Prostate Imaging Reporting And Data System Scoring Research Articles

Multi-Parametric Evaluation of Prostate Cancer on MRI

Background: Prostate cancer is the most common malignancy among men globally and the second leading cause of cancer-related deaths. The increasing prevalence, particularly in aging populations, underscores the urgent need for accurate diagnostic tools to facilitate early detection, precise staging, and effective management. Traditional diagnostic methods, including Digital Rectal Examination (DRE) and Prostate-Specific Antigen (PSA) testing, have limitations in sensitivity and specificity, leading to unnecessary procedures and missed diagnoses. Objective: This study aims to evaluate the efficacy of multiparametric Magnetic Resonance Imaging (mpMRI) in the diagnosis and staging of prostate cancer, comparing its accuracy with conventional diagnostic methods and examining its role in improving patient outcomes. Methods: A retrospective descriptive cross-sectional study was conducted at the Diagnostic Center of Combined Military Hospital, Lahore, from January 2021 to June 2023. Sixty male patients suspected of having prostate cancer, based on elevated PSA levels (>10 ng/mL) or clinical symptoms, were included. mpMRI scans were performed using a Siemens Avanto MRI Machine 1.5T, incorporating T2-weighted imaging, Diffusion-Weighted Imaging (DWI), and Dynamic Contrast-Enhanced (DCE) MRI. The Prostate Imaging Reporting and Data System (PI-RADS) was employed for interpretation. Statistical analysis was conducted using SPSS 26.0, with chi-square tests applied to assess the association between mpMRI findings and prostate cancer diagnosis. Results: mpMRI identified prostate cancer with a sensitivity of 85% and specificity of 88.3%, as evidenced by abnormal MRI findings in 53 out of 60 patients. Elevated PSA levels were confirmed in 90% of the cases. T2-weighted imaging demonstrated hypointensity in the peripheral zone in 70% of patients, while DWI and ADC mapping showed restricted diffusion in 85% of the cases. DCE-MRI revealed post-contrast enhancement in 40% of the participants. The PI-RADS scoring system effectively graded the severity of prostate lesions, with 38.3% of cases categorized as PI-RADS IV. Conclusion: Multiparametric MRI significantly enhances the accuracy of prostate cancer diagnosis and staging, surpassing traditional diagnostic methods in sensitivity and specificity. By providing detailed anatomical and functional information, mpMRI facilitates early detection, accurate localization, and appropriate management of prostate cancer, potentially improving patient outcomes.

Exploration of the diagnostic capacity of PSAMR combined with PI-RADS scoring for clinically significant prostate cancer and establishment and validation of the Nomogram prediction model.

The objective of this investigation was to explore the diagnostic capability of Prostate Specific Antigen Mass Ratio (PSAMR) combined with Prostate Imaging Reporting and Data System (PI-RADS) scoring for clinically significant prostate cancer (CSPC), develop and validate a Nomogram prediction model for the probability of prostate cancer occurrence in patients who have not undergone prostate biopsy. Initially, we retrospectively collected clinical and pathological data of patients who underwent trans-perineal prostate puncture at Yijishan Hospital of Wanan Medical College from July 2021 to January 2023. Through logistic univariate and multivariate regression analysis, independent risk factors for CSPC were determined. Receiver Operating Characteristic (ROC) curves were generated to compare the ability of different factors for diagnosis of CSPC. Then, we split the dataset into a training set and validation set, compared their heterogeneity, and developed a Nomogram prediction model based on the training set. Finally, we validated the Nomogram prediction model in terms of discrimination, calibration, and clinical usefulness. Logistic multivariate regression analysis illustrated that age [64-69 (OR = 2.736, P = 0.029); 69-75 (OR = 4.728, P = 0.001); > 75 (OR = 11.344, P < 0.001)], PSAMR [0.44-0.73 (OR = 4.144, P = 0.028); 0.73-1.64(OR = 13.022, P < 0.001); > 1.64(OR = 50.541, P < 0.001)], and PI-RADS score [4 points (OR = 7.780, P < 0.001); 5 points (OR = 24.533, P < 0.001)] were independent risk factors for CSPC. The Area Under the Curve (AUC) of the ROC curves of PSA, PSAMR, PI-RADS score, and PSAMR combined with PI-RADS score were respectively 0.797, 0.874, 0.889, and 0.928. The performance of PSAMR and PI-RADS score for diagnosis of CSPC was superior to PSA, but inferior to PSAMR combined with PI-RADS. Age, PSAMR, and PI-RADS were included in the Nomogram prediction model. The AUCs of the training set ROC curve and the validation set ROC curve were 0.943 (95% CI 0.917-0.970) and 0.878 (95% CI 0.816-0.940), respectively, in the discrimination validation. The calibration curve showed good consistency, and the decision analysis curve suggested the model had good clinical efficacy. We found that PSAMR combined with PI-RADS scoring had a strong diagnostic capability for CSPC, and provided a Nomogram prediction model to predict the probability of prostate cancer occurrence combined with clinical data.

The combination of prostate MRI PI-RADS scoring system and a genomic classifier is associated with pelvic lymph node metastasis at the time of radical prostatectomy.

Pelvic lymph node metastasis (PLNM) at the time of radical prostatectomy (RP) portends an unfavorable prognosis in prostate cancer patients. Conventional and advanced imaging remains limited in its ability to detect PLNM. We sought to evaluate the combination of a genomic classifier Decipher with Prostate Imaging Reporting and Data System (PI-RADS) scores in improving the detection of PLNM. A retrospective review was performed of patients whom underwent RP, Decipher analysis, and pre-operative prostate MRI. Categorical variables were compared using Pearson chi-squareχ2 tests. Quantitative variables were assessed with Wilcoxon rank-sum tests. Multivariable logistic regression was used to identify predictors of PLNM on final pathology. In total, 202 patients were included in the analysis, 23 of whom (11%) had PLNM. Patients with PLNM had higher median Decipher scores (0.73) than those without PLNM (0.61; p = 0.003). Patients with PLNM were more likely to demonstrate PI-RADS scores ≥ 4 (96%) than those without PLNM (74%; p = 0.012). Logistic regression demonstrated an interaction between Decipher score with PI-RADS score ≥4 (OR = 20.41; 95% CI, 2.10-198.74; p = 0.009) The combination demonstrated an area under the curve (AUC) of 0.73 (95% CI, 0.63-0.82; p < 0.001) for predicting PLNM. The combination of elevated Decipher genomic score (≥ 0.6) and clinically significant PI-RADS score (≥ 4) is associated with PLNM at the time of RP in a modern high-risk cohort of patients with PCaprostate cancer. Prostate MRI and genomic testing may help identify patients with adverse pathology.

Performance variability of radiomics machine learning models for the detection of clinically significant prostate cancer in heterogeneous MRI datasets.

Radiomics promises to enhance the discriminative performance for clinically significant prostate cancer (csPCa), but still lacks validation in real-life scenarios. This study investigates the classification performance and robustness of machine learning radiomics models in heterogeneous MRI datasets to characterize suspicious prostate lesions for non-invasive prediction of prostate cancer (PCa) aggressiveness compared to conventional imaging biomarkers. A total of 142 patients with clinical suspicion of PCa underwent 1.5T or 3T biparametric MRI (7 scanner types, 14 institutions) and exhibited suspicious lesions [prostate Imaging Reporting and Data System (PI-RADS) score ≥3] in peripheral or transitional zones. Whole-gland and index-lesion segmentations were performed semi-automatically. A total of 1,482 quantitative morphologic, shape, texture, and intensity-based radiomics features were extracted from T2-weighted and apparent diffusion coefficient (ADC)-images and assessed using random forest and logistic regression models. Five-fold cross-validation performance in terms of area under the ROC curve was compared to mean ADC (mADC), PI-RADS and prostate-specific antigen density (PSAD). Bias mitigation techniques targeting the high-dimensional feature space and inherent class imbalance were applied and robustness of results was systematically evaluated. Trained models showed mean area under the curves (AUCs) ranging from 0.78 to 0.83 in csPCa classification. Despite using mitigation techniques, high performance variability of results could be demonstrated. Trained models achieved on average numerically higher classification performance compared to clinical parameters PI-RADS (AUC =0.78), mADC (AUC =0.71) and PSAD (AUC =0.63). Radiomics models' classification performance of csPCa was numerically but not significantly higher than PI-RADS scoring. Overall, clinical applicability in heterogeneous MRI datasets is limited because of high variability of results. Performance variability, robustness and reproducibility of radiomics-based measures should be addressed more transparently in future research to enable broad clinical application.

PIRADS for Rad Oncs: A Multidisciplinary Educational Lecture to Improve Prostate MRI Interpretation for Radiation Oncology Residents

Background The Prostate Imaging Reporting and Data System (PIRADS) reported on multiparametric magnetic resonance imaging (mpMRI) is a standard assessment in the diagnostic workup for patients with prostate adenocarcinoma. Therapeutically, mpMRI and PIRADS lesions are increasingly utilized in radiation oncology target delineation for definitive treatment with either external beam radiation therapy or brachytherapy. Though imaging interpretation is an integral component of radiation oncology, formal education is limited. Here we report on an educational lecture created with radiology colleagues to increase knowledge and confidence in mpMRI and PIRADS. Methods An educational lecture was created to summarize the aims and limitations of the PIRADS version 2.1 and to review the relationship of mpMRI, PIRADS scoring, and the dominant intraprostatic lesion (DIL). Educational materials were reviewed by a radiology fellow specialized in body MRI and two faculty radiation oncologists; a radiology fellow also attended the lecture. Radiation oncology residents from three institutions participated in the lecture and completed a pre-lecture (pre-test) and post-lecture (post-test) survey. The surveys assessed the participants' confidence and knowledge in PIRADS and mpMRI. Confidence was assessed using Likert scales ranging from 1-5. The knowledge assessment portion consisted of true/false, dichotomous choice, and multiple choice questions. Statistical Analysis: Likert confidence ratings pre- and post-lecture were averaged separately for each participant. A Wilcoxon Signed Rank test was performed to compare average confidence ratings. A paired sample t-test was performed to compare pre-lecture and post-lecture raw scores of the knowledge assessment section. All statistical analysis was performed using IBM SPSS Statistics for Mac, Version 28.0. (Armonk, NY: IBM Corp). Results 13 participants completed a pre-test survey and attended the lecture. 11/13 (85%) of participants completed a post-test survey. The median pre-test confidence score was 1.6 (Range 1-4), indicating that participants did not feel confident in PIRADS interpretation and mpMRI. The median knowledge assessment pre-test score was 58.3% and significantly improved by 43% after the lecture, with a median post-test score of 83.3% (Range 50%-100%, p<0.001). Participant median confidence score increased to 4.1 (Range 2-5, p=0.003) suggesting increased confidence in interpreting PIRADS and mpMRI. Discussion Radiation oncology residents do not feel confident in PIRADS scoring and imaging interpretation on mpMRI. Formal educational lectures can improve resident comfort and knowledge in PIRADS, and multidisciplinary lectures in collaboration with radiology colleagues should be encouraged. The Prostate Imaging Reporting and Data System (PIRADS) reported on multiparametric magnetic resonance imaging (mpMRI) is a standard assessment in the diagnostic workup for patients with prostate adenocarcinoma. Therapeutically, mpMRI and PIRADS lesions are increasingly utilized in radiation oncology target delineation for definitive treatment with either external beam radiation therapy or brachytherapy. Though imaging interpretation is an integral component of radiation oncology, formal education is limited. Here we report on an educational lecture created with radiology colleagues to increase knowledge and confidence in mpMRI and PIRADS. An educational lecture was created to summarize the aims and limitations of the PIRADS version 2.1 and to review the relationship of mpMRI, PIRADS scoring, and the dominant intraprostatic lesion (DIL). Educational materials were reviewed by a radiology fellow specialized in body MRI and two faculty radiation oncologists; a radiology fellow also attended the lecture. Radiation oncology residents from three institutions participated in the lecture and completed a pre-lecture (pre-test) and post-lecture (post-test) survey. The surveys assessed the participants' confidence and knowledge in PIRADS and mpMRI. Confidence was assessed using Likert scales ranging from 1-5. The knowledge assessment portion consisted of true/false, dichotomous choice, and multiple choice questions. Statistical Analysis: Likert confidence ratings pre- and post-lecture were averaged separately for each participant. A Wilcoxon Signed Rank test was performed to compare average confidence ratings. A paired sample t-test was performed to compare pre-lecture and post-lecture raw scores of the knowledge assessment section. All statistical analysis was performed using IBM SPSS Statistics for Mac, Version 28.0. (Armonk, NY: IBM Corp). 13 participants completed a pre-test survey and attended the lecture. 11/13 (85%) of participants completed a post-test survey. The median pre-test confidence score was 1.6 (Range 1-4), indicating that participants did not feel confident in PIRADS interpretation and mpMRI. The median knowledge assessment pre-test score was 58.3% and significantly improved by 43% after the lecture, with a median post-test score of 83.3% (Range 50%-100%, p<0.001). Participant median confidence score increased to 4.1 (Range 2-5, p=0.003) suggesting increased confidence in interpreting PIRADS and mpMRI. Radiation oncology residents do not feel confident in PIRADS scoring and imaging interpretation on mpMRI. Formal educational lectures can improve resident comfort and knowledge in PIRADS, and multidisciplinary lectures in collaboration with radiology colleagues should be encouraged.

Deep learning-based PI-RADS score estimation to detect prostate cancer using multiparametric magnetic resonance imaging

Prostate cancer (PCa) is the most common type of cancer among men. Digital rectal examination and prostate-specific antigen (PSA) tests are used to diagnose the PCa accurately. Since PSA is organ-specific and not disease-specific, multiparametric magnetic resonance imaging (mpMRI) is used to reduce unnecessary biopsies. Prostate imaging reporting and data system (PI-RADS) is widely used for mpMRI scoring to detect PCa. There is low-level agreement among interpreters and also subjectivity associated with PI-RADS scoring. Hence, in this study, a hybrid model has been proposed to accurately interpret mpMRI examination and predict PI-RADS scores. In the proposed systems, feature maps of mpMR images were extracted using the MobilenetV2, Efficientnetb0, and Darknet53 architectures. Then, the feature maps obtained using these three architectures were combined. The merged feature maps are subjected to neighborhood components analysis (NCA) to eliminate redundant features. The proposed system provided 96.09% accuracy.

A Validation Study of the Deep-Learning-Based Prostate Imaging Reporting and Data System Scoring Algorithm

Purpose: The Prostate Imaging Reporting and Data System (PI-RADS) was introduced to standardize prostate cancer diagnosis by MRI. However, the inter-reader agreement by PI-RADS scoring is not always high. The purpose of this study was to validate a deep-learning-based diagnostic algorithm of PI-RADS. Methods: We applied a Siemens Healthineers Prostate Artificial Intelligence (AI) prototype (work in progress) for fully automated prostate lesion detection, classification and reporting. More than 2000 bi-parametric MRI studies along with the PI-RADS reports were included as training, validation, and test data. This prospective validation study includes 101 consecutive patients suspected of prostate cancer, and 100 patients were included in the analysis. All subjects underwent a noncontrast-enhanced bi-parametric MRI including T2-weighted and diffusion-weighted imaging. Two board-certified radiologists independently scored the PI-RADS, and if there were disagreements; another radiologist confirmed the diagnosis. We compared the AI results with the interpretation results by the radiologists. Results: The sensitivity of our AI model for PI-RADS ≥ 4 was 0.76, and the specificity was 0.76. For the cases with PI-RADS ≥ 3, the sensitivity was 0.69, and the specificity was 0.76. In the lesion-based analysis, AI detection rates of PI-RADS 3, 4, 5 lesions in the peripheral zone were 43%, 63%, and 100%, respectively. In the transition zone, AI detection rates of PI-RADS 3, 4, 5 were 30%, 54%, and 100%, respectively. Conclusion: Our deep-learning-based algorithm has been validated and shown to help score PI-RADS.

The Relationship between PI-RADS Categories and Incidental Findings in Multiparametric Prostate MRI.

To find the frequency and significance of extraprostatic incidental findings (ep-IFs) during multiparametric-magnetic prostate resonance imaging (mp-MRI), and compare them with prostate imaging reporting and data system (PI-RADS) outcomes. Analytical study. Place & Duration of Study: Goztepe Education and Research Hospital, Istanbul, Turkey, from June 2019 to January 2020. Images of 185 men, who underwent mp-MRI, were reviewed and ep-IFs were also classified as urologic or non-urologic and benign or malign. The PI-RADS score was also recorded in biopsy-naïve subjects or in whom a sufficient time (which would not impair imaging) was elapsed after the biopsy. The cases were also divided into two groups, according to the PI-RADS score (Group 1: PI-RADS 1 or 2, Group 2: PI-RADS 3 or more) and the incidental findings (IFs) were compared between the groups. Overall, 139 ep-IFs were detected in 88 (47.6%) patients. The remaining 97 (52.4%) cases were free of ep-IFs. The ep-IFs were benign in 85 (96.6%) and malignant in 3 (3.4%) cases. The frequency of total ep-IFs did not differ between groups 1 and 2 (47.8% vs. 47.6%, respectively, p>0.05). Extra prostatic incitental findings are frequently encountered during mp-MRI, benign ep-IFs are quite frequent; although rare malignant ep-IFs may be subject to being missed due to focused analysis and interpretation of prostate. PI-RADS scoring system does not contribute to the diagnosis of incidental mp-MRI. Key Words: Incidental findings, Multiparametric prostate MRI, Prostate cancer, MP-MRI.

An integrated nomogram combining deep learning, Prostate Imaging–Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study

SummaryBackgroundBiparametric MRI (comprising T2-weighted MRI and apparent diffusion coefficient maps) is increasingly being used to characterise prostate cancer. Although previous studies have combined Prostate Imaging–Reporting & Data System (PI-RADS)-based MRI findings with routinely available clinical variables and with deep learning-based imaging predictors, respectively, for prostate cancer risk stratification, none have combined all three. We aimed to construct an integrated nomogram (referred to as ClaD) combining deep learning-based imaging predictions, PI-RADS scoring, and clinical variables to identify clinically significant prostate cancer on biparametric MRI.MethodsIn this retrospective multicentre study, we included patients with prostate cancer, with histopathology or biopsy reports and a screening or diagnostic MRI scan in the axial view, from four cohorts in the USA (from University Hospitals Cleveland Medical Center, Icahn School of Medicine at Mount Sinai, Cleveland Clinic, and Long Island Jewish Medical Center) and from the PROSTATEx Challenge dataset in the Netherlands. We constructed an integrated nomogram combining deep learning, PI-RADS score, and clinical variables (prostate-specific antigen, prostate volume, and lesion volume) using multivariable logistic regression to identify clinically significant prostate cancer on biparametric MRI. We used data from the first three cohorts to train the nomogram and data from the remaining two cohorts for independent validation. We compared the performance of our ClaD integrated nomogram with that of integrated nomograms combining clinical variables with either the deep learning-based imaging predictor (referred to as DIN) or PI-RADS score (referred to as PIN) using area under the receiver operating characteristic curves (AUCs). We also compared the ability of the nomograms to predict biochemical recurrence on a subset of patients who had undergone radical prostatectomy. We report cross-validation AUCs as means for the training set and used AUCs with 95% CIs to assess the performance on the test set. The difference in AUCs between the models were tested for statistical significance using DeLong’s test. We used log-rank tests and Kaplan-Meier curves to analyse survival.FindingsWe investigated 592 patients (823 lesions) with prostate cancer who underwent 3T multiparametric MRI at five hospitals in the USA between Jan 8, 2009, and June 3, 2017. The training data set consisted of 368 patients from three sites (the PROSTATEx Challenge cohort [n=204], University Hospitals Cleveland Medical Center [n=126], and Icahn School of Medicine at Mount Sinai [n=38]), and the independent validation data set consisted of 224 patients from two sites (Cleveland Clinic [n=151] and Long Island Jewish Medical Center [n=73]). The ClaD clinical nomogram yielded an AUC of 0·81 (95% CI 0·76–0·85) for identification of clinically significant prostate cancer in the validation data set, significantly improving performance over the DIN (0·74 [95% CI 0·69–0·80], p=0·0005) and PIN (0·76 [0·71–0·81], p<0·0001) nomograms. In the subset of patients who had undergone radical prostatectomy (n=81), the ClaD clinical nomogram resulted in a significant separation in Kaplan-Meier survival curves between patients with and without biochemical recurrence (HR 5·92 [2·34–15·00], p=0·044), whereas the DIN (1·22 [0·54–2·79], p=0·65) and PIN nomograms did not (1·30 [0·62–2·71], p=0·51).InterpretationRisk stratification of patients with prostate cancer using the integrated ClaD nomogram could help to identify patients with prostate cancer who are at low risk, very low risk, and favourable intermediate risk, who might be candidates for active surveillance, and could also help to identify patients with lethal prostate cancer who might benefit from adjuvant therapy.FundingNational Cancer Institute of the US National Institutes of Health, National Institute for Biomedical Imaging and Bioengineering, National Center for Research Resources, US Department of Veterans Affairs Biomedical Laboratory Research and Development Service, US Department of Defense, US National Institute of Diabetes and Digestive and Kidney Diseases, The Ohio Third Frontier Technology Validation Fund, Case Western Reserve University, Dana Foundation, and Clinical and Translational Science Collaborative.

Semi-automated PIRADS scoring via mpMRI analysis.

Purpose: Prostate cancer (PCa) is the most common solid organ cancer and second leading cause of death in men. Multiparametric magnetic resonance imaging (mpMRI) enables detection of the most aggressive, clinically significant PCa (csPCa) tumors that require further treatment. A suspicious region of interest (ROI) detected on mpMRI is now assigned a Prostate Imaging-Reporting and Data System (PIRADS) score to standardize interpretation of mpMRI for PCa detection. However, there is significant inter-reader variability among radiologists in PIRADS score assignment and a minimal input semi-automated artificial intelligence (AI) system is proposed to harmonize PIRADS scores with mpMRI data. Approach: The proposed deep learning model (the seed point model) uses a simulated single-click seed point as input to annotate the lesion on mpMRI. This approach is in contrast to typical medical AI-based approaches that require annotation of the complete lesion. The mpMRI data from 617 patients used in this study were prospectively collected at a major tertiary U.S. medical center. The model was trained and validated to classify whether an mpMRI image had a lesion with a PIRADS score greater than or equal to PIRADS 4. Results: The model yielded an average receiver-operator characteristic (ROC) area under the curve (ROC-AUC) of 0.704 over a 10-fold cross-validation, which is significantly higher than the previously published benchmark. Conclusions: The proposed model could aid in PIRADS scoring of mpMRI, providing second reads to promote quality as well as offering expertise in environments that lack a radiologist with training in prostate mpMRI interpretation. The model could help identify tumors with a higher PIRADS for better clinical management and treatment of PCa patients at an early stage.

The Value of PSA Density in Combination with PI-RADS™ for the Accuracy of Prostate Cancer Prediction

Multiparametric magnetic resonance imaging has an emerging role in prostate cancer diagnostics. In addition, clinical information is a reliable predictor of significant prostate cancer. We analyzed whether the negative predictive value of multiparametric magnetic resonance imaging to rule out significant prostate cancer could be improved using clinical factors, especially prostate specific antigen density. A total of 1,040 consecutive men with suspicion of prostate cancer underwent multiparametric magnetic resonance imaging first, followed by transperineal systematic and magnetic resonance imaging-transrectal ultrasound fusion guided biopsy. Logistic regression analyses were performed to test different clinical factors as predictors of significant prostate cancer and build nomograms. To simplify these nomograms for clinical use patients were stratified into 3 prostate specific antigen density groups, including group 1-less than 0.07, group 2-0.07 to 0.15 and group 3-greater than 0.15ng/ml/ml. After stratification we calculated the negative predictive value of a PI-RADS (Prostate Imaging Reporting and Data System) Likert score of less than 3. Significant prostate cancer was defined as a Gleason score of 3 + 4 or greater. High grade prostate cancer was defined as a Gleason score of 4 + 3 or greater. Overall 451 men were diagnosed with significant prostate cancer, including 187 with a Gleason score of 4 + 3 or greater. On ROC curve analyses the predictive power of the developed nomogram for significant prostate cancer showed a higher AUC than that of PI-RADS alone (0.79 vs 0.75, p <0.001). The negative predictive value of harboring significant prostate cancer increased in men with unsuspicious magnetic resonance imaging from 79% up to 89% when prostate specific antigen density was 0.15 ng/ml/ml or less. In the repeat biopsy setting the negative predictive value of significant prostate cancer increased from 83% to 93%. The negative predictive value to harbor high grade prostate cancer increased from 92% up to 98% in the entire cohort. Using prostate specific antigen density combined with multiparametric magnetic resonance imaging improved the negative predictive value of PI-RADS scoring. By increasing the probability of ruling out significant prostate cancer approximately 20% of unnecessary biopsies could be avoided safely.

Multiparametric magnetic resonance imaging-transrectal ultrasound fusion prostate biopsy: A prospective, single centre study.

Introduction:Transrectal rectal ultrasound (TRUS)-guided systematic biopsy is the gold standard for diagnosis of prostate cancer. However, systematic biopsy has high false-negative rate and often misses anteriorly located tumors. Magnetic resonance imaging (MRI)-TRUS fusion biopsy can potentially improve cancer detection by better visualization and targeting of cancer focus. We evaluated the role of fusion biopsy in detection of prostate cancer and the association of prostate imaging reporting and data system (PI-RADS) score for predicting cancer risk and its aggression.Methods:Ninety-six consecutive men with suspected prostate cancer underwent MRI-TRUS fusion-targeted biopsy of suspicious lesions and standard 12 core biopsy from May 2014 to July 2015 in our institution. All patients underwent 3.0 T multiparametric MRI before biopsy. mp-MRI included T2W, DWI, DCE and MRS sequences to identify lesions suspicious for prostate cancer. Suspected lesions were scored according to PI-RADS scoring system. Comparison of cancer detection between standard 12 core biopsy and MRI-TRUS fusion biopsy was done. Detection of prostate cancer was primary end point of this study.Results:Mean age was 64.4 years and median prostate-specific antigen was 8.6 ng/ml. Prostate cancer was detected in 57 patients (59.3%). Of these 57 patients, 8 patients (14%) were detected by standard 12 core biopsy only, 7 patients (12.3%) with MRI-TRUS fusion biopsy only, and 42 patients (73.7%) by both techniques. Of the 7 patients, detected with MRI-TRUS fusion biopsy alone, 6 patients (85.7%) had Gleason ≥7 disease. Prostate cancer was detected on either standard 12 core biopsy or MRI-TRUS fusion biopsy in 0%, 42.8%, 74%, and 89.3% patients of suspicious lesions of highest PI-RADS score 2, 3, 4, and 5, respectively.Conclusions:MRI-TRUS fusion prostate biopsy improves cancer detection rate when combined with standard 12 cores biopsy and detects more intermediate or high-grade prostate cancer (Gleason ≥7). With increasing PI-RADS score, there is an increase chance of detection of cancer as well as its aggressiveness.

Prostate Cancer Detection with Multiparametric Magnetic Resonance Imaging: Prostate Imaging Reporting and Data System Version 1 versus Version 2.

Background:Prostate Imaging Reporting and Data System (PI-RADS) is a globally acceptable standardization for multiparametric magnetic resonance imaging (mp-MRI) in prostate cancer (PCa) diagnosis. The American College of Radiology revised the PI-RADS to address the limitations of version 1 in December 2014. This study aimed to determine whether the PI-RADS version 2 (PI-RADS v2) scoring system improves the diagnostic accuracy of mp-MRI of the prostate compared with PI-RADS v1.Methods:This retrospective study was approved by the institutional review board. A total of 401 consecutive patients, with clinically suspicious PCa undergoing 3.0 T mp-MRI (T2-weighted imaging + diffusion-weighted imaging + DCE) before transrectal ultrasound-guided biopsy between June 2013 and July 2015, were included in the study. All patients were scored using the 5-point PI-RADS scoring system based on either PI-RADS v1 or v2. Receiver operating characteristics were calculated for statistical analysis. Sensitivity, specificity, and diagnostic accuracy were compared using McNemar's test.Results:PCa was present in 150 of 401 (37.41%) patients. When we pooled data from both peripheral zone (PZ) and transition zone (TZ), the areas under the curve were 0.889 for PI-RADS v1 and 0.942 for v2 (P = 0.0001). Maximal accuracy was achieved with a score threshold of 4. At this threshold, in the PZ, similar sensitivity, specificity, and accuracy were achieved with v1 and v2 (all P > 0.05). In the TZ, sensitivity was higher for v2 than for v1 (96.36% vs. 76.36%, P = 0.003), specificity was similar for v2 and v1 (90.24% vs. 84.15%, P = 0.227), and accuracy was higher for v2 than for v1 (92.70% vs. 81.02%, P = 0.002).Conclusions:Both v1 and v2 showed good diagnostic performance for the detection of PCa. However, in the TZ, the performance was better with v2 than with v1.

Central zone lesions on magnetic resonance imaging: Should we be concerned?

Introduction and objectiveThe Prostate Imaging Reporting and Data System (PI-RADS) score was developed to evaluate lesions in the peripheral and transition zone on multiparametric magnetic resonance imaging (mpMRI) of the prostate. We aim to determine if the PI-RADS scoring system can be used to evaluate central zone lesions on mpMRI. Materials and methodsA retrospective review of 73 patients who underwent mpMRI/ultrasound (US) fusion-guided biopsy of 143 suspicious lesions between February 2014 and October 2015 was performed. All patients underwent a 3T mpMRI. Indications for mpMRI included an abnormal digital rectal examination, PSA velocity >0.75ng/dl/y, and patients on active surveillance. The mpMRI sequence involved T2-weighted imaging, diffusion-weighted imaging, and dynamic contrast enhancement. Using 3-dimensional model software (Invivo Corporation, Gainesville, FL, USA), a minimum of 3 magnetic resonance imaging (MRI)/US fusion-guided biopsy samples were taken from each prostate lesion seen on mpMRI irrespective of PI-RADS score, using local anesthesia in an outpatient clinic setting. ResultsA total of 73 patients underwent MRI/US fusion-guided biopsy of 85 peripheral zone lesions, 31 transitional zone lesions, and 27 central zone lesions. Only 2 (7%) of central zone lesions were positive for prostate cancer. Both patients had lesions which were graded as PI-RADS 3. Both the patients had multifocal lesions that encompassed≥50% of the central and transition zones on the sagittal view MRI images. Both patients previously had transrectal US-guided biopsy of the prostate which was negative for cancer. Both patients underwent a robotic-assisted laparoscopic prostatectomy, each revealing high-grade cancer. ConclusionsLesions involving only the central gland/zone seen on MRI are less concerning for malignancy and should not be given equal weight as peripheral zone lesions. In this series, no lesions involving solely the central gland/zone, regardless of PI-RADS score, was positive for malignancy on MRI/US fusion-guided biopsy. Consideration of a modified PI-RADS scoring system should be given to help identify central zone lesions with malignant potential.

Combination of prostate imaging reporting and data system (PI-RADS) score and prostate-specific antigen (PSA) density predicts biopsy outcome in prostate biopsy naïve patients.

To assess the value of the Prostate Imaging Reporting and Data System (PI-RADS) scoring system, for prostate multi-parametric magnetic resonance imaging (mpMRI) to detect prostate cancer, and classical parameters, such as prostate-specific antigen (PSA) level, prostate volume and PSA density, for predicting biopsy outcome in biopsy naïve patients who have suspected prostate cancer. Patients who underwent mpMRI at our hospital, and who had their first prostate biopsy between July 2010 and April 2014, were analysed retrospectively. The prostate biopsies were taken transperineally under transrectal ultrasonography guidance. In all, 14 cores were biopsied as a systematic biopsy in all patients. Two cognitive fusion-targeted biopsy cores were added for each lesion in patients who had suspicious or equivocal lesions on mpMRI. The PI-RADS scoring system version 2.0 (PI-RADS v2) was used to describe the MRI findings. Univariate and multivariate analyses were performed to determine significant predictors of prostate cancer and clinically significant prostate cancer. In all, 288 patients were analysed. The median patient age, PSA level, prostate volume and PSA density were 69 years, 7.5 ng/mL, 28.7 mL, and 0.26 ng/mL/mL, respectively. The biopsy results were benign, clinically insignificant, and clinically significant prostate cancer in 129 (45%), 18 (6%) and 141 (49%) patients, respectively. The multivariate analysis revealed that PI-RADS v2 score and PSA density were independent predictors for prostate cancer and clinically significant prostate cancer. When PI-RADS v2 score and PSA density were combined, a PI-RADS v2 score of ≥4 and PSA density ≥0.15 ng/mL/mL, or PI-RADS v2 score of 3 and PSA density of ≥0.30 ng/mL/mL, was associated with the highest clinically significant prostate cancer detection rates (76-97%) on the first biopsy. Of the patients in this group with negative biopsy results, 22% were subsequently diagnosed as prostate cancer. In contrast, a PI-RADS v2 score of ≤3 and PSA density of <0.15 ng/mL/mL yielded no clinically significant prostate cancer and no additional detection of prostate cancer on further biopsies. A combination of PI-RADS v2 score and PSA density can help in the decision-making process before prostate biopsy and in the follow-up strategy in biopsy naïve patients. Patients with a PI-RADS v2 score of ≤3 and PSA density of <0.15 ng/mL/mL may avoid unnecessary biopsies.

MP05-20 HISTOPATHOLOGIC CORRELATION OF PIRADS VERSION 2 LESIONS ON 3T MULTIPARAMETRIC PROSTATE MRI

You have accessJournal of UrologyImaging/Radiology: Uroradiology I1 Apr 2016MP05-20 HISTOPATHOLOGIC CORRELATION OF PIRADS VERSION 2 LESIONS ON 3T MULTIPARAMETRIC PROSTATE MRI Corinne C. Liu, Kaitlin E. Kosinski, Jeffrey T. Schiff, and Aaron E. Katz Corinne C. LiuCorinne C. Liu More articles by this author , Kaitlin E. KosinskiKaitlin E. Kosinski More articles by this author , Jeffrey T. SchiffJeffrey T. Schiff More articles by this author , and Aaron E. KatzAaron E. Katz More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2016.02.1997AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Prostate Imaging Reporting and Data System (PIRADS) Version 2 was recently issued by The American College of Radiology. Each score, from 1 to 5, corresponds to the likelihood of clinically significant cancer. We aim to evaluate the correlation between a higher PIRADS score and the presence of high grade tumor on histopathology. METHODS 300 consecutive 3T Multiparametric Prostate MRI (MP-MRI) were identified from March 26, 2012 to June 29, 2015. We reviewed patient charts and a total of 102 patients underwent either a transrectal ultrasound guided (TRUS) biopsy or a total prostatectomy after the MP-MRI. 34 patients were excluded due to prior radiation or cryoablation to the prostate, leaving 68 treatment naive patients (44 - 84 years). MP-MRIs were re-interpreted utilizing the new PIRADS V.2 scoring system and correlated with pathology findings. Statistical comparisons were made using R (v.3.1.2) and Fisher’s exact test. RESULTS High grade (Gleason 8-10) tumors were found only in MP-MRI with a PIRADS score of 5 and a higher percentage of tumor is found in MP-MRI with PIRADS scores of 4 and 5 (80%) when compared to PIRADS scores of 2 and 3 (32%). Low grade (Gleason 6-7) tumors were also found in both PIRADS 4 and 5 categories. There was a significant relationship between a higher PIRADS score and a finding of cancer on pathology (p = 0.0003, OR=0.118, 95% CI 0.03-0.41). CONCLUSIONS The PIRADS scoring system is a useful tool in suggesting the likelihood of clinically significant cancer, with high grade tumors found only in MP-MRI with a score of PIRADS 5. However, MP-MRI and PIRADS criteria alone are not sufficient for determining clinically significant prostate cancer since both high grade and low grade tumors were found in PIRADS 5 and 4 categories. Correlation with other clinical parameters and biopsy is required for confirmation. A larger scale study would be helpful for further delineation. © 2016FiguresReferencesRelatedDetails Volume 195Issue 4SApril 2016Page: e44-e45 Advertisement Copyright & Permissions© 2016MetricsAuthor Information Corinne C. Liu More articles by this author Kaitlin E. Kosinski More articles by this author Jeffrey T. Schiff More articles by this author Aaron E. Katz More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Prostate Cancer: PI-RADS Version 2 Helps Preoperatively Predict Clinically Significant Cancers.

Purpose To retrospectively analyze whether Prostate Imaging Reporting and Data System (PI-RADS) version 2 is helpful for the detection of clinically significant prostate cancer. Materials and Methods Institutional review board approved this retrospective study. A total of 425 patients with prostate cancer who had undergone magnetic resonance (MR) imaging and radical prostatectomy were included. Preoperative parameters such as prostate-specific antigen, biopsy Gleason score, greatest percentage of the core, percentage of the positive core number, and score at PI-RADS version 2 with MR imaging were investigated. Two independent readers performed PI-RADS scoring. Clinically significant prostate cancer was defined as follows: (a) Gleason score of 7 or greater, (b) tumor volume of 0.5 cm(3) or greater, or a (c) positive extracapsular extension or seminal vesicle invasion. The reference standard was based on review of surgical specimen. Logistic regression was conducted to determine which parameters are associated with the presence of clinically significant cancer. Interreader agreement (ie, score ≥4 or not) was investigated by using κ statistics. Results At univariate analysis, all of the preoperative parameters were significant for clinically significant prostate cancer (P < .05). However, multivariate analysis revealed that PI-RADS score was the only significant parameter for both readers (reader 1: odds ratio = 28.170, P = .002; reader 2: odds ratio = 5.474, P = .007). The interreader agreement was excellent for PI-RADS score of 4 or greater (weighted κ = 0.801; 95% confidence interval: 0.737, 0.865). Conclusion The use of PI-RADS version 2 may help preoperatively diagnose clinically significant prostate cancer. (©) RSNA, 2016.

Utility of prostate imaging reporting and data system for predicting prostate cancer

Objective To evaluate the efficiency of prostate imaging reporting and data system (PI-RADS) for the diagnosis of prostate cancer in Chinese population. Methods The 43 patients received the multiparametric MRI (mp-MRI) test followed by targeted prostate biopsy from July 2014 to June2015. The PI-RADS score of each region of interests (ROIs) was evaluated according to the European Society of Urological Radiology (ESUR) guideline. Receiver operating characteristics (ROC) curve was performed to test diagnostic efficacy of mp-MRI. Results The 71 ROIs were evaluated, of which 25 were prostate cancer and 46 were benign prostatic hyperplasia (BPH). Compared with BPH, prostate cancer showed the higher score of T2WI (3.80±0.50 vs. 3.13±0.54), DWI (4.20±0.76 vs. 2.93±0.71) and PI-RADS (4.04±0.61 vs. 3.02±0.68) (all P<0.05). The ROIs score was higher in high grade prostate cancer than in low grade cancer (P<0.05). The area under the curve (AUC) for the detection of prostate cancer was 0.83, 0.88 and 0.89 for T2WI, DWI and PI-RADS, with the sensitivity of 84%, 80%, 88% and specificity of 80%, 89%, 87%, respectively. As for high grade prostate cancer, the detection sensitivity was 80%, 87% and 93%, and the specificity was 68%, 79% and 75%. Conclusions The PI-RADS scoring system shows high relevance to prostate cancer, which is of great value for the diagnosis of prostate cancer in Chinese population. Key words: Magnetic resonance imaging; Diagnosis; Prostatic neoplasms

Histopathologic correlation of PI-RADS V.2 lesions on 3T multiparametric prostate MRI.

10 Background: Prostate Imaging Reporting and Data System (PIRADS) Version 2 was recently issued by The American College of Radiology. Each score, from 1 to 5, corresponds to the likelihood of clinically significant cancer. We aim to evaluate the correlation between a higher PIRADS score and the presence of high grade tumor on histopathology. Methods: 300 consecutive 3T Multiparametric Prostate MRI (MP-MRI) were identified from March 26, 2012, to June 29, 2015. We reviewed patient charts and a total of 102 patients underwent either a transrectal ultrasound guided (TRUS) biopsy or a total prostatectomy after the MP-MRI. Thirty-four patients were excluded due to prior radiation or cryoablation to the prostate, leaving 68 treatment-naive patients (44-84 years). MP-MRIs were re-interpreted utilizing the new PIRADS V.2 scoring system and correlated with pathology findings. Statistical comparisons were made using R (v.3.1.2) and Fisher’s exact test. Results: High grade (Gleason 8-10) tumors were found only in MP-MRI with a PIRADS score of 5 and a higher percentage of tumor is found in MP-MRI with PIRADS scores of 4 and 5 (80%) when compared to PIRADS scores of 2 and 3 (32%). Low grade (Gleason 6-7) tumors were also found in both PIRADS 4 and 5 categories. There was a significant relationship between a higher PIRADS score and a finding of cancer on pathology (p = 0.0003, OR=0.118, 95% CI 0.03-0.41). Conclusions: The PIRADS scoring system is a useful tool in suggesting the likelihood of clinically significant cancer, with high grade tumors found only in MP-MRI with a score of PIRADS 5. However, MP-MRI and PIRADS criteria alone are not sufficient for determining clinically significant prostate cancer since both high grade and low grade tumors were found in PIRADS 5 and 4 categories. Correlation with other clinical parameters and biopsy is required for confirmation. A larger scale study would be helpful for further delineation. [Table: see text]

Predicting Pathological Features at Radical Prostatectomy in Patients with Prostate Cancer Eligible for Active Surveillance by Multiparametric Magnetic Resonance Imaging

PurposeThe aim of this study was to investigate the prognostic performance of multiparametric magnetic resonance imaging (mpMRI) and Prostate Imaging Reporting and Data System (PIRADS) score in predicting pathologic features in a cohort of patients eligible for active surveillance who underwent radical prostatectomy.MethodsA total of 223 patients who fulfilled the criteria for “Prostate Cancer Research International: Active Surveillance”, were included. Mp–1.5 Tesla MRI examination staging with endorectal coil was performed at least 6–8 weeks after TRUS-guided biopsy. In all patients, the likelihood of the presence of cancer was assigned using PIRADS score between 1 and 5. Outcomes of interest were: Gleason score upgrading, extra capsular extension (ECE), unfavorable prognosis (occurrence of both upgrading and ECE), large tumor volume (≥0.5ml), and seminal vesicle invasion (SVI). Receiver Operating Characteristic (ROC) curves and Decision Curve Analyses (DCA) were performed for models with and without inclusion of PIRADS score.ResultsMultivariate analysis demonstrated the association of PIRADS score with upgrading (P<0.0001), ECE (P<0.0001), unfavorable prognosis (P<0.0001), and large tumor volume (P = 0.002). ROC curves and DCA showed that models including PIRADS score resulted in greater net benefit for almost all the outcomes of interest, with the only exception of SVI.ConclusionsmpMRI and PIRADS scoring are feasible tools in clinical setting and could be used as decision-support systems for a more accurate selection of patients eligible for AS.