforts and effectiveness of identifying 20 participants with clinically significant prostate cancer (csPCa) using bpMRI. DESIGN, SETTING, AND PARTICIPANTS: Biopsy-naïve men aged over 45 yr were included. All participants underwent 3 Tesla bpMRI, PSA, and digital rectal examination (DRE). Targeted-only biopsy was performed in participants with Prostate Imaging Reporting and Data System (PI-RADS) ≥3. Men with negative bpMRI but suspicious DRE or elevated PSA/PSA density had template biopsies. Preintended protocol adjustments were made after an interim analysis for PI-RADS 3 lesions: no biopsy and follow-up MRI after 6 mo and biopsy only if lesions persisted or upgraded. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Biopsy results underwent a comparison using Fisher's exact test and univariable logistic regression to identify prognostic factors for positive biopsy. RESULTS AND LIMITATIONS: A total of 229 men were enrolled in this study, of whom 79 underwent biopsy. Among these men, 77 displayed suspicious PI-RADS lesions. PCa was detected in 29 participants (12.7%), of whom 21 had csPCa (9.2%). Biparametric MRI detected 21 csPCa cases, while PSA and DRE would have missed 38.1%. Protocol adjustment led to a 54.6% biopsy reduction in PI-RADS 3 lesions. Overall, in this cohort of men with a median PSA value of 1.26 ng/ml, 10.9 bpMRI scans were needed to identify one participant with csPCa. A major limitation of the study is the lack of a control cohort undergoing systematic biopsies. CONCLUSIONS: Opportunistic screening utilising bpMRI as a primary tool has higher sensitivity in detecting csPCa than classical screening methods. PATIENT SUMMARY: Screening with biparametric magnetic resonance imaging (bpMRI) and targeted biopsy identified clinically significant prostate cancer in every 11th man, regardless of the prostate-specific antigen (PSA) levels. Preselecting patients based on PSA >1 ng/ml and a positive family history of prostate cancer, as well as other potential blood tests may further improve the effectiveness of bpMRI in this setting.

[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.

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.

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.

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.

Effect of preoperative PI-RADS assessment on pathological outcomes in patients who underwent radical prostatectomy.

OBJECTIVE: To assess the effect of preoperative MRI with standardized Prostate Imaging-Reporting and Data System (PI-RADS) assessment on pathological outcomes in prostate cancer (PCa) patients who underwent radical prostatectomy (RP). PATIENTS AND METHODS: This retrospective cohort study included patients who had undergone prostate MRI and subsequent RP for PCa between January 2017 and December 2022. The patients were divided into the PI-RADS group and the non-PI-RADS group according to evaluation scheme of presurgery MRI. The preoperative characteristics and postoperative outcomes were retrieved and analyzed. The pathological outcomes included pathological T stage (pT2 vs. pT3-4) and positive surgical margins (PSMs). Patients were further stratified according to statistically significant preoperative variables to assess the difference in pathological outcomes. A propensity score matching based on the above preoperative characteristics was additionally performed. RESULTS: A total of 380 patients were included in this study, with 201 patients in the PI-RADS group and 179 in the non-PI-RADS group. The two groups had similar preoperative characteristics, except for clinical T stage (cT). As for pathological outcomes, the PI-RADS group showed a significantly lower percentage of pT3-4 (21.4% vs. 48.0%, p < 0.001), a lower percentage of PSMs (31.3% vs. 40.9%, p = 0.055), and a higher concordance between the cT and pT (79.1% vs. 64.8%, p = 0.003). The PI-RADS group also showed a lower proportion of pT3-4 (p < 0.001) in the cT1-2 subgroup and the cohort after propensity score matching. The PSM rate of cT3 patients was reduced by 39.2% in the PI-RADS group but without statistical significance (p = 0.089). CONCLUSIONS: Preoperative MRI with standardized PI-RADS assessment could benefit the decision-making of patients by reducing the rate of pathologically confirmed non-organ-confined PCa after RP and slightly reducing the PSM rate compared with non-PI-RADS assessment.

Evaluation of Multiparametric Magnetic Resonance Imaging and Correlation with Radical Prostatectomy Histopathology Specimen in Prostate Cancer.

Magnetic resonance imaging (MRI) has shown a great potential in the evaluation and management of prostate cancer. In this study, we would like to evaluate the benefit of multiparametric MRI in the detection and localization of prostate cancer by comparing it with the gold standard of histopathology from radical prostatectomy. In this single-centre prospective study, 90 consecutive patients underwent radical prostatectomy from November 2016 to May 2018. All patients first underwent multiparametric (mp)-MRI, and all suspicious regions of interest were delineated and recorded on a 5-point scale as defined in prostate imaging reporting and data system version 2 (PI-RADS V2) score. All radical prostatectomy specimens, acquired after robotic radical prostatectomy with extended pelvic lymphadenectomy, were sent for histopathological examination (HPE). The mean age of the 90 patients was 65.3 years, and the mean serum prostate-specific antigen (PSA) was 16.9 ng/ml. The sensitivity and specificity of mp-MRI in the detection of the corresponding region of interest (ROI) on HPE were 67.4% and 89.3% respectively. Positive predictive value (PPV), negative predictive value (NPV), and accuracy of mp-MRI in the detection of corresponding ROI on HPE were 86.3%, 73.3%, and 78.3% respectively. The mp-MRI detected 96.8% solitary lesions and 61.7% multifocal lesions on the corresponding ROI on HPE. Multiparametric MRI has an excellent specificity and reasonable sensitivity for the diagnosis of prostate cancer. It is a good modality for the detection of solitary tumours, higher-grade tumours, detection of seminal vesicle invasion and extracapsular extension and helps in the decision-making process before radical prostatectomy, focal therapy or selecting an appropriate candidate for active surveillance.

Added Value of Prostate-specific Antigen Density in Selecting Prostate Biopsy Candidates Among Men with Elevated Prostate-specific Antigen and PI-RADS ≥3 Lesions on Multiparametric Magnetic Resonance Imaging of the Prostate: A Systematic Assessment by PI-RADS Score.

BACKGROUND: A significant proportion of patients with positive multiparametric magnetic resonance imaging (mpMRI; Prostate Imaging-Reporting and Data System [PI-RADS] scores of 3-5) have negative biopsy results. OBJECTIVE: To systematically assess all prostate-specific antigen density (PSAD) values and identify an appropriate cutoff for identification of patients with positive mpMRI who could potentially avoid biopsy on the basis of their PI-RADS score. DESIGN, SETTING, AND PARTICIPANTS: The study included a cohort of 1341 patients with positive mpMRI who underwent combined targeted and systematic biopsies. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Multivariable logistic regression analysis (MVA) was used to assess the association between PSAD and the risk of clinically significant prostate cancer (csPCa, grade group ≥2) after adjusting for confounders. We used locally weighted scatterplot smoothing to explore csPCa risk according to PSAD and PI-RADS scores. PSAD utility was observed only for patients with PI-RADS 3 lesions, so we plotted the effect of each PSAD value as a cutoff for this subgroup in terms of biopsies saved, csPCa cases missed, and clinically insignificant PCa (ciPCa, grade group 1) cases not detected. RESULTS AND LIMITATIONS: Overall, 667 (50%) csPCa cases were identified. On MVA, PSAD independently predicted csPCa (odds ratio 1.57; p < 0.001). For PI-RADS ≥4 lesions, the csPCa risk was ≥40% regardless of PSAD. Conversely, among patients with PI-RADS 3 lesions, csPCa risk ranged from 0% to 60% according to PSAD values, and a PSAD cutoff of 0.10 ng/ml/cm3 corresponded to a threshold probability of 10% for csPCa. Using this PSAD cutoff for patients with PI-RADS 3 lesions would have saved 32% of biopsies, missed 7% of csPCa cases, and avoided detection of 34% of ciPCa cases. Limitations include selection bias and the high experience of the radiologists and urologists involved. CONCLUSIONS: Patients with PI-RADS ≥4 lesions should undergo prostate biopsy regardless of their PSAD, while PSAD should be used to stratify patients with PI-RADS 3 lesions. Using a threshold probability of 10% for csPCa, our data suggest that the appropriate strategy is to avoid biopsy in patients with PI-RADS 3 lesions and PSAD <0.10 ng/ml/cm3. Our results also provide information to help in tailoring an appropriate strategy for every patient with positive mpMRI findings. PATIENT SUMMARY: We investigated whether a cutoff value for PSAD (prostate-specific antigen density) could identify patients with suspicious prostate lesions on MRI (magnetic resonance imaging) who could avoid biopsy according to the PI-RADS score for their scan. We found that patients with PI-RADS ≥4 should undergo prostate biopsy regardless of their PSAD. A PSAD cutoff of 0.10 should be used to stratify patients with PI-RADS 3.

[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 Machine Learning Framework Reduces the Manual Workload for Systematic Reviews of the Diagnostic Performance of Prostate Magnetic Resonance Imaging.

Prostate magnetic resonance imaging has become the imaging standard for prostate cancer in various clinical settings, with interpretation standardized according to the Prostate Imaging Reporting and Data System (PI-RADS). Each year, hundreds of scientific studies that report on the diagnostic performance of PI-RADS are published. To keep up with this ever-increasing evidence base, systematic reviews and meta-analyses are essential. As systematic reviews are highly resource-intensive, we investigated whether a machine learning framework can reduce the manual workload and speed up the screening process (title and abstract). We used search results from a living systematic review of the diagnostic performance of PI-RADS (1585 studies, of which 482 were potentially eligible after screening). A naïve Bayesian classifier was implemented in an active learning environment for classification of the titles and abstracts. Our outcome variable was the percentage of studies that can be excluded after 95% of relevant studies have been identified by the classifier (work saved over sampling: WSS@95%). In simulation runs of the entire screening process (controlling for classifier initiation and the frequency of classifier updating), we obtained a WSS@95% value of 28% (standard error of the mean ±0.1%). Applied prospectively, our classification framework would translate into a significant reduction in manual screening effort. Patient summary: Systematic reviews of scientific evidence are labor-intensive and take a lot of time. For example, many studies on prostate cancer diagnosis via MRI (magnetic resonance imaging) are published every year. We describe the use of machine learning to reduce the manual workload in screening search results. For a review of MRI for prostate cancer diagnosis, this approach reduced the screening workload by about 28%.

Prostate Biopsy in the Case of PIRADS 5-Is Systematic Biopsy Mandatory?

Combining systematic biopsy (SB) with targeted biopsy (TB) in the case of a positive result from multiparametric magnetic resonance imaging (mpMRI) is a matter of debate. The Prostate Imaging Reporting and Data System (PIRADS) score of 5 indicates the highest probability of clinically significant prostate cancer (csPC) detection in TB. Potentially, omitting SB in the case of PIRADS 5 may have a marginal impact on the csPC detection rate. The aim of this study was to determine whether SB can be avoided in the case of PIRADS 5 and to identify potential factors allowing for performing TB only. This cohort study involved n = 225 patients with PIRADS 5 on mpMRI (PIRADS 2.0/2.1) who underwent transperineal or transrectal combined biopsy (CB). CsPC was diagnosed in 51.6% (n = 116/225) of cases. TB and SB resulted in the detection of csPC in 48% (n = 108/225) and 20.4% (n = 46/225) of cases, respectively (TB vs. SB, p < 0.001). When the TB was positive, SB detected csPC in n = 38 of the cases (38/108 = 35%). SB added to TB significantly improved csPC detection in 6.9% of cases in absolute terms (n = 8/116) (TB vs. CB, p = 0.008). The multivariate regression model proved that the significant predictors of csPC detection via SB were the densities of the prostate-specific antigen-PSAD > 0.17 ng/mL2 (OR = 4.038, 95%CI: 1.568-10.398); primary biopsy setting (OR = 2.818, 95%CI: 1.334-5.952); and abnormal digital rectal examination (DRE) (OR = 2.746, 95%CI: 1.328-5.678). In a primary biopsy setting (n = 103), SB detected 10% (n = 6/60) of the additional cases of csPC (p = 0.031), while in a repeat biopsy setting (n = 122), SB detected 3.5% (n = 2/56) of the additional cases of csPC (p = 0.5). In the case of PSAD > 0.17 ng/mL2 (n = 151), SB detected 7.4% (n = 7/95) of additional cases of csPC (p = 0.016), while in the case of PSAD < 0.17 ng/mL2 (n = 74), SB detected 4.8% (n = 1/21) of the additional cases of csPC (p = 1.0). The omission of SB had an impact on the csPC diagnosis rate in patients with PIRADS 5 score lesions. Patients who have already undergone prostate biopsy and those with low PSAD are at a lower risk of missing csPC when SB is avoided. However, performing TB only may result in missing other csPC foci located outside the index lesion, which can alter treatment decisions.

Artificial intelligence as diagnostic aiding tool in cases of Prostate Imaging Reporting and Data System category 3: the results of retrospective multi-center cohort study.

PURPOSE: To study the effect of artificial intelligence (AI) on the diagnostic performance of radiologists in interpreting prostate mpMRI images of the PI-RADS 3 category. METHODS: In this multicenter study, 16 radiologists were invited to interpret prostate mpMRI cases with and without AI. The study included a total of 87 cases initially diagnosed as PI-RADS 3 by radiologists without AI, with 28 cases being clinically significant cancers (csPCa) and 59 cases being non-csPCa. The study compared the diagnostic efficacy between readings without and with AI, the reading time, and confidence levels. RESULTS: AI changed the diagnosis in 65 out of 87 cases. Among the 59 non-csPCa cases, 41 were correctly downgraded to PI-RADS 1-2, and 9 were incorrectly upgraded to PI-RADS 4-5. For the 28 csPCa cases, 20 were correctly upgraded to PI-RADS 4-5, and 5 were incorrectly downgraded to PI-RADS 1-2. Radiologists assisted by AI achieved higher diagnostic specificity and accuracy than those without AI [0.695 vs 0.000 and 0.736 vs 0.322, both P < 0.001]. Sensitivity with AI was not significantly different from that without AI [0.821 vs 1.000, P = 1.000]. AI reduced reading time significantly compared to without AI (mean: 351 seconds, P < 0.001). The diagnostic confidence score with AI was significantly higher than that without AI (Cohen Kappa: -0.016). CONCLUSION: With the help of AI, there was an improvement in the diagnostic accuracy of PI-RADS category 3 cases by radiologists. There is also an increase in diagnostic efficiency and diagnostic confidence.

Patient-related characteristics predict prostate cancers in men with PI-RADS 4-5 to further optimize the diagnostic performance of MRI.

PURPOSE: To develop a prediction model based on patient-related characteristics for detecting prostate cancer (PCa) in patients with Prostate Imaging Reporting and Data System (PI-RADS) 4-5 in multiparametric magnetic resonance imaging (mp-MRI), aiming to optimize pre-biopsy risk stratification in MRI. MATERIALS AND METHODS: The patient-related characteristics including the lesion location, age, prostate-specific antigen (PSA), free prostate-specific antigen (fPSA), fPSA/PSA, prostate-specific antigen density (PSAD) and body mass index (BMI) were collected for patients who underwent mp-MRI and prostate biopsy between February 2014 and October 2022. Univariate and multivariate logistic regression analyses were conducted to select independent predictors of PCa and further create a prediction model. The diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Moreover, sensitivity, specificity, positive-predictive value (PPV) and negative-predictive value (NPV) were also calculated. RESULTS: A total of 833 patients were included in this study. In the subgroup PI-RADS 4, the independent characteristics of lesion location, age, fPSA/PSA and PSAD were selected to create the prediction model with an AUC of 0.748 (95% CI 0.694-0.803), sensitivity of 61.88%, specificity of 85.32%, PPV of 92.52%, and NPV of 43.26%. Besides, the prediction model in PI-RADS 5 was created using PSA and PSAD with an AUC of 0.893 (95% CI 0.844-0.941), sensitivity of 81.40%, specificity of 84.85%, PPV of 98.37% and NPV of 28.87%. CONCLUSION: The patient-related clinical characteristics were significant predictors of PCa and the prediction model based on selected characteristics could achieve a medium risk prediction of PCa in PI-RADS 4-5.

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.