Improving Prostate MR Image Quality in Practice - Initial results from the ACR Prostate MR Image Quality Improvement Collaborative.

OBJECTIVE: Variability in prostate MRI quality is an increasingly recognized problem that negatively affects patient care. This report aims to describe the results and key learnings of the first cohort of the ACR Learning Network Prostate MR Image Quality Improvement Collaborative. METHODS: Teams from five organizations in the U.S. were trained on a structured improvement method. After reaching a consensus on image quality and auditing their images using the Prostate Imaging Quality (PI-QUAL) system, teams conducted a current state analysis to identify barriers to obtaining high-quality images. Through plan-do-study-act cycles involving frontline staff, each site designed and tested interventions targeting image quality key drivers. The percentage of exams meeting quality criteria (i.e., PI-QUAL score ≥ 4) was plotted on a run chart, and project progress was reviewed in weekly meetings. At the collaborative level, the goal was to increase the percentage of exams with PI-QUAL ≥ 4 to at least 85%. RESULTS: Across 2380 exams audited, the mean weekly rates of prostate MR exams meeting image quality criteria increased from 67% (range: 60-74%) at baseline to 87% (range: 80-97%) upon program completion. The most commonly employed interventions were MR protocol adjustments, development and implementation of patient preparation instructions, personell training and development of an auditing process mechanism. CONCLUSION: A Learning Network model, where organizations share knowledge and work together toward a common goal, can improve prostate MR image quality at multiple sites simultaneously. The inaugural cohort's key learnings provide a roadmap for improvement on a broader scale.

New Prostate MRI Scoring Systems (PI-QUAL, PRECISE, PI-RR, and PI-FAB): AJR Expert Panel Narrative Review.

Multiparametric MRI (mpMRI), interpreted using PI-RADS, improves the initial detection of clinically significant prostate cancer (PCa). Prostate MR image quality has increasingly recognized relevance to the use of mpMRI for PCa diagnosis. Additionally, mpMRI is increasingly used in scenarios beyond initial detection, including active surveillance and assessment for local recurrence after prostatectomy, radiation therapy, or focal therapy. Acknowledging these evolving demands, specialized prostate MRI scoring systems beyond PI-RADS have emerged, to address distinct scenarios and unmet needs. Examples include Prostate Imaging Quality (PI-QUAL) for assessment of image quality of mpMRI, Prostate Cancer Radiologic Estimation of Change in Sequential Evaluation (PRECISE) recommendations for evaluation of serial mpMRI examinations during active surveillance, Prostate Imaging for Recurrence Reporting System (PI-RR) for assessment for local recurrence after prostatectomy or radiation therapy, and Prostate Imaging after Focal Ablation (PI-FAB) for assessment for local recurrence after focal therapy. These systems' development and early uptake signal a compelling shift towards prostate MRI standardization in different scenarios, and ongoing research will help refine their roles in practice. This AJR Expert Panel Narrative Review critically examines these new prostate MRI scoring systems (PI-QUAL, PRECISE, PI-RR, and PI-FAB), analyzing the available evidence, delineating current limitations, and proposing solutions for improvement.

Enhancing prostate MRI expertise: educational strategies for radiologists.

The adoption of multiparametric MRI (mpMRI) and the Prostate Imaging Reporting and Data System has significantly changed prostate cancer diagnosis and management. These advancements, alongside novel biomarkers and updated International Society of Uropathology grade groups, have improved cancer detection and prognostication. Despite this progress, varying levels of expertise in mpMRI among radiologists have resulted in inconsistent assessments, potentially leading to unnecessary procedures and diminished confidence in the modality. This review assesses the educational landscape for prostate MRI, highlighting available resources for radiologists at all professional stages. It emphasizes the need for targeted educational strategies to bridge knowledge gaps and improve patient care outcomes in prostate cancer management.

PRECISE Version 2: Updated Recommendations for Reporting Prostate Magnetic Resonance Imaging in Patients on Active Surveillance for Prostate Cancer.

BACKGROUND AND OBJECTIVE: The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) recommendations standardise the reporting of prostate magnetic resonance imaging (MRI) in patients on active surveillance (AS) for prostate cancer. An international consensus group recently updated these recommendations and identified the areas of uncertainty. METHODS: A panel of 38 experts used the formal RAND/UCLA Appropriateness Method consensus methodology. Panellists scored 193 statements using a 1-9 agreement scale, where 9 means full agreement. A summary of agreement, uncertainty, or disagreement (derived from the group median score) and consensus (determined using the Interpercentile Range Adjusted for Symmetry method) was calculated for each statement and presented for discussion before individual rescoring. KEY FINDINGS AND LIMITATIONS: Participants agreed that MRI scans must meet a minimum image quality standard (median 9) or be given a score of 'X' for insufficient quality. The current scan should be compared with both baseline and previous scans (median 9), with the PRECISE score being the maximum from any lesion (median 8). PRECISE 3 (stable MRI) was subdivided into 3-V (visible) and 3-NonV (nonvisible) disease (median 9). Prostate Imaging Reporting and Data System/Likert ≥3 lesions should be measured on T2-weighted imaging, using other sequences to aid in the identification (median 8), and whenever possible, reported pictorially (diagrams, screenshots, or contours; median 9). There was no consensus on how to measure tumour size. More research is needed to determine a significant size increase (median 9). PRECISE 5 was clarified as progression to stage ≥T3a (median 9). CONCLUSIONS AND CLINICAL IMPLICATIONS: The updated PRECISE recommendations reflect expert consensus opinion on minimal standards and reporting criteria for prostate MRI in AS. PATIENT SUMMARY: The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) recommendations are used in clinical practice and research to guide the interpretation and reporting of magnetic resonance imaging for patients on active surveillance for prostate cancer. An international panel has updated these recommendations, clarified the areas of uncertainty, and highlighted the areas for further research.

PI-RADS Upgrading Rules: Impact on Prostate Cancer Detection and Biopsy Avoidance of MRI-Directed Diagnostic Pathways.

Background: PI-RADS incorporate rules by which ancillary sequence findings upgrade a dominant score to a higher final category. Evidence on the upgrading rules' impact on diagnostic pathways remains scarce. Objective: To evaluate the clinical net benefit of the PI-RADS upgrading rules in MRI-directed diagnostic pathways. Methods: This study was a retrospective analysis of a prospectively maintained clinical registry. The study included patients without known prostate cancer who underwent prostate MRI followed by prostate biopsy from January 2016 to May 2020. Clinically significant prostate cancer (csPCa) was defined as International Society of Urological Pathology (ISUP) grade group ≥2. csPCa detection was compared between dominant (i.e., no upgrade rule applied) and upgraded lesions. Decision curve analysis was used to compare the net benefit, considering the tradeoff of csPCa detection and biopsy avoidance, of MRI-directed pathways in scenarios considering and disregarding PI-RADS upgrading rules. These included biopsy-all pathway, MRI-focused pathway (no biopsy for PI-RADS ≤2), and risk-based pathway (use of PSA density ≥0.15 to select patients with PI-RADS ≤3 for biopsy). Results: The sample comprised 716 patients (mean age, 64.9 years; 93 with a PI-RADS ≤2 examination, 623 with total of 780 PI-RADS ≥3 lesions). Frequencies of csPCa were not significantly different between dominant and upgraded PI-RADS 3 transition zone lesions (20% vs 19%), dominant and upgraded PI-RADS 4 transition zone lesions (33% vs 26%), and dominant and upgraded PI-RADS 4 peripheral zone lesions (58% vs 45%) (p>.05). In the biopsy-all, per-guideline MRI-focused, MRI-focused disregarding upgrading rules, per-guideline risk-based, and risk-based disregarding upgrading rules pathways, csPCa frequency was 53%, 52%, 51%, 52%, and 48%, and biopsy avoidance was 0%, 13%, 16%, 19%, and 25%, respectively. Disregarding upgrading rules yielded 5.5 and 1.9 biopsies avoided per missed csPca for MRI-focused and risk-based pathways, respectively. At probability thresholds for biopsy selection of 7.5-30%, net benefit was highest for the per-guideline risk-based pathway. Conclusion: Disregarding PI-RADS upgrading rules reduced net clinical benefit of the risk-based MRI-directed diagnostic pathway when considering tradeoffs between csPCa detection and biopsy avoidance. Clinical Impact: This study supports the application of PI-RADS upgrading rules to optimize biopsy selection, particularly in risk-based pathways.

Quality of T2-weighted MRI re-acquisition versus deep learning GAN image reconstruction: A multi-reader study.

PURPOSE: To evaluate CycleGAN's ability to enhance T2-weighted image (T2WI) quality. METHOD: A CycleGAN algorithm was used to enhance T2WI quality. 96 patients (192 scans) were identified from patients who underwent multiple axial T2WI due to poor quality on the first attempt (RAD1) and improved quality on re-acquisition (RAD2). CycleGAN algorithm gave DL classifier scores (0-1) for quality quantification and produced enhanced versions of QI1 and QI2 from RAD1 and RAD2, respectively. A subset (n = 20 patients) was selected for a blinded, multi-reader study, where four radiologists rated T2WI on a scale of 1-4 for quality. The multi-reader study presented readers with 60 image pairs (RAD1 vs RAD2, RAD1 vs QI1, and RAD2 vs QI2), allowing for selecting sequence preferences and quantifying the quality changes. RESULTS: The DL classifier correctly discerned 71.9 % of quality classes, with 90.6 % (96/106) as poor quality and 48.8 % (42/86) as diagnostic in original sequences (RAD1, RAD2). CycleGAN images (QI1, QI2) demonstrated quantitative improvements, with consistently higher DL classifier scores than original scans (p < 0.001). In the multi-reader analysis, CycleGAN demonstrated no qualitative improvements, with diminished overall quality and motion in QI2 in most patients compared to RAD2, with noise levels remaining similar (8/20). No readers preferred QI2 to RAD2 for diagnosis. CONCLUSION: Despite quantitative enhancements with CycleGAN, there was no qualitative boost in T2WI diagnostic quality, noise, or motion. Expert radiologists didn't favor CycleGAN images over standard scans, highlighting the divide between quantitative and qualitative metrics.

Novel radiomic analysis on bi-parametric MRI for characterizing differences between MR non-visible and visible clinically significant prostate cancer.

Background: around one third of clinically significant prostate cancer (CsPCa) foci are reported to be MRI non-visible (MRI─). Objective: To quantify the differences between MR visible (MRI+) and MRI─ CsPCa using intra- and peri-lesional radiomic features on bi-parametric MRI (bpMRI). Methods: This retrospective and multi-institutional study comprised 164 patients with pre-biopsy 3T prostate multi-parametric MRI from 2014 to 2017. The MRI─ CsPCa referred to lesions with PI-RADS v2 score < 3 but ISUP grade group > 1. Three experienced radiologists were involved in annotating lesions and PI-RADS assignment. The validation set (Dv) comprised 52 patients from a single institution, the remaining 112 patients were used for training (Dt). 200 radiomic features were extracted from intra-lesional and peri-lesional regions on bpMRI.Logistic regression with least absolute shrinkage and selection operator (LASSO) and 10-fold cross-validation was applied on Dt to identify radiomic features associated with MRI─ and MRI+ CsPCa to generate corresponding risk scores RMRI─ and RMRI+. RbpMRI was further generated by integrating RMRI─ and RMRI+. Statistical significance was determined using the Wilcoxon signed-rank test. Results: Both intra-lesional and peri-lesional bpMRI Haralick and CoLlAGe radiomic features were significantly associated with MRI─ CsPCa (p < 0.05). Intra-lesional ADC Haralick and CoLlAGe radiomic features were significantly different among MRI─ and MRI+ CsPCa (p < 0.05). RbpMRI yielded the highest AUC of 0.82 (95 % CI 0.72-0.91) compared to AUCs of RMRI+ 0.76 (95 % CI 0.63-0.89), and PI-RADS 0.58 (95 % CI 0.50-0.72) on Dv. RbpMRI correctly reclassified 10 out of 14 MRI─ CsPCa on Dv. Conclusion: Our preliminary results demonstrated that both intra-lesional and peri-lesional bpMRI radiomic features were significantly associated with MRI─ CsPCa. These features could assist in CsPCa identification on bpMRI.

Optimization of non-endorectal prostate MR image quality using PI-QUAL: A multidisciplinary team approach.

PURPOSE: To evaluate the utility of the PI-QUAL score in assessing protocol changes aimed to improve image quality from a non-endorectal coil prostate MR imaging protocol during a 9-month quality improvement (QI) project and to quantify the inter-reader agreement of PI-QUAL scores between radiologists, technologists, and physicists. METHODS: This retrospective study audited 1,012 multiparametric prostate MRI examinations as part of a national QI project according to the PI-QUAL standard. PI-QUAL scores were used to inform MR protocol changes. Following the project, 4 radiologists, 2 technologists, and 1 medical physicist collectively audited an additional set of 150 examinations to identify statistical improvements in image quality using the two-tailed Wilcoxon rank sum test. The improvements due to individual protocol changes were assessed among subsets of the 1,012 examinations which compared examinations occurring before and after the isolated protocol change. Inter-reader variability was assessed using the percent majority agreement and the average standard deviation of PI-QUAL scores between evaluators. RESULTS: During this QI project, PI-QUAL scores improved from 3.67 ± 0.75 to 4.16 ± 0.59 (p < 0.01) after implementing a series of protocol changes. Among a subset of 451 cases, we found that adopting R/L rather than A/P phase encoding reduced distortion in diffusion-weighted imaging (DW) from 21.6% (41/190 A/P phase encoded cases) to 11.5% (30/261 R/L phase encoded cases) (p < 0.01). Similarly, in the same 451 cases, adopting R/L phase encoding in T2WI reduced breathing motion artifacts from 34.6% (94/272 A/P phase encoding cases) to 12.8% (23/179 R/L phase encoding cases) (p < 0.01). DWI wraparound artifact was mitigated by employing a full-pelvis shim and enabling the abdomen shim option. The occurrence of low signal-to-noise ratio was reduced from 19.4% (19/98 cases without a weight-based threshold) to 6.3% (10/160) by instituting a weight-based threshold for using an endorectal coil (p < 0.01). The percent majority agreement was similar between radiologists, technologists and physicists, and all evaluators combined (72%, 77%, and 67%, respectively). CONCLUSIONS: PI-QUAL can evaluate image quality changes resulting from protocol optimizations at both the exam- and series-levels. With training, radiologists, technologists, and physicists can perform PI-QUAL scoring with similar performance. Broadening the scope of the quality improvement team can result in meaningful and lasting change.

Taking PI-QUAL beyond the prostate: Towards a standardized radiological image quality score (RI-QUAL).

PURPOSE: To compare the interreader agreement of a novel quality score, called the Radiological Image Quality Score (RI-QUAL), to a slighly modified version of the existing Prostate Imaging Quality (mPI-QUAL) score for magnetic resonance imaging (MRI) of the prostate. METHODS: A total of 43 consecutive scans were evaluated by two subspecialized radiologists who assigned scores using both the RI-QUAL and mPI-QUAL methods. The interreader agreement was analyzed using three statistical methods: concordance correlation coefficient (CCC), intraclass correlation coefficient (ICC), and Cohen's kappa. Time needed to arrive at a quality judgment was measured and compared using the Wilcoxon signed rank test. RESULTS: The interreader agreement for RI-QUAL and mPI-QUAL scores was comparable, as evidenced by the high CCC (0.76 vs. 0.77, p = 0.93), ICC (0.86 vs. 0.87, p = 0.93), and moderate Cohen's kappa (0.61 vs. 0.64, p = 0.85) values. Moreover, RI-QUAL assessment was faster than mPI-QUAL (19 vs. 40 s, p = 0.001). CONCLUSION: RI-QUAL is a new quality score that has comparable interreader agreement to the mPI-QUAL score, but with the potential to be applied to different MRI protocols and even different modalities. Like PI-QUAL, RI-QUAL may also facilitate communication about quality to referring physicians, as it provides a standardized and easily interpretable score. Further studies are warranted to validate the usefulness of RI-QUAL in larger patient cohorts and for other imaging modalities.

American College of Radiology initiatives on prostate magnetic resonance imaging quality.

Magnetic resonance imaging (MRI) has become integral to diagnosing and managing patients with suspected or confirmed prostate cancer. However, the benefits of utilizing MRI can be hindered by quality issues during imaging acquisition, interpretation, and reporting. As the utilization of prostate MRI continues to increase in clinical practice, the variability in MRI quality and how it can negatively impact patient care have become apparent. The American College of Radiology (ACR) has recognized this challenge and developed several initiatives to address the issue of inconsistent MRI quality and ensure that imaging centers deliver high-quality patient care. These initiatives include the Prostate Imaging Reporting and Data System (PI-RADS), developed in collaboration with an international panel of experts and members of the European Society of Urogenital Radiology (ESUR), the Prostate MR Image Quality Improvement Collaborative, which is part of the ACR Learning Network, the ACR Prostate Cancer MRI Center Designation, and the ACR Appropriateness Criteria. In this article, we will discuss the importance of these initiatives in establishing quality assurance and quality control programs for prostate MRI and how they can improve patient outcomes.

ACR Appropriateness Criteria® Staging of Renal Cell Carcinoma: 2022 Update.

Renal cell carcinoma is a complex group of highly heterogenous renal tumors demonstrating variable biological behavior. Pretreatment imaging of renal cell carcinoma involves accurate assessment of the primary tumor, presence of nodal, and distant metastases. CT and MRI are the key imaging modalities used in the staging of renal cell carcinoma. Important imaging features that impact treatment include tumor extension into renal sinus and perinephric fat, involvement of pelvicalyceal system, infiltration into adrenal gland, involvement of renal vein and inferior vena cava, as well as the presence of metastatic adenopathy and distant metastases. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Region-specific deep learning models for accurate segmentation of rectal structures on post-chemoradiation T2w MRI: a multi-institutional, multi-reader study.

Introduction: For locally advanced rectal cancers, in vivo radiological evaluation of tumor extent and regression after neoadjuvant therapy involves implicit visual identification of rectal structures on magnetic resonance imaging (MRI). Additionally, newer image-based, computational approaches (e.g., radiomics) require more detailed and precise annotations of regions such as the outer rectal wall, lumen, and perirectal fat. Manual annotations of these regions, however, are highly laborious and time-consuming as well as subject to inter-reader variability due to tissue boundaries being obscured by treatment-related changes (e.g., fibrosis, edema). Methods: This study presents the application of U-Net deep learning models that have been uniquely developed with region-specific context to automatically segment each of the outer rectal wall, lumen, and perirectal fat regions on post-treatment, T2-weighted MRI scans. Results: In multi-institutional evaluation, region-specific U-Nets (wall Dice = 0.920, lumen Dice = 0.895) were found to perform comparably to multiple readers (wall inter-reader Dice = 0.946, lumen inter-reader Dice = 0.873). Additionally, when compared to a multi-class U-Net, region-specific U-Nets yielded an average 20% improvement in Dice scores for segmenting each of the wall, lumen, and fat; even when tested on T2-weighted MRI scans that exhibited poorer image quality, or from a different plane, or were accrued from an external institution. Discussion: Developing deep learning segmentation models with region-specific context may thus enable highly accurate, detailed annotations for multiple rectal structures on post-chemoradiation T2-weighted MRI scans, which is critical for improving evaluation of tumor extent in vivo and building accurate image-based analytic tools for rectal cancers.

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.

The Feasibility and Value of a Focal Therapy Multidisciplinary Tumor Board, Including Radiographic and Pathological Overreads in Refining the Selection for High Intensity Focused Ultrasound in Prostate Cancer Patients.

INTRODUCTION: Focal therapy for prostate cancer is increasingly recognized as an acceptable therapeutic option in well-selected men. A focal therapy multidisciplinary tumor board geared toward improving patient selection is a novel concept which has not been reported. We describe our institution's initial experience with a multidisciplinary tumor board for focal therapy and its outcomes in terms of patient selection. METHODS: This was a single-center, prospective study of patients referred to a multidisciplinary tumor board. All prostate MRIs were re-reviewed by a single radiologist with >10 years of experience, and the number, size, location, and Prostate Imaging Reporting & Data System scores of lesions visible on MRI were recorded and compared to the original report. Outside histopathology, when requested, was also re-reviewed for cancer grade groups and adverse pathological features. A descriptive statistical analysis was performed. RESULTS: Seventy-four patients were presented at our multidisciplinary tumor board (January-October 2022). Sixty-seven patients were treatment naïve, while 7 had prior radiation±androgen deprivation therapy. MRI overread was performed on all treatment-naïve patients (67/74 [91%]), while pathology overreads were performed on 14/74 (19.9%). Following multidisciplinary tumor board, 19 patients (25.6%) were deemed suitable candidates for focal therapy. A total of 24 patients (35.8%) were not deemed candidates for high intensity focused ultrasound focal therapy based exclusively on findings identified at MRI overread. Pathology re-review changed management for 3/14 patients, with two-thirds being downgraded to grade group 1 disease and opting for active surveillance. CONCLUSIONS: Multidisciplinary tumor board for focal therapy is feasible. MRI overread is an essential component of this process and demonstrates significant findings that alter eligibility or management in over a third of patients.

The ACR Learning Network: Facilitating Local Performance Improvement Through Shared Learning.

PURPOSE: The ACR Learning Network was established to test the viability of the learning network model in radiology. In this report, the authors review the learning network concept, introduce the ACR Learning Network and its components, and report progress to date and plans for the future. METHODS: Patterned after institutional programs developed by the principal investigator, the ACR Learning Network was composed of four distinct improvement collaboratives. Initial participating sites were solicited through broad program advertisement. Candidate programs were selected on the basis of assessments of local leadership support, experience with quality improvement initiatives, intraorganizational relationships, and access to data and analytic support. Participation began with completing a 27-week formal quality improvement training and project support program, with local teams reporting weekly progress on a common performance measure. RESULTS: Four improvement collaborative topics were chosen for the initial cohort with the following numbers of participating sites: mammography positioning (6), prostate MR image quality (6), lung cancer screening (6), and follow-up on recommendations for management of incidental findings (4). To date, all sites have remained actively engaged and have progressed in an expected fashion. A detailed report of the results of the improvement phase will be provided in a future publication. CONCLUSIONS: To date, the ACR Learning Network has successfully achieved planned milestones outlined in the program's plan, with preparation under way for the second and third cohorts. By providing a shared platform for improvement training and knowledge sharing, the authors are optimistic that the network may facilitate widespread performance improvement in radiology on a number of topics for years to come.

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.

OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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. ADVANCES IN KNOWLEDGE: Prostate MRI and genomic testing may help identify patients with adverse pathology.