Prostate Imaging Reporting And Data System Version 2 Research Articles

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

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

Performance of a Region of Interest–based Algorithm in Diagnosing International Society of Urological Pathology Grade Group ≥2 Prostate Cancer on the MRI-FIRST Database—CAD-FIRST Study

Background and objectiveProstate multiparametric magnetic resonance imaging (MRI) shows high sensitivity for International Society of Urological Pathology grade group (GG) ≥2 cancers. Many artificial intelligence algorithms have shown promising results in diagnosing clinically significant prostate cancer on MRI. To assess a region-of-interest–based machine-learning algorithm aimed at characterising GG ≥2 prostate cancer on multiparametric MRI. MethodsThe lesions targeted at biopsy in the MRI-FIRST dataset were retrospectively delineated and assessed using a previously developed algorithm. The Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) score assigned prospectively before biopsy and the algorithm score calculated retrospectively in the regions of interest were compared for diagnosing GG ≥2 cancer, using the areas under the curve (AUCs), and sensitivities and specificities calculated with predefined thresholds (PIRADSv2 scores ≥3 and ≥4; algorithm scores yielding 90% sensitivity in the training database). Ten predefined biopsy strategies were assessed retrospectively. Key findings and limitationsAfter excluding 19 patients, we analysed 232 patients imaged on 16 different scanners; 85 had GG ≥2 cancer at biopsy. At patient level, AUCs of the algorithm and PI-RADSv2 were 77% (95% confidence interval [CI]: 70–82) and 80% (CI: 74–85; p = 0.36), respectively. The algorithm’s sensitivity and specificity were 86% (CI: 76–93) and 65% (CI: 54–73), respectively. PI-RADSv2 sensitivities and specificities were 95% (CI: 89–100) and 38% (CI: 26–47), and 89% (CI: 79–96) and 47% (CI: 35–57) for thresholds of ≥3 and ≥4, respectively. Using the PI-RADSv2 score to trigger a biopsy would have avoided 26–34% of biopsies while missing 5–11% of GG ≥2 cancers. Combining prostate-specific antigen density, the PI-RADSv2 and algorithm’s scores would have avoided 44–47% of biopsies while missing 6–9% of GG ≥2 cancers. Limitations include the retrospective nature of the study and a lack of PI-RADS version 2.1 assessment. Conclusions and clinical implicationsThe algorithm provided robust results in the multicentre multiscanner MRI-FIRST database and could help select patients for biopsy. Patient summaryAn artificial intelligence–based algorithm aimed at diagnosing aggressive cancers on prostate magnetic resonance imaging showed results similar to expert human assessment in a prospectively acquired multicentre test database.

Prostagram magnetic resonance imaging in a screening population: Prostate Imaging-Reporting and Data System or Likert?

To compare biopsy recommendation rates and accuracy of the Prostate Imaging-Reporting and Data System, version 2 (PI-RADSv2) with the Likert scale for detection of clinically significant and insignificant prostate cancer in men screened within the Imperial Prostate 1 Prostate Cancer Screening Trial Using Imaging (IP1-PROSTAGRAM). Men aged 50-69 years were screened with Prostagram MRI. Scans were prospectively reported using both PI-RADSv2 (excluding dynamic contrast-enhanced sequence score) and 5-point Likert scores by expert uro-radiologists. Systematic and targeted transperineal biopsy was recommended if the scan was scored ≥ 3, based on either reporting system. The proportion of patients recommended for biopsy and detection rates for Grade Groups (GGs) 1 and ≥ 2 were compared. Receiver operating characteristic (ROC) analysis was performed to compare performance. A total of 406 men underwent Prostagram MRI. The median (interquartile range) age and prostate-specific antigen level were 57 (53-61) years and 0.91 (0.56-1.74) ng/mL, respectively. At MRI score ≥ 3, more patients were recommended for biopsy based on Likert criteria (94/406; 23%, 95% confidence interval [CI] 19.2%-27.6%) compared to PI-RADSv2 (72/406; 18%, 95% CI 14.2%-21.9%; P = 0.03). For MRI scores ≥ 4, PI-RADSv2 and Likert scales led to 43/406 (11%, 95% CI 7.9%-14.1%) and 35/406 (9%, 95% CI 6.2%-11.9%) men recommended for biopsy (P = 0.40). For GG ≥ 2 detection, PIRADSv2 and Likert detected 22% (95% CI 11.4%-30.8%, 14/72) and 16% (95% CI 9.5%-25.3%, 15/94), respectively (P = 0.56). For GG1 cancers detection these were 11% (95% CI 4.3%-19.6%, seven of 72) vs 11% (95% CI 4.7%-17.8%, nine of 94; P = 1.00). The accuracy of PI-RADSv2 and Likert scale was similar (area under the ROC curve 0.64 vs 0.65, P = 0.95). In reporting non-contrast-enhanced Prostagram MRI in a screening population, the PI-RADSv2 and Likert scoring systems were equally accurate; however, Likert scale use led to more men undergoing biopsy without a subsequent increase in significant cancer detection rates. To improve reporting of Prostagram MRI, either the PI-RADSv2 or a modified Likert scale or a standalone scoring system should be developed.

The Impact of Multiparametric Magnetic Resonance Imaging on Treatment Strategies for Incidental Prostate Cancer after Holmium Laser Enucleation of the Prostate.

To investigate the impact of multiparametric magnetic resonance imaging (mpMRI) on treatment strategies for incidental prostate cancer (iPCa) after holmium enucleation of the prostate (HoLEP); Methods: Data from 1781 men who underwent HoLEP for clinical bladder outlet obstruction between September 2009 and March 2022 were reviewed retrospectively. Among patients with confirmed iPCa, those with prostate-specific antigen (PSA) levels < 10 ng/mL and who underwent mpMRI 3 months after HoLEP were included. Pathologic results, including Gleason grade (GG) and tumor volume, were identified. mpMRI was interpreted using the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2). Treatment strategies for iPCa according to GG alone, or according to a combination of Gleason grade and mpMRI, were analyzed and compared. Of 1764 men with serum PSA levels < 10 ng/mL, iPCa was confirmed in 64 (3.6%) after HoLEP. Of the 62 men who underwent mpMRI, the median (IQR) age at the time of HoLEP was 72.5 (66.5-78.0) years. The median PSA level and prostate volume were 3.49 (1.82-5.03) ng/mL and 49.6 (38.5-85.4) cm3, respectively. The pathologic results of iPCa were as follows: GG1 = 42 (67.7%), GG2 = 13 (21.0%), GG3 = 2 (3.2%), GG4 = 1 (1.6%), and GG5 = 4 (6.5%). Of the patients with GG1 and GG2, 78.6% (33/42) and 53.8% (7/13), respectively, underwent active surveillance (AS). However, of 42 patients with GG1, 27 (64.3%) had a PI-RADSv2 score of 2, and 24 (88.9%) of them underwent AS. Of the 13 patients with GG2, 4 (80%) with a PI-RADSv2 score of 2 underwent AS. All patients with GG 3-5 were clinically expected to have locally advanced PCa and be treated with radiotherapy and/or ADT. For patients with iPCa of GG 1-2 after HoLEP, mpMRI helps to establish a treatment strategy by allowing risk stratification to select those who should be considered for AS or active treatment.

Characterization of high-grade prostate cancer at multiparametric MRI using a radiomic-based computer-aided diagnosis system as standalone and second reader

PurposeThe purpose of this study was to develop and test across various scanners a zone-specific region-of-interest (ROI)-based computer-aided diagnosis system (CAD) aimed at characterizing, on MRI, International Society of Urological Pathology (ISUP) grade≥2 prostate cancers. Materials and methodsROI-based quantitative models were selected in multi-vendor training (265 pre-prostatectomy MRIs) and pre-test (112 pre-biopsy MRIs) datasets. The best peripheral and transition zone models were combined and retrospectively assessed in internal (158 pre-biopsy MRIs) and external (104 pre-biopsy MRIs) test datasets. Two radiologists (R1/R2) retrospectively delineated the lesions targeted at biopsy in test datasets. The CAD area under the receiver operating characteristic curve (AUC) for characterizing ISUP≥2 cancers was compared to that of the Prostate Imaging-Reporting and Data System version2 (PI-RADSv2) score prospectively assigned to targeted lesions. ResultsThe best models used the 25th apparent diffusion coefficient (ADC) percentile in transition zone and the 2nd ADC percentile and normalized wash-in rate in peripheral zone. The PI-RADSv2 AUCs were 82% (95% confidence interval [CI]: 74–87) and 86% (95% CI: 81–91) in the internal and external test datasets respectively. They were not different from the CAD AUCs obtained with R1 and R2 delineations, in the internal (82% [95% CI: 76–89], P = 0.95 and 85% [95% CI: 78–91], P = 0.55) and external (82% [95% CI: 74–91], P = 0.41 and 86% [95% CI:78–95], P = 0.98) test datasets. The CAD yielded sensitivities of 86–89% and 90–91%, and specificities of 64–65% and 69–75% in the internal and external test datasets respectively. ConclusionThe CAD performance for characterizing ISUP grade≥2 prostate cancers on MRI is not different from that of PI-RADSv2 score across two test datasets.

Prospective Evaluation of PI-RADS Version 2.1 for Prostate Cancer Detection and Investigation of Multiparametric MRI-derived Markers.

Background Data regarding the prospective performance of Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 alone and in combination with quantitative MRI features for prostate cancer detection is limited. Purpose To assess lesion-based clinically significant prostate cancer (csPCa) rates in different PI-RADS version 2.1 categories and to identify MRI features that could improve csPCa detection. Materials and Methods This single-center prospective study included men with suspected or known prostate cancer who underwent multiparametric MRI and MRI/US-guided biopsy from April 2019 to December 2021. MRI scans were prospectively evaluated using PI-RADS version 2.1. Atypical transition zone (TZ) nodules were upgraded to category 3 if marked diffusion restriction was present. Lesions with an International Society of Urological Pathology (ISUP) grade of 2 or higher (range, 1-5) were considered csPCa. MRI features, including three-dimensional diameter, relative lesion volume (lesion volume divided by prostate volume), sphericity, and surface to volume ratio (SVR), were obtained from lesion contours delineated by the radiologist. Univariable and multivariable analyses were conducted at the lesion and participant levels to determine features associated with csPCa. Results In total, 454 men (median age, 67 years [IQR, 62-73 years]) with 838 lesions were included. The csPCa rates for lesions categorized as PI-RADS 1 (n = 3), 2 (n = 170), 3 (n = 197), 4 (n = 319), and 5 (n = 149) were 0%, 9%, 14%, 37%, and 77%, respectively. csPCa rates of PI-RADS 4 lesions were lower than PI-RADS 5 lesions (P < .001) but higher than PI-RADS 3 lesions (P < .001). Upgraded PI-RADS 3 TZ lesions were less likely to harbor csPCa compared with their nonupgraded counterparts (4% [one of 26] vs 20% [20 of 99], P = .02). Predictors of csPCa included relative lesion volume (odds ratio [OR], 1.6; P < .001), SVR (OR, 6.2; P = .02), and extraprostatic extension (EPE) scores of 2 (OR, 9.3; P < .001) and 3 (OR, 4.1; P = .02). Conclusion The rates of csPCa differed between consecutive PI-RADS categories of 3 and higher. MRI features, including lesion volume, shape, and EPE scores of 2 and 3, predicted csPCa. Upgrading of PI-RADS category 3 TZ lesions may result in unnecessary biopsies. ClinicalTrials.gov registration no. NCT03354416 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Goh in this issue.

Prostate imaging-reporting and data system version 2 has improved biopsy tumor grade accuracy: a single, tertiary institutional experience.

To investigate change in prostate biopsy accuracy regarding tumor grade before and after the release of Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) in a single tertiary institution. We retrospectively evaluated 1191 patients with biopsy-proven prostate cancer (PCa) who had undergone prostate magnetic resonance imaging (MRI) and surgery before (2013 cohort, n = 394) and 5 years after PI-RADSv2 release (2020 cohort, n = 797). The highest tumor grade of each biopsy and surgical specimen was recorded, respectively. We compared concordant, underestimated, and overestimated biopsy rates regarding tumor grade to surgery between two cohorts, respectively. For patients who underwent both prostate MRI and biopsy at our institution, we investigated proportion of pre-biopsy MRI, age, and prostate-specific antigen of patients, and performed logistic regression to analyze which parameters are associated with concordant biopsy. Concordant and underestimated biopsy rates were significantly different between two cohorts: Concordance and underestimation rates were 47.2% and 46.3% in 2013 and 54.5% and 36.4% in 2020 (p = .019; p = .003), respectively. Overestimated biopsy rates were similar (p = .993). Proportion of pre-biopsy MRI was significantly higher in 2020 than in 2013 (80.9% versus 4.9%; p < .001), and was independently associated with concordant biopsy results in multivariate analysis (odds ratio = 1.486; 95% confidence interval, 1.057-2.089; p = .022). There was a significant change in proportion of pre-biopsy MRI before and after the release of PI-RADSv2 in patients who underwent surgery for PCa. This change appears to have improved biopsy accuracy regarding tumor grade by reducing underestimation.

PI-RADS: Where Next?

Prostate MRI plays an important role in the clinical management of localized prostate cancer, mainly assisting in biopsy decisions and guiding biopsy procedures. The Prostate Imaging Reporting and Data System (PI-RADS) has been available to radiologists since 2012, with the most up-to-date and actively used version being PI-RADS version 2.1. This review article discusses the current use of PI-RADS, including its limitations and controversies, and summarizes research that aims to improve future iterations of this system.

Comparison of PSA density and lesion volume strategies for selecting men with equivocal PI-RADS 3 lesions on bpMRI for biopsies

PurposeTo compare two strategies: Prostate-specific antigen density (PSAd) and lesion volume measurement in ruling out significant prostate cancer (sPCa) in men with equivocal Prostate Imaging Reporting and Data System (PI-RADS) category 3 index lesions on biparametric magnetic resonance imaging.MethodsIn total, 130 men from our database had index lesions with PI-RADS scores of 3. Prostate volume was measured using the ellipsoid method, in accordance with PI-RADS version 2.1 criteria. Index lesion volumes were also measured using the ellipsoidal formula on the diffusion-weighted imaging sequence with the highest b-value and sagittal T2 sequences.ResultsAmong 130 men with PI-RADS category 3 index lesions, 23 (18%) had sPCa. In total, 6 of the 89 men with PSAd < 0.15 ng/mL2 (7%) had sPCa, whereas 8 of the 49 men with index lesion volumes < 0.5 mL (16%) had sPCa. The difference was statistically significant (McNemar, p < 0.0001).ConclusionThe PSAd strategy performed better than the lesion volume strategy in ruling out sPCa in men with equivocal PI-RADS category 3 index lesions.

An independent practice validation of the Prostate Imaging Reporting and Data System version 2 scoring system and the introduction of PDP (prostate-specific antigen density × PI-RADSv2) score to assist with further risk assessment.

To provide concise information to clinicians on how to better interpret multiparametric magnetic resonance imaging for prostate cancer risk stratification. We analyzed 2 separate cohorts. For patients receiving a Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) score of 1 or 2, we reviewed the charts of 226 patients who underwent multiparametric magnetic resonance imaging of the prostate ordered from 2015 to 2017 to determine who developed clinically significant prostate cancer (csPCa) by August 27, 2020. For patients receiving PI-RADSv2 a score of 3, 4, or 5, we reviewed the results of 733 fusion biopsies on solitary lesions. Statistical analysis was used to further determine risk factors for csPCa. Ten percent of men with PI-RADSv2 a score of 1 eventually developed csPCa. Seven percent with a score of 2 were eventually diagnosed with csPCa. Only 1 of 226 with a score of 1 or 2 developed metastasis. For PI-RADSv2 scores of 3, 4, and 5, csPCa was detected in 16%, 45%, and 67% of fusion biopsies. Peripheral zone (PZ) PI-RADSv2 score of 4 or 5 and prostate-specific antigen density (PSA-D) were significant predictors of csPCa on multivariable analysis. Using a PSA-D × PI-RADSv2 score of ≤0.39, we identified 38% of men with a PI-RADSv2 score of 3 in the PZ or 3, 4, or 5 in the transition zone who could have avoided a benign biopsy. The vast majority of patients with PI-RADSv2 scores 1 and 2 can be safely monitored with close surveillance. Lesions with PI-RADSv2 scores of 4 and 5 in the PZ should be biopsied. Peripheral zone lesions with a PI-RADSv2 score of 3 and transition zone lesions with 3, 4, or 5 can be risk-stratified using the PSA-D × PI-RADSv2 score to determine who may safely avoid a biopsy and who should proceed to fusion biopsy.

Clinical Utility of Prostate Health Index for Diagnosis of Prostate Cancer in Patients with PI-RADS 3 Lesions.

Simple SummaryMulti-parametric magnetic resonance imaging (mpMRI) is regarded as an essential tool for identifying prostate cancer (PCa) in suspected cases. However, unnecessary biopsies continue to be performed in real clinics, especially for prostate imaging reporting and data system version 2 (PI-RADS v2) score-3 lesions, corresponding to the “gray zone”. To aid the diagnosis of PCa, as well as of clinically significant PCa (csPCa), in patients with PI-RADSv2 score-3 lesions, we evaluated the clinical utility of the prostate health index (PHI). When a biopsy was restricted to those patients with PI-RADSv2 score-3 lesions and a PHI of ≥30, 34.4% of unnecessary biopsies could be avoided at the cost of missing 8.3% of overall PCa cases. However, there were no cases of missed csPCa diagnosis. The combination of PHI and PI-RADSv2 score-3 lesions offered higher accuracy in the diagnosis of PCa as well as of csPCa.The risk of prostate cancer (PCa) in prostate imaging reporting and data system version 2 (PI-RADSv2) score-3 lesions is equivocal; it is regarded as an intermediate status of presented PCa. In this study, we evaluated the clinical utility of the prostate health index (PHI) for the diagnosis of PCa and clinically significant PCa (csPCa) in patients with PI-RADSv2 score-3 lesions. The study cohort included patients who underwent a transrectal ultrasound (TRUS)-guided, cognitive-targeted biopsy for PI-RADSv2 score-3 lesions between November 2018 and April 2021. Before prostate biopsy, the prostate-specific antigen (PSA) derivatives, such as total PSA (tPSA), [-2] proPSA (p2PSA) and free PSA (fPSA) were determined. The calculation equation of PHI is as follows: [(p2PSA/fPSA) × tPSA ½]. Using a receiver operating characteristic (ROC) curve analysis, the values of PSA derivatives measured by the area under the ROC curve (AUC) were compared. For this study, csPCa was defined as Gleason grade 2 or higher. Of the 392 patients with PI-RADSv2 score-3 lesions, PCa was confirmed in 121 (30.9%) patients, including 59 (15.1%) confirmed to have csPCa. Of all the PSA derivatives, PHI and PSA density (PSAD) showed better performance in predicting overall PCa and csPCa, compared with PSA (all p < 0.05). The AUC of the PHI for predicting overall PCa and csPCa were 0.807 (95% confidence interval (CI): 0.710–0.906, p = 0.001) and 0.819 (95% CI: 0.723–0.922, p < 0.001), respectively. By the threshold of 30, PHI was 91.7% sensitive and 46.1% specific for overall PCa, and was 100% sensitive for csPCa. Using 30 as a threshold for PHI, 34.4% of unnecessary biopsies could have been avoided, at the cost of 8.3% of overall PCa, but would include all csPCa.

Clinical Utility of Combining Prostate Health Index and PI-RADS Version 2 to Improve Detection of Clinically Significant Prostate Cancer

Purpose: To evaluate the performance of combining prostate health index (PHI) and Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) for detection of clinically significant prostate cancer (csPCa).Materials and Methods: We retrospectively reviewed patients who underwent prostate biopsy for elevated prostate-specific antigen (PSA) ≥2.5 ng/mL and/or abnormal digital rectal examination. Serum markers for PSA, free PSA (fPSA), and [-2] proPSA (p2PSA) were measured, and PHI was calculated as ([p2PSA/fPSA]×[PSA]&lt;sup&gt;1/2&lt;/sup&gt;). Multiparametric magnetic resonance imaging was performed using a 3.0T scanner and scored using PI-RADSv2. csPCa was defined as either grade group (GG) ≥2 disease or GG1 cancer detected in &gt;2 cores or &gt;50% of positive on biopsy. Univariable and multivariable logistic regression modelling, along with receiver-operating characteristic (ROC) curve analysis was used to predict the probability of csPCa.Results: Of the total 358 patients, 159 (44.4%) were diagnosed with csPCa. On univariable analysis, age, PSA density (PSAD), PHI and PI-RADSv2 were associated with csPCa. The area under the ROC curve (AUC) of baseline model incorporating age and PSAD was 0.663. The AUC of combining PHI and PI-RADSv2 to baseline model was higher than that of PHI alone to baseline model (0.884 vs. 0.807, p&lt;0.0001) and PI-RADSv2 alone to baseline model (0.884 vs. 0.846, p=0.0002), respectively. If biopsy was restricted to patients with PI-RADS 5 as well as PI-RADS 3 or 4 and PHI ≥27, 36.0% of unnecessary biopsy could be avoided at the cost of missing 4.7% of csPCa.Conclusions: The combination of PHI and PI-RADSv2 to baseline model incorporating age and PSAD had higher accuracy for detection of csPCa compared with PHI or PI-RADSv2 alone.

Audit of cancer yields after prostate MRI using both the PI-RADS version 2 and Likert scoring systems

To audit diagnostic yields of the updated magnetic resonance imaging (MRI)-directed prostate cancer diagnostic service according to Prostate Imaging - Reporting and Data System (PI-RADS) version 2 and Likert assessments, comparing outcomes of the two scoring systems. Consecutive men with suspected cancer undergoing prostate MRI were included. Biopsy rates and histological diagnostic yields of all and International Society of Urological Pathology Grade Group (ISUP GG) ≥2 cancers according to PI-RADS and Likert assessment categories were documented and outcomes compared. Of 326 men (91% biopsy naive), 177 (54%) underwent transrectal (n=119) or transperineal (n=58) ultrasound-guided biopsies; 92% with negative MRI avoided immediate biopsies following multidisciplinary team (MDT) review. All cancer and ISUP GG≥2 cancer-detection rates increased with increasing suspicion scores. Prospective paired PI-RADS and Likert scoring was undertaken in 323/326 studies, with 87% concordance rate. High concordance between PI-RADS and Likert scores was observed in negative MRI (99%) and score 5 (96%). High discordance was demonstrated in the PI-RADS 4 group (32% with PI-RADS 4 categories up-risked to Likert 5). All cancer and ISUP GG≥2 cancer detection rates for MRI score ≥3 were 78% and 63%, and for MRI score ≥4 were 75% and 61%, respectively for both PI-RADS and Likert scoring systems. Most men with negative prostate MRI can avoid immediate biopsies following MDT review. Performance of PI-RADS and Likert scoring systems in clinically significant cancer detection after positive MRI is equivalent. Discordance between PI-RADS and Likert systems seems mostly confined to PI-RADS 4 categories.

Comparison of multiple abbreviated multiparametric MRI-derived protocols for the detection of clinically significant prostate cancer.

The aim of this paper was to compare the accuracy of multiple abbreviated multiparametric magnetic resonance imaging (mpMRI)-derived protocols in detecting clinically significant prostate cancer (csPCa). One hundred and eight men undergoing staging 3.0T mpMRI with a Prostate Imaging - Reporting and Data System version 2 (PI-RADSv2)-compliant protocol before radical prostatectomy (RP) were retrospectively evaluated. Two readers (R1, R2) independently analyzed mpMRI, assigning a PI-RADSv2 category to each observation as appearing on each examination sequence. A study coordinator assessed final PI-RADSv2 category by combining readers' assignments according to four protocols: short MRI (sMRI) (diffusion-weighted imaging + axial T2-weighted imaging), contrast-enhanced short MRI (cesMRI) (sMRI + dynamic contrast-enhanced [DCE] imaging), biparametric MRI (diffusion-weighted imaging + multiplanar T2-weigthed imaging), and mpMRI. Using RP pathology as the reference standard for csPCa, we calculated the per-lesion cancer detection rate (CDR) and false discovery rate (FDR) for each MRI protocol (cut-off PI-RADSv2 category ≥3), and the per-PI-RADSv2 category prevalence of csPCa and false positives. Pathology after RP found 142 csPCas with median International Society of Urogenital Pathology grade group 2, and stage ≤pT2c in 68.6% of cases. CDR was comparable across the four MRI protocols (74.6% to 75.3% for R1, and 68.3% for R2). FDR was comparable as well (14.4%-14.5% for R1 and 11.1% for R2). sMRI was the minimum protocol equaling mpMRI in terms of CDR, although cesMRI, similarly to mpMRI, was associated with fewer PI-RADSv2 category 3 assignments and higher prevalence of csPCa within PI-RADSv2 category 3 observations (66.7% versus 76.9% for R1, and 100% versus 91.7% for R2, respectively). Among multiple abbreviated mpMRI-derived protocols, cesMRI was the one equaling mpMRI in terms of csPCa detection and minimizing PI-RADSv2 category 3 assignments.

Artificial intelligence trained with integration of multiparametric MR-US imaging data and fusion biopsy trajectory-proven pathology data for 3D prediction of prostate cancer: A proof-of-concept study.

We aimed to develop an artificial intelligence (AI) algorithm that predicts the volume and location of clinically significant cancer (CSCa) using convolutional neural network (CNN) trained with integration of multiparametric MR-US image data and MRI-US fusion prostate biopsy (MRI-US PBx) trajectory-proven pathology data. Twenty consecutive patients prospectively underwent MRI-US PBx, followed by robot-assisted radical prostatectomy (RARP). The AI algorithm was trained with the integration of MR-US image data with a MRI-US PBx trajectory-proven pathology. The relationship with the 3D-cancer-mapping of RARP specimens was compared between AI system-suggested 3D-CSCa mapping and an experienced radiologist's suggested 3D-CSCa mapping on MRI alone according to the Prostate Imaging Reporting and Data System (PI-RADS) version 2. The characteristics of detected and undetected tumors at AI were compared in 22,968 image data. The relationships between CSCa volumes and volumes predicted by AI as well as the radiologist's reading based on PI-RADS were analyzed. The concordance of the CSCa center with that in RARP specimens was significantly higher in the AI prediction than the radiologist' reading (83% vs. 54%, p = 0.036). CSCa volumes predicted with AI were more accurate (r = 0.90, p < 0.001) than the radiologist's reading. The limitations include that the elastic fusion technology has its own registration error. We presented a novel pilot AI algorithm for 3D prediction of PCa. AI was trained by integration of multiparametric MR-US image data and fusion biopsy trajectory-proven pathology data. This deep learning AI model may more precisely predict the 3D mapping of CSCa in its volume and center location than a radiologist's reading based on PI-RADS version 2, and has potential in the planning of focal therapy.

PI-RADS version 2.1 for the evaluation of transition zone lesions: a practical guide for radiologists.

Multiparametric MRI has been established as the most accurate non-invasive diagnostic imaging tool for detecting prostate cancer (PCa) in both the peripheral zone and the transition zone (TZ) using the PI-RADS (Prostate Imaging Reporting and Data System) v. 2.1 released in 2019 as a guideline to reporting. TZ PCa remains the most difficult to diagnose due to a markedly heterogeneous background and a wide variety of atypical imaging presentations as well as other anatomical and pathological processes mimicking PCa. The aim of this paper was to present a spectrum of PCa in the TZ, as a guide for radiologists.

Evaluation of the Predictive Role of Blood-Based Biomarkers in the Context of Suspicious Prostate MRI in Patients Undergoing Prostate Biopsy.

The aim of this study was to assess the predictive value of pre-biopsy blood-based markers in patients undergoing a fusion biopsy for suspicious prostate magnetic resonance imaging (MRI). We identified 365 consecutive patients who underwent MRI-targeted and systematic prostate biopsy for an MRI scored Prostate Imaging–Reporting and Data System Version (PI-RADS) ≥ 3. We evaluated the neutrophil/lymphocyte ratio (NLR), derived neutrophil/lymphocyte ratio (dNLR), platelet/lymphocyte ratio (PLR), systemic immune inflammation index (SII), lymphocyte/monocyte ratio (LMR,) de Ritis ratio, modified Glasgow Prognostic Score (mGPS), and prognostic nutrition index (PNI). Uni- and multivariable logistic models were used to analyze the association of the biomarkers with biopsy findings. The clinical benefits of biomarkers implemented in clinical decision-making were assessed using decision curve analysis (DCA). In total, 69% and 58% of patients were diagnosed with any prostate cancer and Gleason Grade (GG) ≥ 2, respectively. On multivariable analysis, only high dNLR (odds ratio (OR) 2.61, 95% confidence interval (CI) 1.23–5.56, p = 0.02) and low PNI (OR 0.48, 95% CI 0.26–0.88, p = 0.02) remained independent predictors for GG ≥ 2. The logistic regression models with biomarkers reached AUCs of 0.824–0.849 for GG ≥ 2. The addition of dNLR and PNI did not enhance the net benefit of a standard clinical model. Finally, we created the nomogram that may help guide biopsy avoidance in patients with suspicious MRI. In patients with PI-RADS ≥ 3 lesions undergoing MRI-targeted and systematic biopsy, a high dNLR and low PNI were associated with unfavorable biopsy outcomes. Pre-biopsy blood-based biomarkers did not, however, significantly improve the discriminatory power and failed to add a clinical benefit beyond standard clinical factors.

MP05-04 CLINICALLY SIGNIFICANT PROSTATE CANCER DETECTION RATES ON MRI GUIDED FUSION NEEDLE BIOPSY - EXPERIENCE FROM THE PENNSYLVANIA UROLOGIC REGIONAL COLLABORATIVE

You have accessJournal of UrologyProstate Cancer: Detection & Screening I (MP05)1 Sep 2021MP05-04 CLINICALLY SIGNIFICANT PROSTATE CANCER DETECTION RATES ON MRI GUIDED FUSION NEEDLE BIOPSY - EXPERIENCE FROM THE PENNSYLVANIA UROLOGIC REGIONAL COLLABORATIVE Ako Adams Ako, Serge Ginzburg, Eric Singer, Bruce Jacobs, Claudette Fonshell, Adam Reese, Edouard Trabulsi, Jeffery Tomaszewski, John Danella, Laurence Belkoff, Thomas Guzzo, Robert Uzzo, and Jay Raman Ako Adams AkoAko Adams Ako More articles by this author , Serge GinzburgSerge Ginzburg More articles by this author , Eric SingerEric Singer More articles by this author , Bruce JacobsBruce Jacobs More articles by this author , Claudette FonshellClaudette Fonshell More articles by this author , Adam ReeseAdam Reese More articles by this author , Edouard TrabulsiEdouard Trabulsi More articles by this author , Jeffery TomaszewskiJeffery Tomaszewski More articles by this author , John DanellaJohn Danella More articles by this author , Laurence BelkoffLaurence Belkoff More articles by this author , Thomas GuzzoThomas Guzzo More articles by this author , Robert UzzoRobert Uzzo More articles by this author , and Jay RamanJay Raman More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000001972.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Magnetic resonance imaging (MRI) fusion needle biopsy has been shown to outperform systematic biopsy in detecting clinically significant prostate cancer. This study sought to: 1) Examine the variability of clinically significant prostate cancer detection rates in a large, regional quality improvement collaborative. 2) Examine the association between patient characteristics and clinically significant prostate cancer detection. METHODS: The Pennsylvania Urologic Regional Collaborative (PURC) is a physician-led data-sharing and quality improvement collaborative comprised of urology practices across Pennsylvania and New Jersey. We analyzed 857 first-time MRI fusion biopsy procedures performed at 5 PURC practices with a minimum of 30 procedures between January 2015 and June 2019. Chi-square tests and hierarchical logistic regression analyzed the variability in cancer detection rates by practice and the association between patient characteristics and Gleason ≥4+3 tumor detection. RESULTS: Median prostate specific antigen (PSA) was 6.8 ng/ml, 14.4% of men were ≤59 years old. Detection rates for Gleason ≥4+3 tumors by practice ranged from 14.3% to 28.3% (p=0.02). Age, digital rectal examination, PSA, gland volume and Prostate Imaging-Reporting and Data System version2 (PI-RADSv2) score were significantly associated with Gleason ≥4+3 tumors. Adjusting for patient characteristics demonstrated that there was no significant variation in Gleason ≥4+3 tumor detection by practice (Intercept variance = 0.04; p=0.26). PIRADSv2 ≥4 was associated with increased likelihood of detecting a Gleason ≥4+3 tumor (OR 2.67; 95% CI 1.76–4.07; p<0.0001). CONCLUSIONS: Variability in clinically significant prostate cancer detection between individual practice sites was predominantly due to variability in patient characteristics. Results highlight the importance of pre-biopsy MRI in the diagnosis of clinically significant prostate cancer. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e77-e78 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Ako Adams Ako More articles by this author Serge Ginzburg More articles by this author Eric Singer More articles by this author Bruce Jacobs More articles by this author Claudette Fonshell More articles by this author Adam Reese More articles by this author Edouard Trabulsi More articles by this author Jeffery Tomaszewski More articles by this author John Danella More articles by this author Laurence Belkoff More articles by this author Thomas Guzzo More articles by this author Robert Uzzo More articles by this author Jay Raman More articles by this author Expand All Advertisement Loading ...

Utility of machine learning of apparent diffusion coefficient (ADC) and T2-weighted (T2W) radiomic features in PI-RADS version 2.1 category 3 lesions to predict prostate cancer diagnosis.

To evaluate if machine learning (ML) of radiomic features extracted from apparent diffusion coefficient (ADC) and T2-weighted (T2W) MRI can predict prostate cancer (PCa) diagnosis in Prostate Imaging-Reporting and Data System (PI-RADS) version 2.1 category 3 lesions. This multi-institutional review board-approved retrospective case-control study evaluated 158 men with 160 PI-RADS category 3 lesions (79 peripheral zone, 81 transition zone) diagnosed at 3-Tesla MRI with histopathology diagnosis by MRI-TRUS-guided targeted biopsy. A blinded radiologist confirmed PI-RADS v2.1 score and segmented lesions on axial T2W and ADC images using 3D Slicer, extracting radiomic features with an open-source software (Pyradiomics). Diagnostic accuracy for (1) any PCa and (2) clinically significant (CS; International Society of Urogenital Pathology Grade Group ≥ 2) PCa was assessed using XGBoost with tenfold cross -validation. From 160 PI-RADS 3 lesions, there were 50.0% (80/160) PCa, including 36.3% (29/80) CS-PCa (63.8% [51/80] ISUP 1, 23.8% [19/80] ISUP 2, 8.8% [7/80] ISUP 3, 3.8% [3/80] ISUP 4). The remaining 50.0% (80/160) lesions were benign. ML of all radiomic features from T2W and ADC achieved area under receiver operating characteristic curve (AUC) for diagnosis of (1) CS-PCa 0.547 (95% Confidence Intervals 0.510-0.584) for T2W and 0.684 (CI 0.652-0.715) for ADC and (2) any PCa 0.608 (CI 0.579-0.636) for T2W and 0.642 (CI 0.614-0.0.670) for ADC. Our results indicate ML of radiomic features extracted from T2W and ADC achieved at best moderate accuracy for determining which PI-RADS category 3 lesions represent PCa.

INTEGRATIVE RADIOMICS MODELS TO PREDICT BIOPSY RESULTS FOR NEGATIVE PROSTATE MRI.

Multi-parametric MRI (mpMRI) is a powerful non-invasive tool for diagnosing prostate cancer (PCa) and is widely recommended to be performed before prostate biopsies. Prostate Imaging Reporting and Data System version (PI-RADS) is used to interpret mpMRI. However, when the pre-biopsy mpMRI is negative, PI-RADS 1 or 2, there exists no consensus on which patients should undergo prostate biopsies. Recently, radiomics has shown great abilities in quantitative imaging analysis with outstanding performance on computer-aid diagnosis tasks. We proposed an integrative radiomics-based approach to predict the prostate biopsy results when pre-biopsy mpMRI is negative. Specifically, the proposed approach combined radiomics features and clinical features with machine learning to stratify positive and negative biopsy groups among negative mpMRI patients. We retrospectively reviewed all clinical prostate MRIs and identified 330 negative mpMRI scans, followed by biopsy results. Our proposed model was trained and validated with 10-fold cross-validation and reached the negative predicted value (NPV) of 0.99, the sensitivity of 0.88, and the specificity of 0.63 in receiver operating characteristic (ROC) analysis. Compared with results from existing methods, ours achieved 11.2% higher NPV and 87.2% higher sensitivity with a cost of 23.2% less specificity.