Prostate Imaging Reporting And Data System Research Articles

A pictorial essay of PI-RADS pearls and pitfalls: toward less ambiguity and better practice.

Prostate Imaging Reporting and Data System (PI-RADS) was designed to standardize the interpretation of multiparametric magnetic resonance imaging (MRI) of the prostate, aiding in assessing the probability of clinically significant prostate cancer. By providing a structured scoring system, it enables better risk stratification, guiding decisions regarding the need for biopsy and subsequent treatment options. In this article, we explore both the strengths and weaknesses of PI-RADS, offering insights into its updated diagnostic performance and clinical applications, while also addressing potential pitfalls using diverse, representative MRI cases.

Biomarker vs MRI-Enhanced Strategies for Prostate Cancer Screening

Prostate cancer guidelines often recommend obtaining magnetic resonance imaging (MRI) before a biopsy, yet MRI access is limited. To date, no randomized clinical trial has compared the use of novel biomarkers for risk estimation vs MRI-based diagnostic approaches for prostate cancer screening. To evaluate biomarker-based risk estimation (Stockholm3 risk scores or prostate-specific antigen [PSA] levels) with systematic biopsies vs an MRI-enhanced strategy (PSA levels and MRI with systematic and targeted biopsy) for the detection of clinically significant prostate cancer in a screening setting. This open-label randomized clinical trial conducted in Stockholm, Sweden, between April 4, 2018, and December 10, 2020, recruited men aged 50 to 74 years with no history of prostate cancer. Participants underwent blood sampling for PSA and Stockholm3 tests to estimate their risk of clinically significant prostate cancer (Gleason score ≥3 + 4). After the blood tests were performed, participants were randomly assigned in a 2:3 ratio to receive a Stockholm3 test with systematic biopsy (biomarker group) or a PSA test followed by MRI with systematic and targeted biopsy (MRI-enhanced group). Data were analyzed from September 1 to November 5, 2023. In the biomarker group, men with a Stockholm3 risk score of 0.15 or higher underwent systematic biopsies. In the MRI-enhanced group, men with a PSA level of 3 ng/mL or higher had an MRI and those with a Prostate Imaging-Reporting and Data System (PI-RADS) score of 3 or higher (range: 1-5, with higher scores indicating a higher likelihood of clinically significant prostate cancer) underwent targeted and systematic biopsies. Primary outcome was detection of clinically significant prostate cancer (Gleason score ≥3 + 4). Secondary outcomes included detection of clinically insignificant cancer (Gleason score ≤6) and the number of biopsy procedures performed. Of 12 743 male participants (median [IQR] age, 61 [55-67] years), 5134 were assigned to the biomarker group and 7609 to the MRI-enhanced group. In the biomarker group, 8.0% of men (413) had Stockholm3 risk scores of 0.15 or higher and were referred for systematic biopsies. In the MRI-enhanced group, 12.2% of men (929) had a PSA level of 3 ng/mL or higher and were referred for MRI with biopsies if they had a PI-RADS score of 3 or higher. Detection rates of clinically significant prostate cancer were comparable between the 2 groups: 2.3% in the biomarker group and 2.5% in the MRI-enhanced group (relative proportion, 0.92; 95% CI, 0.73-1.15). More biopsies were performed in the biomarker group than in the MRI-enhanced group (326 of 5134 [6.3%] vs 338 of 7609 [4.4%]; relative proportion, 1.43 [95% CI, 1.23-1.66]), and more indolent prostate cancers were detected (61 [1.2%] vs 41 [0.5%]; relative proportion, 2.21 [95% CI, 1.49-3.27]). Findings of this randomized clinical trial indicate that combining a Stockholm3 test with systematic biopsies is comparable with MRI-based screening with PSA levels and systematic and targeted biopsies for detection of clinically significant prostate cancer, but this approach resulted in more biopsies as well as detection of a greater number of indolent cancers. In regions where access to MRI is lacking, the Stockholm3 test can aid in selecting patients for systematic prostate biopsy. ClinicalTrials.gov Identifier: NCT03377881.

Role of Multiparametric Magnetic Resonance Imaging and Targeted Biopsy in the Detection of Clinically Significant Prostate Cancer in Patients with Suspicious Digital Rectal Examination

Objectives: Few studies have examined the role of prostate MRI in patients with suspicious digital rectal examination (DRE) and/or PSA > 10 ng/mL. In a cohort of non-screened patients with suspicious DRE, we investigated the rate of avoidable prostate biopsies and potentially missed clinically significant prostate cancer (csPCa) with negative MRI, and the concordance between targeted biopsy (TBx) and systematic biopsy (SBx) in detecting csPCa with positive MRI. Methods: We retrospectively examined 199 biopsy-naïve patients with suspicious DRE who underwent prostate MRI before transperineal biopsy. Prostate Imaging—Reporting and Data System (PI-RADS) v2.1 ≥ 3 category of the index lesion defined a positive MRI. Combined TBx/SBx and SBx alone were performed for positive and negative MRI, respectively. An International Society of Urogenital Pathology Grade Group ≥ 2 defined csPCa. We calculated the csPCa detection rate of TBx, SBx, and combined TBx/SBx. The Cohen kappa statistic was used to measure the concordance between TBx and SBx. Results: Ninety-one (45.7%) csPCa cases were detected. MRI was positive in 153 (76.9%) patients. In the 46 patients with negative MRI, SBx detected 5 (10.9%) csPCa cases. Prostate biopsy could, thus, be avoided in 41/199 (20.6%) patients at the cost of missing 5/91 (5.5%) csPCa cases. The concordance between TBx and SBx in detecting csPCa with positive mpMRI was substantial (k = 0.70). Specifically, 6/86 (6.9%) csPCa cases were detected with TBx, and 17/86 (19.7%) with SBx alone. Concordance was almost perfect (k = 0.82) in patients with PSA > 10 ng/mL. Only 4/38 (10.5%) csPCa cases were missed by TBx, and only 1 (2.6%) csPCa case was identified by TBx alone. Conclusions: MRI in patients with suspicious DRE could avoid roughly 21% of unnecessary biopsies at the cost of missing approximately 6% of csPCa cases. Moreover, MRI and TBx complemented SBx in detecting csPCa in the subgroup with PSA > 10 ng/mL.

Novel clinical risk calculator for improving cancer predictability of mpMRI fusion biopsy in prostates.

Prostate Imaging-Reporting and Data System (PI-RADS) assists in evaluating lesions on multiparametric magnetic resonance imaging (mpMRI), but there are still ongoing efforts in improving the predictive value for the presence of clinically significant PCa (csPCa) with a Gleason grade group ≥ 2 on Fusion-Biopsy. This pilot study intends to propose an easily implementable method for augmenting predictability of csPCa for PI-RADS. A cohort of 151 consecutive patients underwent mpMRI Fusion and random US Biopsy as a result of having at least one PI-RADS lesion grade 3-5 between January 1, 2019 and December 31, 2022. A single radiologist reads all films in this study applying PI-RADS V2. Of the 151 consecutive patients, 49 had a highest lesion of PI-RADS 3, 82 had a highest lesion of PI-RADS 4, and 20 had a highest lesion of PI-RADS 5. For each respective group, 12, 42, and 18 patients had proven csPCa. Two predictive models for csPCa were created by employing a logistical regression with parameters readily available to providers. The models had an AUC of 0.8133 and 0.8206, indicating promising effective models. PI-RADS classification has relevant predictability problems for grades 3 and 4. By applying the presented risk calculators, patients with PI-RADS 3 and 4 are better stratified, and thus, a significant number of patients can be spared biopsies with potential complications, such as infection and bleeding. The presented predictive models may be a valuable diagnostic tool, adding additional information in the clinical decision-making process for biopsies.

Assessment of the accuracy of biparametric MRI/TRUS fusion-guided biopsy for index tumor evaluation using postoperative pathology specimens

BackgroundMultiparametric MRI (mpMRI) is widely used for the diagnosis, surveillance, and staging of prostate cancer. However, it has several limitations, including higher costs, longer examination times, and the use of gadolinium-based contrast agents. This study aimed to investigate the accuracy of preoperatively assessed index tumors (ITs) using biparametric MRI (bpMRI)/transrectal ultrasound (TRUS) fusion biopsy compared with radical prostatectomy (RP) specimens.MethodsWe included 113 patients diagnosed with prostate cancer through bpMRI/TRUS fusion-guided biopsies of lesions with a Prostate Imaging Reporting and Data System (PI-RADS) category ≥ 3. These patients underwent robot-assisted laparoscopic radical prostatectomy (RARP) at our institution between July 2017 and March 2023. We examined the localization of preoperative and postoperative ITs, the highest Gleason score (GS), and tumor diameter in these patients.ResultsThe preoperative cT stage matched the postoperative pT stage in 53 cases (47%), while 31 cases (27%) were upstaged, and 29 cases (26%) were downstaged (Weighted Kappa = 0.21). The preoperative and postoperative IT localizations were consistent in 97 cases (86%). The concordance rate between Gleason groups in targeted biopsies and RP specimens was 51%, with an upgrade in 25 cases (23%) and a downgrade in 27 cases (25%) (Weighted Kappa = 0.42). The maximum diameter of the IT and the maximum cancer core length on biopsy were correlated with the RP tumor's maximum diameter (p < 0.001 for both).ConclusionThe diagnostic accuracy of bpMRI/TRUS fusion biopsy is comparable to mpMRI, suggesting that it can be a cost-effective and time-saving alternative.

Prediction of false-positive PI-RADS 5 lesions on prostate multiparametric MRI: development and internal validation of a clinical-radiological characteristics based nomogram

BackgroundTo develop a risk model including clinical and radiological characteristics to predict false-positive The Prostate Imaging Reporting and Data System (PI-RADS) 5 lesions.MethodsData of 612 biopsy-naïve patients who had undergone multiparametric magnetic resonance imaging (mpMRI) before prostate biopsy were collected. Clinical variables and radiological variables on mpMRI were adopted. Lesions were divided into the training and validation cohort randomly. Stepwise multivariate logistic regression analysis with backward elimination was performed to screen out variables with significant difference. A diagnostic nomogram was developed in the training cohort and further validated in the validation cohort. Calibration curve and receiver operating characteristic (ROC) analysis were also performed.Results296 PI-RADS 5 lesions in 294 patients were randomly divided into the training and validation cohort (208 : 88). 132 and 56 lesions were confirmed to be clinically significant prostate cancer in the training and validation cohort respectively. The diagnostic nomogram was developed based on prostate specific antigen density, the maximum diameter of lesion, zonality of lesion, apparent diffusion coefficient minimum value and apparent diffusion coefficient minimum value ratio. The C-index of the model was 0.821 in the training cohort and 0.871 in the validation cohort. The calibration curve showed good agreement between the estimation and observation in the two cohorts. When the optimal cutoff values of ROC were 0.288 in the validation cohort, the sensitivity, specificity, PPV, and NPV were 90.6%, 67.9%, 61.7%, and 92.7% in the validation cohort, potentially avoiding 9.7% unnecessary prostate biopsies.ConclusionsWe developed and validated a diagnostic nomogram by including 5 factors. False positive PI-RADS 5 lesions could be distinguished from clinically significant ones, thus avoiding unnecessary prostate biopsy.

3T multiparametric MRI’s accuracy in detecting prostate cancer using Prostate Imaging Reporting and Data System (PIRADS) version 2.1 with prostate biopsy as a reference

BackgroundMultiparametric magnetic resonance imaging (MRI) is valuable in detecting prostate cancer due to its high sensitivity to malignant lesions. It is commonly utilized to improve the identification of clinically significant cancers within the prostate. This study aimed to correlate the findings from 3T multiparametric MRI of the prostate using the updated Prostate Imaging Reporting and Data System version 2.1 (PIRADSv2.1) from 2019 with reference to prostate biopsy results. Additionally, PIRADSv2.1 was used to calculate the sensitivity, specificity, positive predictive value, and negative predictive value of the 3T multiparametric MRI of the prostate.Methods and materialsA retrospective study was conducted at a tertiary center, wherein we identified patients who underwent a prostate biopsy between June 2019 and June 2021 and had a corresponding MRI of the prostate performed at the same institution, evaluated with PIRADSv2.1 criteria.ResultsA total of 50 patients were eligible for final analysis. The prevalence of prostate cancer was 69% (95% confidence interval (CI) 54–81%). Receiver operating characteristic (ROC) curves were generated for 3T multiparametric MRI of the prostate using PIRADSv2.1 to diagnose prostate cancer; the area under the ROC curve was 0.81 (95% CI 0.68–0.95, p < 0.001). The sensitivity, specificity, positive predictive value, and negative predictive value of the 3T multiparametric prostate MRI using PIRADSv2.1 were 74.0%, 87.0%, 92.9%, and 59.1%, respectively.ConclusionsPIRADSv2.1 exhibited good overall performance in the diagnosis of prostate cancer.

Utility of transperineal template-guided mapping prostate biopsy in biopsy-naïve men with PI-RADS 1-2 on multiparametric magnetic resonance imaging

ObjectiveTo analyze the outcomes of transperineal template-guided mapping biopsy (TTMB) in biopsy-naïve men with multiparametric magnetic resonance imaging (mpMRI) results of Prostate Imaging-Reporting and Data System (PI-RADS) 1-2. Patients and MethodsWe retrospectively reviewed TTMB outcomes in biopsy naïve patients with PI-RADS 1-2 at a single center from August 2018 to May 2023. The patients' clinicopathologic data were reviewed, clinically significant prostate cancer (csPCa) detection rates were identified. We determined significant predictive factors and determined those optimal cutoff point using receiver operating characteristic (ROC) curves. Results255 biopsy naïve patients with PI-RADS 1-2 underwent TTMB. 72 (28.2%) were diagnosed with prostate cancer and 30 (11.8%) were diagnosed with csPCa. ROC curves were used to identify predictive factors for diagnosing csPCa. Age (area under ROC curve [AUC]: 0.74, 95% CI: 0.65–0.83, p<0.001) and prostate specific antigen density (PSAD) (AUC: 0.63, 95% CI: 0.53–0.72, p=0.025) were significant predictive factors, and the optimal cutoff points determined using the Youden index were 65 years and 0.15 ng/mL/mL, respectively. ConclusionOf biopsy naïve patients classified as PI-RADS 1–2, 11.8% were diagnosed with csPCa, and we identified age and PSAD as significant predictive factors. Our study will help determine the biopsy method for patients with PI-RADS 1–2 without biopsy experience.

Diagnostic Utility of Artificial Intelligence–assisted Transperineal Biopsy Planning in Prostate Cancer Suspected Men: A Prospective Cohort Study

Background and objectiveAccurate magnetic resonance imaging (MRI) reporting is essential for transperineal prostate biopsy (TPB) planning. Although approved computer-aided diagnosis (CAD) tools may assist urologists in this task, evidence of improved clinically significant prostate cancer (csPCa) detection is lacking. Therefore, we aimed to document the diagnostic utility of using Prostate Imaging Reporting and Data System (PI-RADS) and CAD for biopsy planning compared with PI-RADS alone. MethodsA total of 262 consecutive men scheduled for TPB at our referral centre were analysed. Reported PI-RADS lesions and an US Food and Drug Administration–cleared CAD tool were used for TPB planning. PI-RADS and CAD lesions were targeted on TPB, while four (interquartile range: 2–5) systematic biopsies were taken. The outcomes were the (1) proportion of csPCa (grade group ≥2) and (2) number of targeted lesions and false-positive rate. Performance was tested using free-response receiver operating characteristic curves and the exact Fisher-Yates test. Key findings and limitationsOverall, csPCa was detected in 56% (146/262) of men, with sensitivity of 92% and 97% (p = 0.007) for PI-RADS– and CAD-directed TPB, respectively. In 4% (10/262), csPCa was detected solely by CAD-directed biopsies; in 8% (22/262), additional csPCa lesions were detected. However, the number of targeted lesions increased by 54% (518 vs 336) and the false-positive rate doubled (0.66 vs1.39; p = 0.009). Limitations include biopsies only for men at clinical/radiological suspicion and no multidisciplinary review of MRI before biopsy. Conclusions and clinical implicationsThe tested CAD tool for TPB planning improves csPCa detection at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences. Patient summaryThe computer-aided diagnosis tool tested for transperineal prostate biopsy planning improves the detection of clinically significant prostate cancer at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences.

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

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. 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. 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. 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.

Interaction of patient age and high-grade prostate cancer on targeted biopsies of MRI suspicious lesions.

To evaluate the interaction of patient age and Prostate Imaging-Reporting and Data System (PI-RADS) score in determining the grade of prostate cancer (PCa) identified on magnetic resonance imaging (MRI)-targeted biopsy in older men. From a prospectively accrued Institutional Review Board-approved comparative study of MRI-targeted and systematic biopsy between June 2012 and December 2022, men with at least one PI-RADS ≥3 lesion on pre-biopsy MRI and no prior history of PCa were selected. Ordinal and binomial logistic regression analyses were performed. A total of 2677 men met study criteria. The highest PI-RADS score was 3 in 1220 men (46%), 4 in 950 men (36%), and 5 in 507 men (19%). The median (interquartile range [IQR]) patient age was 66.7 (60.8-71.8) years, median (IQR) prostate-specific antigen (PSA) level was 6.1 (4.6-9.0) ng/mL, median (IQR) prostate volume was 48 (34-68) mL, and median (IQR) PSA density was 0.13 (0.08-0.20) ng/mL/mL. Clinically significant (cs)PCa and high-risk PCa were identified on targeted biopsy in 1264 (47%) and 321 (12%) men, respectively. Prevalence of csPCa and high-risk PCa were significantly higher in the older age groups. On multivariable analyses, patient age was significantly associated with csPCa but not high-risk PCa; PI-RADS score and the interaction of age and PI-RADS score were significantly associated with high-risk PCa but not csPCa. In our cohort, the substantial rate of high-risk PCa on MRI-ultrasound fusion targeted biopsies in older men, and its significant association with MRI findings, supports the value of pre-biopsy MRI to localise disease that could cause cancer mortality even in older men.

Segmentation versus detection: Development and evaluation of deep learning models for prostate imaging reporting and data system lesions localisation on Bi‐parametric prostate magnetic resonance imaging

AbstractAutomated prostate cancer detection in magnetic resonance imaging (MRI) scans is of significant importance for cancer patient management. Most existing computer‐aided diagnosis systems adopt segmentation methods while object detection approaches recently show promising results. The authors have (1) carefully compared performances of most‐developed segmentation and object detection methods in localising prostate imaging reporting and data system (PIRADS)‐labelled prostate lesions on MRI scans; (2) proposed an additional customised set of lesion‐level localisation sensitivity and precision; (3) proposed efficient ways to ensemble the segmentation and object detection methods for improved performances. The ground‐truth (GT) perspective lesion‐level sensitivity and prediction‐perspective lesion‐level precision are reported, to quantify the ratios of true positive voxels being detected by algorithms over the number of voxels in the GT labelled regions and predicted regions. The two networks are trained independently on 549 clinical patients data with PIRADS‐V2 as GT labels, and tested on 161 internal and 100 external MRI scans. At the lesion level, nnDetection outperforms nnUNet for detecting both PIRADS ≥ 3 and PIRADS ≥ 4 lesions in majority cases. For example, at the average false positive prediction per patient being 3, nnDetection achieves a greater Intersection‐of‐Union (IoU)‐based sensitivity than nnUNet for detecting PIRADS ≥ 3 lesions, being 80.78% ± 1.50% versus 60.40% ± 1.64% (p &lt; 0.01). At the voxel level, nnUnet is in general superior or comparable to nnDetection. The proposed ensemble methods achieve improved or comparable lesion‐level accuracy, in all tested clinical scenarios. For example, at 3 false positives, the lesion‐wise ensemble method achieves 82.24% ± 1.43% sensitivity versus 80.78% ± 1.50% (nnDetection) and 60.40% ± 1.64% (nnUNet) for detecting PIRADS ≥ 3 lesions. Consistent conclusions are also drawn from results on the external data set.

MRI in-bore biopsy following MRI/US fusion-guided biopsy in patients with persistent suspicion of clinically significant prostate cancer

PurposePatients with suspicion of clinically significant prostate cancer (csPC) on multiparametric prostate MRI (mpMRI) but negative or inconclusive MRI/US fusion-guided biopsy (FB) can be challenging in clinical practice. To assess the utility of MRI in-bore biopsy (IB) in patients with discordant imaging and histopathological findings after FB. MethodsConsecutive patients with Prostate Imaging Reporting and Data System (PI-RADS) category 4 or 5 on mpMRI at 3T after FB without histologically confirmed csPC who underwent IB between 01/2014 and 05/2022, were retrospectively included. The primary objective was to assess the detection rate of csPC. Secondary objectives were to analyze clinical parameters, MRI parameters, and lesion localization. ResultsIn the final cohort of 51 patients, the IB resulted in an overall detection rate of 71% for PC and 47% for csPC. Furthermore, in 55% of cases with initial low-grade PC, the Gleason score was upgraded after IB. CsPC was often detected apical and/or anterior. The detection rate for PC was 58% in PI-RADS category 4 and 94% in PI-RADS category 5 (csPC 39% and 61%, respectively). Patients with csPC had statistically significant smaller prostate volumes, a higher PI-RADS category, a higher prostate-specific antigen density (PSAD), and were older. ConclusionsFor a relevant proportion of patients with PI-RADS category 4 or 5 and negative or inconclusive findings on previous FB, but with persistent suspicion of csPC, a subsequent IB verified the presence of csPC. Therefore, IB can be a backup in cases of uncertainty.

PI-RADS v2.1 and PSAD for the prediction of clinically significant prostate cancer among patients with PSA levels of 4–10 ng/ml

This study intended to evaluate the diagnostic accuracy of the prostate imaging reporting and data system (PI-RADS) and prostate-specific antigen density (PSAD) for clinically significant prostate cancer (csPCa) with PSA levels of 4–10 ng/ml. Between July 2018 and June 2022, a total of 453 patients with PSA levels of 4–10 ng/ml were retrospectively included, which were randomly assigned to the training group (323 patients) and validation group (130 patients). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with their 95% CI were calculated. The overall diagnostic performance was determined with area under the receiver operating characteristic curve (AUC), and an integrated nomogram combining PI-RADS score and PSAD was constructed and tested in a validation cohort. In the training group, the AUC for PI-RADS 2.1 and PSAD alone were 0.875 (95% CI 0.834–0.916) and 0.712 (95% CI 0.648–0.775). At the cutoff PI-RADS score ≥ 4, the sensitivity and specificity were 86.2% (95% CI 77.4–1.9%) and 84.7% (95% CI 79.6–88.8%), respectively. For PSAD, the sensitivity and specificity were 73.3% (95% CI 63.0–82.4%) and 62.1% (95% CI 55.8–68.5%) at the cutoff 0.162 ng/ml/ml. While combining PI-RADS with PSAD, the diagnostic performance was improved significantly, with AUC of 0.893 (95% CI 0.853–0.933). In the validation group, the nomogram yielded a AUC of 0.871 (95% CI 0.807–0.934), which is significantly higher than PI-RADS alone (0.829, 95% CI 0.759–0.899, P = 0.02). For patients with PSA levels of 4–10 ng/ml, PSAD demonstrated moderate diagnostic accuracy whereas PI-RADS showed high performance. By combination of PSAD and PI-RADS together, the diagnostic performance could be improved significantly.

The impact of the relationship between lesion diameter and total core length on the detection rate of clinically significant prostate cancer for PI-RADS 3 lesions

BackgroundThe aim of our study was to determine the effect of total core length (TCL) for prostate imaging reporting and data system (PI-RADS) 3 lesions to facilitate clinically significant prostate cancer (csPCa) detection based on the lesion diameter.Materials and methodsA total of 149 patients with at least 1 lesion with a PI-RADS 3 were evaluated retrospectively. The lesions with diameters of < 1 cm were categorized as small lesions and lesions of ≥ 1 cm were categorized as large lesions. The lengths of biopsy cores from PI-RADS 3 lesions were summed for each lesion separately, and TCL was calculated. The relationship between TCL and csPCa was analyzed separately for the small and large groups with multiple logistic regression analyses.ResultsA total of 208 lesions were detected by multiparametric magnetic resonance imaging (MpMRI) in 149 males included in the study. The mean TCL was 44.68 mm (26–92) and the mean lesion diameter was 10.73 mm (4–27) in PIRADS 3 lesions. For small diameter lesions (< 1 cm), the odds of finding clinically insignificant prostate cancer (ciPCa) increase by 1.67 times if TCL increases by one unit. Hence, increasing TCL for small lesions only increases the odds of ciPCa detection. For large diameter lesions (≥ 1 cm), if TCL increases by one unit, the odds of finding ciPCa increase 1.13 times and the odds of finding csPCa increases1.16 times. Accordingly, large lesions are more likely to have both csPCa and ciPCa as TCL increases.ConclusionsOur study showed that for PI-RADS 3 lesions, both more csPCa and more ciPCa were detected as TCL increased. However, in lesions with a size of < 1 cm, only ciPCa was detected more frequently as TCL increased. In conclusion, taking more and longer biopsy cores in PI-RADS 3 lesions below 1 cm does not contribute to the detection of csPCa.

The Value of 68Ga-PSMA PET/MRI for Classifying Patients with PI-RADS 3 Lesions on Multiparametric MRI: A Prospective Single-Center Study.

Prostate Imaging Reporting and Data System (PI-RADS) category 3 lesions remain a diagnostic challenge for detecting clinically significant prostate cancer (csPCa). This article evaluates the added value of 68Ga-labeled prostate-specific membrane antigen-11 (68Ga-PSMA) PET/MRI in classifying PI-RADS 3 lesions to avoid unnecessary biopsies. Methods: Sixty biopsy-naïve men with PI-RADS 3 lesions on multiparametric MRI were prospectively enrolled between February 2020 and October 2022. In all, 56 participants underwent 68Ga-PSMA PET/MRI and prostate systematic biopsy. 68Ga-PSMA PET/MRI was independently evaluated and reported by the 5-level PRIMARY score developed within the PRIMARY trial. Receiver-operating-characteristic curve analysis was used to estimate the diagnostic performance. Results: csPCa was detected in 8 of 56 patients (14.3%). The proportion of patients with csPCa and a PRIMARY score of 1, 2, 3, 4, and 5 was 0% (0/12), 0% (0/13), 6.3% (1/16), 38.5% (5/13), and 100% (2/2), respectively. The estimated area under the curve of the PRIMARY score was 0.91 (95% CI, 0.817-0.999). For a PRIMARY score of 4-5 versus a PRIMARY score of 1-3, the sensitivity, specificity, positive predictive value, and negative predictive value were 87.5%, 83.3%, 46.7%, and 97.5%, respectively. With a PRIMARY score of at least 4 to make a biopsy decision in men with PI-RADS 3 lesions, 40 of 48 patients (83.3%) could avoid unnecessary biopsies, at the expense of missing 1 of 8 (12.5%) csPCa cases. Conclusion: 68Ga-PSMA PET/MRI has great potential to classify patients with PI-RADS 3 lesions and help avoid unnecessary biopsies.

Utility of positive core number on MRI-ultrasound fusion targeted biopsy in combination with PI-RADS scores for predicting unexpected extracapsular extension of clinically localized prostate cancer.

To evaluate the utility of magnetic resonance imaging (MRI) and MRI-ultrasound fusion targeted biopsy (TB) for predicting unexpected extracapsular extension (ECE) in clinically localized prostate cancer (CLPC). This study enrolled 89 prostate cancer patients with one or more lesions showing a Prostate Imaging-Reporting and Data System (PI-RADS) score ≥3 but without morphological abnormality in the prostatic capsule on pre-biopsy MRI. All patients underwent TB and systematic biopsy followed by radical prostatectomy (RP). Each lesion was examined by 3-core TB, taking cores from each third of the lesion. The preoperative variables predictive of ECE were explored by referring to RP specimens in the lesion-based analysis. Overall, 186 lesions, including 81 (43.5%), 73 (39.2%), and 32 (17.2%) with PI-RADS 3, 4, and 5, respectively, were analyzed. One hundred and twenty-two lesions (65.6%) were diagnosed as cancer on TB, and ECE was identified in 33 (17.7%) on the RP specimens. The positive TB core number was ≤2 in 129 lesions (69.4%) and three in 57 lesions (30.6%). On the multivariate analysis, PI-RADS ≥4 (p = 0.049, odds ratio [OR] = 2.39) and three positive cores on TB (p = 0.005, OR = 3.07) were independent predictors of ECE. Lesions with PI-RADS ≥4 and a positive TB core number of 3 had a significantly higher rate of ECE than those with PI-RADS 3 and a positive TB core number ≤2 (37.5% vs. 7.8%, p < 0.001). Positive TB core number in combination with PI-RADS scores is helpful to predict unexpected ECE in CLPC.

Pearls and Pitfalls in Applying PI-RADS 2.1

AbstractThe use of Prostate Imaging Reporting and Data System (PI-RADS) with multiparametric magnetic resonance imaging (MRI) has significantly improved the detection of clinically significant prostate cancer (csPCa), but there are certain challenges that the reader may face. This review provides an overview of the pitfalls associated with the PI-RADS system for multiparametric prostate MRI (mpMRI), with suggestions/pearls to help overcome these pitfalls.PI-RADS assessment is hindered by several causes of false positives (FPs) and false negatives (FNs). In addition, there is wide variability in the positive predictive value (PPV) of the PI-RADS system across different centers, highlighting the need for improvement. While the negative predictive value (NPV) for csPCa is generally high, variations exist.This review discusses the pitfalls contributing to FNs, including MRI artifacts, such as susceptibility and motion artifacts. Techniques to optimize image acquisition, such as switching the phase encoding direction and reducing bowel peristalsis, are suggested to mitigate these artifacts. Improper b-value selection for diffusion-weighted imaging (DWI) is another pitfall, emphasizing the importance of using high b-values (≥1,400 s/mm2) to optimize neoplasm detection. Similarly, optimizing window settings to visualize csPCa, correctly positioning the endorectal coil, awareness of rare variants like mucinous adenocarcinoma and cribriform adenocarcinoma, and distinguishing central zone tumors from normal central zone are discussed.This article highlights the common pitfalls causing FPs, such as benign pathologies like prostatitis, granulomatous prostatitis, prostatic abscess, stromal BPH nodules, extruded BPH nodules, and prostatic calcifications. It also discusses the pitfalls related to normal anatomical structures, including the central zone, anterior fibromuscular stroma, thickened surgical capsule, neurovascular bundle, and periprostatic venous plexus. Techniques for carefully evaluating these entities' morphology and distribution of signal abnormalities are described to avoid overdiagnosing these as PCa. The article also addresses the pitfalls related to postintervention changes, including postbiopsy hemorrhage and artifacts caused by the UroLift procedure, while providing recommendations for managing these challenges.Finally, the pitfalls that may be encountered during staging, including evaluation for extraprostatic extension, and pelvic nodal and osseous metastases, are highlighted.

Prediction of clinically significant prostate cancer using radiomics models in real-world clinical practice: a retrospective multicenter study

PurposeTo develop and evaluate machine learning models based on MRI to predict clinically significant prostate cancer (csPCa) and International Society of Urological Pathology (ISUP) grade group as well as explore the potential value of radiomics models for improving the performance of radiologists for Prostate Imaging Reporting and Data System (PI-RADS) assessment.Material and methodsA total of 1616 patients from 4 tertiary care medical centers were retrospectively enrolled. PI-RADS assessments were performed by junior, senior, and expert-level radiologists. The radiomics models for predicting csPCa were built using 4 machine-learning algorithms. The PI-RADS were adjusted by the radiomics model. The relationship between the Rad-score and ISUP was evaluated by Spearman analysis.ResultsThe radiomics models made using the random forest algorithm yielded areas under the receiver operating characteristic curves (AUCs) of 0.874, 0.876, and 0.893 in an internal testing cohort and external testing cohorts, respectively. The AUC of the adjusted_PI-RADS was improved, and the specificity was improved at a slight sacrifice of sensitivity. The participant-level correlation showed that the Rad-score was positively correlated with ISUP in all testing cohorts (r > 0.600 and p < 0.0001).ConclusionsThis radiomics model resulted as a powerful, non-invasive auxiliary tool for accurately predicting prostate cancer aggressiveness. The radiomics model could reduce unnecessary biopsies and help improve the diagnostic performance of radiologists’ PI-RADS. Yet, prospective studies are still needed to validate the radiomics models further.Critical relevance statementThe radiomics model with MRI may help to accurately screen out clinically significant prostate cancer, thereby assisting physicians in making individualized treatment plans.Key points• The diagnostic performance of the radiomics model using the Random Forest algorithm is comparable to the Prostate Imaging Reporting and Data System (PI-RADS) obtained by radiologists.• The performance of the adjusted Prostate Imaging Reporting and Data System (PI-RADS) was improved, which implied that the radiomics model could be a potential radiological assessment tool.• The radiomics model lowered the percentage of equivocal cases. Moreover, the Rad-scores can be used to characterize prostate cancer aggressiveness.Graphical

Understanding Spatial Correlation Between Multiparametric MRI Performance and Prostate Cancer.

Multiparametric MRI (mpMRI) has shown a substantial impact on prostate cancer (PCa) diagnosis. However, the understanding of the spatial correlation between mpMRI performance and PCa location is still limited. To investigate the association between mpMRI performance and tumor spatial location within the prostate using a prostate sector map, described by Prostate Imaging Reporting and Data System (PI-RADS) v2.1. Retrospective. One thousand one hundred forty-three men who underwent mpMRI before radical prostatectomy between 2010 and 2022. 3.0 T. T2-weighted turbo spin-echo, a single-shot spin-echo EPI sequence for diffusion-weighted imaging, and a gradient echo sequence for dynamic contrast-enhanced MRI sequences. Integrated relative cancer prevalence (rCP), detection rate (DR), and positive predictive value (PPV) maps corresponding to the prostate sector map for PCa lesions were created. The relationship between tumor location and its detection/missing by radiologists on mpMRI compared to WMHP as a reference standard was investigated. A weighted chi-square test was performed to examine the statistical differences for rCP, DR, and PPV of the aggregated sectors within the zone, anterior/posterior, left/right prostate, and different levels of the prostate with a statistically significant level of 0.05. A total of 1665 PCa lesions were identified in 1143 patients, and from those 1060 lesions were clinically significant (cs)PCa tumors (any Gleason score [GS] ≥7). Our sector-based analysis utilizing weighted chi-square tests suggested that the left posterior part of PZ had a high likelihood of missing csPCa lesions at a DR of 67.0%. Aggregated sector analysis indicated that the anterior or apex locations in PZ had the significantly lowest csPCa detection at 67.3% and 71.5%, respectively. Spatial characteristics of the per-lesion-based mpMRI performance for diagnosis of PCa were studied. Our results demonstrated that there is a spatial correlation between mpMRI performance and locations of PCa on the prostate. 4 TECHNICAL EFFICACY: Stage 2.