Precision in prostate cancer detection: integrating prostate-specific antigen density (PSAD) and the Prostate Imaging Reporting and Data System (PI-RADS) to provide additional risk stratification for a more accurate diagnostic decision.

PURPOSE: This study focuses on integrating prostate-specific antigen density (PSAD) and Prostate Imaging Reporting and Data System (PI-RADS) for enhanced risk stratification in biopsy-naïve patients. METHODS: A prospective study was conducted on 339 patients with suspected prostate cancer, utilizing PSAD and PI-RADS in combination. Logistic regression models were employed, and receiver operating characteristic (ROC) analysis performed to evaluate predictive performance. The patient cohort underwent multiparametric MRI, targeted biopsy, and systematic biopsy. RESULTS: When patients were stratified into four PSAD risk groups, the rate of clinically significant prostate cancer (csPCa) increased significantly with higher PSAD levels. Logistic regression confirmed the independent contribution of PI-RADS and PSAD, highlighting their role in the prediction of csPCa. Combined models showed superior performance, as evidenced by the area under the curve (AUC) for PI-RADS category and PSAD (0.756), which exceeded that of the individual predictors (PSA AUC, 0.627, PI-RADS AUC 0.689, PSAD AUC 0.708). CONCLUSION: This study concludes that combining PSAD and PI-RADS improves diagnostic accuracy and predictive value for csPCa in biopsy-naïve men, resulting in a promising strategy to provide additional risk stratification for more accurate diagnostic decision in biopsy-naïve patients, especially in the PI-RADS 3 group.

Urine biomarkers can predict prostate cancer and PI-RADS score prior to biopsy.

Prostate-Specific Antigen (PSA) based screening of prostate cancer (PCa) needs refinement. The aim of this study was the identification of urinary biomarkers to predict the Prostate Imaging-Reporting and Data System (PI-RADS) score and the presence of PCa prior to prostate biopsy. Urine samples from patients with elevated PSA were collected prior to prostate biopsy (cohort = 99). The re-analysis of mass spectrometry data from 45 samples was performed to identify urinary biomarkers to predict the PI-RADS score and the presence of PCa. The most promising candidates, i.e. SPARC-like protein 1 (SPARCL1), Lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1), Alpha-1-microglobulin/bikunin precursor (AMBP), keratin 13 (KRT13), cluster of differentiation 99 (CD99) and hornerin (HRNR), were quantified by ELISA and validated in an independent cohort of 54 samples. Various biomarker combinations showed the ability to predict the PI-RADS score (AUC = 0.79). In combination with the PI-RADS score, the biomarkers improve the detection of prostate carcinoma-free men (AUC = 0.89) and of those with clinically significant PCa (AUC = 0.93). We have uncovered the potential of urinary biomarkers for a test that allows a more stringent prioritization of mpMRI use and improves the decision criteria for prostate biopsy, minimizing patient burden by decreasing the number of unnecessary prostate biopsies.

Clinical-imaging metrics for the diagnosis of prostate cancer in PI-RADS 3 lesions.

OBJECTIVE: To explore the feasibility and efficacy of clinical-imaging metrics in the diagnosis of prostate cancer (PCa) and clinically significant prostate cancer (csPCa) in prostate imaging-reporting and data system (PI-RADS) category 3 lesions. METHODS: A retrospective analysis was conducted on lesions diagnosed as PI-RADS 3. They were categorized into benign, non-csPCa and csPCa groups. Apparent diffusion coefficient (ADC), T2-weighted imaging signal intensity (T2WISI), coefficient of variation of ADC and T2WISI, prostate-specific antigen density (PSAD), ADC density (ADCD), prostate-specific antigen lesion volume density (PSAVD) and ADC lesion volume density (ADCVD) were measured and calculated. Univariate and multivariate analyses were used to identify risk factors associated with PCa and csPCa. Receiver operating characteristic curve (ROC) and decision curves were utilized to assess the efficacy and net benefit of independent risk factors. RESULTS: Among 202 patients, 133 had benign prostate disease, 25 non-csPCa and 44 csPCa. Age, PSA and lesion location showed no significant differences (P > 0.05) among the groups. T2WISI and coefficient of variation of ADC (ADCcv) were independent risk factors for PCa in PI-RADS 3 lesions, yielding an area under the curve (AUC) of 0.68. ADC was an independent risk factor for csPCa in PI-RADS 3 lesions, yielding an AUC of 0.65. Decision curve analysis showed net benefit for patients at certain probability thresholds. CONCLUSIONS: T2WISI and ADCcv, along with ADC, respectively showed considerable promise in enhancing the diagnosis of PCa and csPCa in PI-RADS 3 lesions.

Molecular and diffusion features for identification of clinically significant prostate cancer in PI-RADS 3 lesions.

BACKGROUND: The recommendation to perform biopsy of PIRADS 3 lesions has not been adopted with strength as compared to higher scored lesions on multiparametric MRI. This represents a challenging scenario and an unmet need for clinicians to apply a risk adapted approach in these cases. In the present study, we examined clinical and radiologic characteristics in men with PI-RADS 3 index lesions that can predict csPCa on mpMRI-target biopsy. METHODS: Revision of a prospective database with patients who underwent targeted and systematic biopsies from 2015 to 2023 for PI-RADS 3 lesions identified on mpMRI. Baseline variables were collected, such as PSA density (PSAd), 4Kscore, prostate size, and the apparent diffusion coefficient (ADC) value of the lesion on mpMRI. Logistic regression, receiver operating characteristic (ROC) and decision curve analyses (DCA) assessing the association between clinic-radiologic factors and csPCa were performed. RESULTS: Overall, 230 patients were included in the study and the median age was 65 years. The median prostate size and PSA were 50 g and 6.26 ng/mL, respectively. 17.4% of patients had csPCa, while 27.5% had Gleason group 1. In univariable logistic analyses, we found that age, BMI, prostate size, PSAd, ADC, and 4Kscore were significant csPCa predictors (P < 0.05). PSAd showed the best prediction performance in terms of AUC (= 0.679). On multivariable analysis, PSAd and 4Kscore were associated with csPCa. The net benefit of PSAd combined with clinical features was superior to those of other parameters. Within patients with PSAd < 0.15, 4Kscore was a statistically significant predictor of csPCa (OR = 3.25, P = 0.032). CONCLUSION: PSAd and 4Kscore are better predictors of csPCa in patients with PIRADS 3 lesions compared to ADC. The predictive role of 4Kscore is higher in patients with low PSAd. These results can assist practitioners in the risk stratification of patients with equivocal lesions to determine the need of biopsy.

Case-by-case combination of the prostate imaging reporting and data system version 2.1 with the Likert score to reduce the false-positives of prostate MRI: a proof-of-concept study.

OBJECTIVES: To retrospectively investigate whether a case-by-case combination of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) with the Likert score improves the diagnostic performance of mpMRI for clinically significant prostate cancer (csPCa), especially by reducing false-positives. METHODS: One hundred men received mpMRI between January 2020 and April 2021, followed by prostate biopsy. Reader 1 (R1) and reader 2 (R2) (experience of > 3000 and < 200 mpMRI readings) independently reviewed mpMRIs with the PI-RADS version 2.1. After unveiling clinical information, they were free to add (or not) a Likert score to upgrade or downgrade or reinforce the level of suspicion of the PI-RADS category attributed to the index lesion or, rather, identify a new index lesion. We calculated sensitivity, specificity, and predictive values of R1/R2 in detecting csPCa when biopsying PI-RADS ≥ 3 index-lesions (strategy 1) versus PI-RADS ≥ 3 or Likert ≥ 3 index-lesions (strategy 2), with decision curve analysis to assess the net benefit. In strategy 2, the Likert score was considered dominant in determining biopsy decisions. RESULTS: csPCa prevalence was 38%. R1/R2 used combined PI-RADS and Likert categorization in 28%/18% of examinations relying mainly on clinical features such as prostate specific antigen level and digital rectal examination than imaging findings. The specificity/positive predictive values were 66.1/63.1% for R1 (95%CI 52.9-77.6/54.5-70.9) and 50.0/51.6% (95%CI 37.0-63.0/35.5-72.4%) for R2 in the case of PI-RADS-based readings, and 74.2/69.2% for R1 (95%CI 61.5-84.5/59.4-77.5%) and 56.6/54.2% (95%CI 43.3-69.0/37.1-76.6%) for R2 in the case of combined PI-RADS/Likert readings. Sensitivity/negative predictive values were unaffected. Strategy 2 achieved greater net benefit as a trigger of biopsy for R1 only. CONCLUSION: Case-by-case combination of the PI-RADS version 2.1 with Likert score translated into a mild but measurable impact in reducing the false-positives of PI-RADS categorization, though greater net benefit in reducing unnecessary biopsies was found in the experienced reader only.

Enhancing prostate cancer diagnosis and reducing unnecessary biopsies with [18F]DCFPyL PET/CT imaging in PI-RADS 3/4 patients.

For patients presenting with prostate imaging reporting and data system (PI-RADS) 3/4 findings on magnetic resonance imaging (MRI) examinations, the standard recommendation typically involves undergoing a biopsy for pathological assessment to ascertain the nature of the lesion. This course of action, though essential for accurate diagnosis, invariably amplifies the psychological distress experienced by patients and introduces a host of potential complications associated with the biopsy procedure. However, [18F]DCFPyL PET/CT imaging emerges as a promising alternative, demonstrating considerable diagnostic efficacy in discerning benign prostate lesions from malignant ones. This study aims to explore the diagnostic value of [18F]DCFPyL PET/CT imaging for prostate cancer in patients with PI-RADS 3/4 lesions, assisting in clinical decision-making to avoid unnecessary biopsies. 30 patients diagnosed with PI-RADS 3/4 lesions through mpMRI underwent [18F]DCFPyL PET/CT imaging, with final biopsy pathology results as the "reference standard". Diagnostic performance was assessed through receiver operating characteristic (ROC) analysis, evaluating the diagnostic efficacy of molecular imaging PSMA (miPSMA) visual analysis and semi-quantitative analysis in [18F]DCFPyL PET/CT imaging. Lesions were assigned miPSMA scores according to the prostate cancer molecular imaging standardized evaluation criteria. Among the 30 patients, 13 were pathologically confirmed to have prostate cancer. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of visual analysis in [18F]DCFPyL PET/CT imaging for diagnosing PI-RADS 3/4 lesions were 61.5%, 88.2%, 80.0%, 75.0%, and 76.5%, respectively. Using SUVmax 4.17 as the optimal threshold, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for diagnosis were 92.3%, 88.2%, 85.7%, 93.8%, and 90.0%, respectively. The area under the ROC curve (AUC) for semi-quantitative analysis was 0.94, significantly higher than visual analysis at 0.80. [18F]DCFPyL PET/CT imaging accurately diagnosed benign lesions in 15 (50%) of the PI-RADS 3/4 patients. For patients with PI-RADS 4 lesions, the positive predictive value of [18F]DCFPyL PET/CT imaging reached 100%. [18F]DCFPyL PET/CT imaging provides potential preoperative prediction of lesion nature in mpMRI PI-RADS 3/4 patients, which may aid in treatment decision-making and reducing unnecessary biopsies.

A Feasibility Study of AI-Assisted Compressed Sensing in Prostate T2-Weighted Imaging.

RATIONALE AND OBJECTIVES: To evaluate the image quality and PI-RADS scoring performance of prostate T2-weighted imaging (T2WI) based on AI-assisted compressed sensing (ACS). MATERIALS AND METHODS: In this prospective study, adult male urological outpatients or inpatients underwent prostate MRI, including T2WI, diffusion-weighted imaging and apparent diffusion coefficient maps. Three accelerated scanning protocols using parallel imaging (PI) and ACS: T2WIPI, T2WIACS1 and T2WIACS2 were evaluated through comparative analysis. Quantitative analysis included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), slope profile, and edge rise distance (ERD). Image quality was qualitatively assessed using a five-point Likert scale (ranging from 1 = non-diagnostic to 5 = excellent). PI-RADS scores were determined for the largest or most suspicious lesions in each patient. The Friedman test and one-way ANOVA with post hoc tests were utilized for group comparisons, with statistical significance set at P < 0.05. RESULTS: This study included 40 participants. Compared to PI, ACS reduced acquisition time by over 50%, significantly enhancing the CNR of sagittal and axial T2WI (P < 0.05), significantly improving the image quality of sagittal and axial T2WI (P < 0.05). No significant differences were observed in slope profile, ERD, and PI-RADS scores between groups (P > 0.05). CONCLUSION: ACS reduced prostate T2WI acquisition time by half while improving image quality without affecting PI-RADS scores.

Integration of PSAd and multiparametric MRI to forecast biopsy outcomes in biopsy-naïve patients with PSA 4~20 ng/ml.

Introduction: This study aims to investigate whether the transrectal ultrasound-guided combined biopsy (CB) improves the detection rates of prostate cancer (PCa) and clinically significant PCa (csPCa) in biopsy-naïve patients. We also aimed to compare the Prostate Imaging Reporting and Data System (PI-RADS v2.1) score, ADC values, and PSA density (PSAd) in predicting csPCa by the combined prostate biopsy. Methods: This retrospective and single-center study included 389 biopsy-naïve patients with PSA level 4~20 ng/ml, of whom 197 underwent prebiopsy mpMRI of the prostate. The mpMRI-based scores (PI-RADS v2.1 scores and ADC values) and clinical parameters were collected and evaluated by logistic regression analyses. Multivariable models based on the mpMRI-based scores and clinical parameters were developed by the logistic regression analyses to forecast biopsy outcomes of CB in biopsy-naïve patients. The ROC curves measured by the AUC values, calibration plots, and DCA were performed to assess multivariable models. Results: The CB can detect more csPCa compared with TRUSB (32.0% vs. 53%). The Spearman correlation revealed that Gleason scores of the prostate biopsy significantly correlated with PI-RADS scores and ADC values. The multivariate logistic regression confirmed that PI-RADS scores 4, 5, and prostate volume were important predictors of csPCa. The PI-RADS+ADC+PSAd (PAP) model had the highest AUCs of 0.913 for predicting csPCa in biopsy-naïve patients with PSA level 4~20 ng/ml. When the biopsy risk threshold of the PAP model was greater than or equal to 0.10, 51% of patients could avoid an unnecessary biopsy, and only 5% of patients with csPCa were missed. Conclusion: The prebiopsy mpMRI and the combined prostate biopsy have a high CDR of csPCa in biopsy-naïve patients. A multivariable model based on the mpMRI-based scores and PSAd could provide a reference for clinicians in forecasting biopsy outcomes in biopsy-naïve patients with PSA 4~20 ng/ml and make a more comprehensive assessment during the decision-making of the prostate biopsy.

Optimizing Prostate Imaging Practices in Saudi Arabian Hospitals: A Comprehensive Analysis of PI-RADS Compliance in Multiparametric MRI.

BACKGROUND: Prostate cancer, a significant contributor to male cancer mortality globally, demands improved diagnostic strategies. In Saudi Arabia, where the incidence is expected to double, this study assessed the compliance of multiparametric MRI (mpMRI) practices with Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) guidelines across diverse healthcare institutions. METHODS: A survey was distributed to the radiology departments of all tertiary referral hospitals in Saudi Arabia (n=60) to assess their compliance with the technical specifications outlined in PI-RADS v2. Statistical analysis included chi-square, Fisher exact, ANOVA, and Student t-tests to examine the collected data. RESULTS: The study revealed an overall commendable compliance rate of 95.23%. However, significant variations were observed in technical parameters, particularly between 1.5 Tesla and 3 Tesla scanners and tertiary versus non-tertiary hospitals. Notable adherence in certain sequences contrasted with discrepancies in T2-weighted and diffusion-weighted imaging parameters. CONCLUSION: These findings underscore the need for nuanced approaches to optimize prostate imaging protocols, considering field strength and institutional differences. The study contributes to the ongoing refinement of standardized mpMRI practices, aiming to enhance diagnostic accuracy and improve clinical outcomes in prostate cancer.

Multiparametric Prostate MRI Accuracy of Prostate Imaging Reporting and Data System (v2.1) Scores 4 and 5: The Influence of Image Quality According to the Prostate Imaging Quality Score.

Purpose: The accuracy of multiparametric magnetic resonance imaging (mpMRI) heavily relies on image quality, as evidenced by the evolution of the prostate imaging quality (PI-QUAL) scoring system for the evaluation of clinically significant prostate cancer (csPC). This study aims to evaluate the impact of PI-QUAL scores in detecting csPC within PI-RADS 4 and 5 lesions. Methods: We retrospectively selected from our database all mpMRI performed from January 2019 to March 2022. Inclusion criteria were as follows: (1) mpMRI acquired in our institution according to the technical requirements from the PI-RADS (v2.1) guidelines; (2) single lesion scored as PI-RADS (v2.1) 4 or 5; (3) MRI-TBx performed in our institution; (4) complete histology report; and (5) complete clinical record. Results: A total of 257 male patients, mean age 70.42 ± 7.6 years, with a single PI-RADS 4 or 5 lesion undergoing MRI-targeted biopsy, were retrospectively studied. Of these, 61.5% were PI-RADS 4, and 38.5% were PI-RADS 5, with 84% confirming neoplastic cells. In high-quality image lesions (PI-QUAL ≥ 4), all PI-RADS 5 lesions were accurately identified as positive at the final histological examination (100% of CDR). For PI-RADS 4 lesions, 37 (23%) were negative, resulting in a cancer detection rate of 77% (95% CI: 67.51-84.83). Conclusions: The accuracy of mpMRI, independently of the PI-RADS score, progressively decreased according to the decreasing PI-QUAL score. These findings emphasize the crucial role of the PI-QUAL scoring system in evaluating PI-RADS 4 and 5 lesions, influencing mpMRI accuracy.

Automatic text classification of prostate cancer malignancy scores in radiology reports using NLP models.

This paper presents the implementation of two automated text classification systems for prostate cancer findings based on the PI-RADS criteria. Specifically, a traditional machine learning model using XGBoost and a language model-based approach using RoBERTa were employed. The study focused on Spanish-language radiological MRI prostate reports, which has not been explored before. The results demonstrate that the RoBERTa model outperforms the XGBoost model, although both achieve promising results. Furthermore, the best-performing system was integrated into the radiological company's information systems as an API, operating in a real-world environment.

Perspectives on technology: Prostate Imaging-Reporting and Data System (PI-RADS) interobserver variability.

OBJECTIVES: To explore the topic of Prostate Imaging-Reporting and Data System (PI-RADS) interobserver variability, including a discussion of major sources, mitigation approaches, and future directions. METHODS: A narrative review of PI-RADS interobserver variability. RESULTS: PI-RADS was developed in 2012 to set technical standards for prostate magnetic resonance imaging (MRI), reduce interobserver variability at interpretation, and improve diagnostic accuracy in the MRI-directed diagnostic pathway for detection of clinically significant prostate cancer. While PI-RADS has been validated in selected research cohorts with prostate cancer imaging experts, subsequent prospective studies in routine clinical practice demonstrate wide variability in diagnostic performance. Radiologist and biopsy operator experience are the most important contributing drivers of high-quality care among multiple interrelated factors including variability in MRI hardware and technique, image quality, and population and patient-specific factors such as prostate cancer disease prevalence. Iterative improvements in PI-RADS have helped flatten the curve for novice readers and reduce variability. Innovations in image quality reporting, administrative and organisational workflows, and artificial intelligence hold promise in improving variability even further. CONCLUSION: Continued research into PI-RADS is needed to facilitate benchmark creation, reader certification, and independent accreditation, which are systems-level interventions needed to uphold and maintain high-quality prostate MRI across entire populations.

The predictive impact of hematological inflammatory markers in detecting prostate cancer in patients with PI-RADS 3 lesions on multiparametric magnetic resonance imaging.

BACKGROUND: The diagnostic accuracy of suspicious lesions that are classified as PI-RADS 3 in multiparametric prostate magnetic-resonance imaging (mpMRI) is controversial. This study aims to assess the predictive capacity of hematological inflammatory markers such as neutrophil-lymphocyte ratio (NLR), pan-immune-inflammation value (PIV), and systemic immune-response index (SIRI) in detecting prostate cancer in PI-RADS 3 lesions. METHODS: 276 patients who underwent mpMRI and subsequent prostate biopsy after PI-RADS 3 lesion detection were included in the study. According to the biopsy results, the patients were distributed to two groups as prostate cancer (PCa) and no cancer (non-PCa). Data concerning age, PSA, prostate volume, PSA density, PI-RADS 3 lesion size, prostate biopsy results, monocyte counts (109/L), lymphocyte counts (109/L), platelet counts (109/L), neutrophils count (109/L) were recorded from the complete blood count. From these data; PIV value is obtained by monocyte × neutrophil × platelet/lymphocyte, NLR by neutrophil/lymphocyte, and SIRI by monocyte number × NLR. RESULTS: Significant variations in neutrophil, lymphocyte, and monocyte levels between PCa and non-PCa patient groups were detected (p = 0.009, p = 0.001, p = 0.005 respectively, p < 0.05). NLR, PIV, and SIRI exhibited significant differences, with higher values in PCa patients (p = 0.004, p = 0.001, p < 0.001 respectively, p < 0.05). The area under curve of SIRI was 0.729, with a cut-off value of 1.20 and with a sensitivity 57.70%, and a specificity of 68.70%. CONCLUSION: SIRI outperformed NLR and PIV in detecting PCa in PI-RADS 3 lesions, showcasing its potential as a valuable biomarker. Implementation of this parameter to possible future nomograms has the potential to individualize and risk-stratify the patients in prostate biopsy decision.

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.

Implications of unconventional histological subtypes on magnetic resonance imaging and oncological outcomes in patients who have undergone radical prostatectomy.

The prognostic significance of unconventional histology (UH) subtypes including intraductal carcinoma of the prostate (IDC-P), ductal adenocarcinoma, and cribriform pattern has been investigated for prostate cancer (PCa). However, little is known about magnetic resonance imaging (MRI) features and the oncological impact of tumor localization in localized PCa with UH. Clinical data of 211 patients with acinar adenocarcinoma (conventional histology [CH]) and 82 patients with UH who underwent robotic-assisted radical prostatectomy (RARP) were reviewed. Patients with UH are more likely to be older and have higher Gleason grade group, higher Prostate Imaging-Reporting and Data System (PI-RADS) v2.1 score, and larger tumor volume (TV) than those with CH. Multivariate analysis identified the presence of UH as an independent prognostic factor for progression-free survival (PFS) (hazard ration (HR) 2.41, 95% confidence interval (CI) 0.22-0.79, P = 0.0073). No significant difference in PFS was seen regarding tumor localization (transition zone [TZ] or peripheral zone [PZ]) in patients with UH (P = 0.8949), whereas PZ cancer showed shorter PFS in patients with CH (P = 0.0174). PCa with UH was associated with higher progression than PCa with CH among resection margin (RM)-negative cases (P < 0.0001). Further, increased PI-RADS v2.1 score did not correlate with larger TV in UH (P = 0.991), whereas a significant difference in TV was observed in CH (P < 0.0001). The prognostic significance of UH tumor was independent of tumor localization, and shorter PFS was observed even in RM-negative cases, indicating an aggressive subtype with micro-metastatic potential. Furthermore, UH tumors are more likely to harbor a large TV despite PI-RADS v2.1 score ≤ 3. These findings will help optimal perioperative management for PCa with UH.

Strategies for improving image quality in prostate MRI.

Prostate magnetic resonance imaging (MRI) stands as the cornerstone in diagnosing prostate cancer (PCa), offering superior detection capabilities while minimizing unnecessary biopsies. Despite its critical role, global disparities in MRI diagnostic performance persist, stemming from variations in image quality and radiologist expertise. This manuscript reviews the challenges and strategies for enhancing image quality in prostate MRI, spanning patient preparation, MRI unit optimization, and radiology team engagement. Quality assurance (QA) and quality control (QC) processes are pivotal, emphasizing standardized protocols, meticulous patient evaluation, MRI unit workflow, and radiology team performance. Additionally, artificial intelligence (AI) advancements offer promising avenues for improving image quality and reducing acquisition times. The Prostate-Imaging Quality (PI-QUAL) scoring system emerges as a valuable tool for assessing MRI image quality. A comprehensive approach addressing technical, procedural, and interpretative aspects is essential to ensure consistent and reliable prostate MRI outcomes.

The importance and future of prostate MRI report templates: improving oncological care.

The radiologist's report is crucial for guiding care post-imaging, with ongoing advancements in report construction. Recent studies across various modalities and organ systems demonstrate enhanced clarity and communication through structured reports. This article will explain the benefits of disease-state specific reporting templates using prostate MRI as the model system. We identify key reporting components for prostate cancer detection and staging as well as imaging in active surveillance and following therapy. We discuss relevant reporting systems including PI-QUAL, PI-RADS, PRECISE, PI-RR and PI-FAB systems. Additionally, we examine optimal reporting structure including disruptive technologies such as graphical reporting and using artificial intelligence to improve report clarity and applicability.

Patient Perceptions of Standardized Risk Language Used in ACR Prostate MRI PI-RADS Scores.

INTRODUCTION: Prostate MRI reports use standardized language to describe risk of clinically significant prostate cancer (csPCa) from "equivocal" (Prostate Imaging Reporting and Data System [PI-RADS] 3), "likely" (PI-RADS 4), to "highly likely" (PI-RADS 5). These terms correspond to risks of 11%, 37%, and 70% according to American Urological Association guidelines, respectively. We assessed how men perceive risk associated with standardized PI-RADS language. METHODOLOGY: We conducted a crowdsourced survey of 1,204 men matching a US prostate cancer demographic. We queried participants' risk perception associated with standardized PI-RADS language across increasing contexts: words only, PI-RADS sentence, full report, and full report with numeric estimate. Median perceived risk (interquartile range) and absolute under/overestimation compared with American Urological Association standards were reported. Multivariable linear mixed-effects analysis identified factors associated with accuracy of risk perception. RESULTS: Median perceived risks of csPCa (interquartile range) for the word-only context were "equivocal" 50% (50%-74%), "likely" 75% (68%-85%), and "highly likely" 87% (78%-92%), corresponding to +39%, +38%, and +17% overestimation, respectively. Median perceived risks for the PI-RADS-sentence context were 50% (50%-50%), 75% (68%-81%), and 90% (80%-94%) for PI-RADS 3, 4, and 5, corresponding to +39%, +38%, and +20% overestimation, respectively. Median perceived risks for the full-report context were 50% (35%-70%), 72% (50%-80%), and 84% (54%-91%) for PI-RADS 3, 4, and 5, corresponding to +39%, +35%, and +14% overestimation, respectively. For the full-report-with-numeric-estimate context describing a PI-RADS 4 lesion, median perceived risk was 70% (50%-%80), corresponding to +33% overestimation. Including numeric estimates increased correct perception of risk from 3% to 11% (P < .001), driven by men with higher numeracy (odds ratio 1.24, P = .04). CONCLUSION: Men overestimate risk of csPCa associated with standardized PI-RADS language regardless of context, especially for PI-RADS 3 and 4 lesions. Changes to PI-RADS language or data-sharing policies for imaging reports should be considered.

Effect of Bacillus Calmette-Guerin Exposure on Prostate Cancer Detection Using Magnetic Resonance Imaging: A Cohort Study.

BACKGROUND: Granulomatous prostatitis is a medical condition that may mimic prostate cancer. PURPOSE: Granulomatous prostatitis resulting from BCG-exposure can confound the diagnosis of prostate cancer based on prostate imaging and data system (PI-RADS) classification observed on multiparametric prostate magnetic resonance imaging (mpMRI). STUDY TYPE, POPULATION, ASSESSMENT AND STATISTICAL TESTS: A cohort study was conducted, enrolling consecutive males at risk for prostate cancer who underwent an mpMRI-targeted prostate biopsy between February 2016 and August 2023. The focus of the study was on prior BCG-exposure as adjuvant treatment for non-muscle-invasive urothelial carcinoma within the 3 years prior the magnetic resonance imaging (MRI). Exclusion criteria were a prior androgen deprivation therapy, prostate surgery or radiation, and BCG-exposure occurring more than 3 years and less than 3 months before the MRI. Chi-square, logistic-regression, statistical association, and homogeneity tests were used. RESULTS: Total 712 patients, 899 biopsied lesions (218 PI-RADS 3, 521 PI-RADS 4 and 160 PI-RADS 5) and 20 patients with 30 lesions within the BCG-exposed cohort. Chi-square and logistic-regression tests showed an association between PI-RADS with malignancy and significant tumor (ST), considering PI-RADS3 as the reference (OR: 4.9 [95% CI, 3.4-7.1] for PI-RADS4 and OR: 21.7 [95% CI, 12.4-37.8] for PI-RADS5 for malignancy, and OR: 5.3 [95% CI, 3.2-8.7] for PI-RADS4 and OR: 16.5 [95% CI, 9.4-28.9] for PI-RADS5 regarding ST). A statistically significant negative association was demonstrated between malignancy and ST with respect to BCG-exposure (OR: 0.15 [95% CI, 0.06-0.39] and OR: 0.39 [95% CI, 0.15-1.0], respectively). Statistically significant risk-difference for malignancy in patients nonexposed to BCG regarding those exposed was 45% (61.6% vs. 16.7%) for PI-RADS4, and 68.5% (90.7% vs. 22.2%) and 42.7% (64.9% vs. 22.2%) concerning malignancy and ST for PI-RADS5, respectively. DATA CONCLUSIONS: Granulomatous prostate reaction caused by BCG-exposure acts as confounding factor for prostate MRI interpretation. The risk of malignancy and significant tumor on targeted biopsy to PI-RADS 3, 4 and 5 is notably lower in exposed patients.

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.