Biparametric Prostate MRI Research Articles

Role of contrast agent in evaluation of periprostatic invasion in prostate cancer.

Our study aims to demonstrate the detection of invasion by biparametric prostate MRI (bpMRI). The cases whose histopathological diagnosis was prostate cancer (PCa) and whose mpMRI report was reported as PIRADS 4 and 5 were evaluated retrospectively by two radiologists with different prostate imaging experiences. The images were grouped into two data sets. Dataset-1 was bpMRI, and dataset-2 was mpMRI. Two radiologists first evaluated dataset-1 independently of each other, and 1 month later, dataset-2. They recorded whether there was an invasion and where it was seen in the patients. Then, the results were compared. A total of 75 patients were included in the study. Periprostatic invasion was detected in 33 of the patients. Both the 1st reader and the 2nd reader image detected all the cases with invasion (100%) separately between dataset-1 and set-2. Compatibility for image dataset-1 and dataset-2 between both readers was observed to be excellent. There is no need to use contrast agent to evaluate periprostatic invasion and to have an idea about local staging in PCa patients.

Assessing deep learning reconstruction for faster prostate MRI: visual vs. diagnostic performance metrics

ObjectiveDeep learning (DL) MRI reconstruction enables fast scan acquisition with good visual quality, but the diagnostic impact is often not assessed because of large reader study requirements. This study used existing diagnostic DL to assess the diagnostic quality of reconstructed images.Materials and methodsA retrospective multisite study of 1535 patients assessed biparametric prostate MRI between 2016 and 2020. Likely clinically significant prostate cancer (csPCa) lesions (PI-RADS ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge$$\\end{document} 4) were delineated by expert radiologists. T2-weighted scans were retrospectively undersampled, simulating accelerated protocols. DL reconstruction (DLRecon) and diagnostic DL detection (DLDetect) were developed. The effect on the partial area under (pAUC), the Free-Response Operating Characteristic (FROC) curve, and the structural similarity (SSIM) were compared as metrics for diagnostic and visual quality, respectively. DLDetect was validated with a reader concordance analysis. Statistical analysis included Wilcoxon, permutation, and Cohen’s kappa tests for visual quality, diagnostic performance, and reader concordance.ResultsDLRecon improved visual quality at 4- and 8-fold (R4, R8) subsampling rates, with SSIM (range: −1 to 1) improved to 0.78 ± 0.02 (p < 0.001) and 0.67 ± 0.03 (p < 0.001) from 0.68 ± 0.03 and 0.51 ± 0.03, respectively. However, diagnostic performance at R4 showed a pAUC FROC of 1.33 (CI 1.28–1.39) for DL and 1.29 (CI 1.23–1.35) for naive reconstructions, both significantly lower than fully sampled pAUC of 1.58 (DL: p = 0.024, naïve: p = 0.02). Similar trends were noted for R8.ConclusionDL reconstruction produces visually appealing images but may reduce diagnostic accuracy. Incorporating diagnostic AI into the assessment framework offers a clinically relevant metric essential for adopting reconstruction models into clinical practice.Clinical relevance statementIn clinical settings, caution is warranted when using DL reconstruction for MRI scans. While it recovered visual quality, it failed to match the prostate cancer detection rates observed in scans not subjected to acceleration and DL reconstruction.

Part II: Effect of different evaluation methods to the application of a computer-aided prostate MRI detection/diagnosis (CADe/CADx) device on reader performance

IntroductionThe construction and results of a multiple-reader multiple-case prostate MRI study are described and reported to illustrate recommendations for how to standardize artificial intelligence (AI) prostate studies per the review constituting Part I1. MethodsOur previously reported approach was applied to review and report an IRB approved, HIPAA compliant multiple-reader multiple-case clinical study of 150 bi-parametric prostate MRI studies across 9 readers, measuring physician performance both with and without the use of the recently FDA cleared CADe/CADx software ProstatID. ResultsUnassisted reader AUC values ranged from 0.418 – 0.759, with AI assisted AUC values ranging from 0.507 – 0.787. This represented a statistically significant AUC improvement of 0.045 (α = 0.05). A free-response ROC (FROC) analysis similarly demonstrated a statistically significant increase in θ from 0.405 to 0.453 (α = 0.05). The standalone performance of ProstatID performed across all prostate tissues demonstrated an AUC of 0.929, while the standalone lesion level performance of ProstatID at all biopsied locations achieved an AUC of 0.710. ConclusionThis study applies and illustrates suggested reporting and standardization methods for prostate AI studies that will make it easier to understand, evaluate and compare between AI studies. Providing radiologists with the ProstatID CADe/CADx software significantly increased diagnostic performance as assessed by both ROC and free-response ROC metrics. Such algorithms have the potential to improve radiologist performance in the detection and localization of clinically significant prostate cancer.

Manual prostate MRI segmentation by readers with different experience: a study of the learning progress

ObjectiveTo evaluate the learning progress of less experienced readers in prostate MRI segmentation.Materials and methodsOne hundred bi-parametric prostate MRI scans were retrospectively selected from the Göteborg Prostate Cancer Screening 2 Trial (single center). Nine readers with varying degrees of segmentation experience were involved: one expert radiologist, two experienced radiology residents, two inexperienced radiology residents, and four novices. The task was to segment the whole prostate gland. The expert’s segmentations were used as reference. For all other readers except three novices, the 100 MRI scans were divided into five rounds (cases 1–10, 11–25, 26–50, 51–76, 76–100). Three novices segmented only 50 cases (three rounds). After each round, a one-on-one feedback session between the expert and the reader was held, with feedback on systematic errors and potential improvements for the next round. Dice similarity coefficient (DSC) > 0.8 was considered accurate.ResultsUsing DSC > 0.8 as the threshold, the novices had a total of 194 accurate segmentations out of 250 (77.6%). The residents had a total of 397/400 (99.2%) accurate segmentations. In round 1, the novices had 19/40 (47.5%) accurate segmentations, in round 2 41/60 (68.3%), and in round 3 84/100 (84.0%) indicating learning progress.ConclusionsRadiology residents, regardless of prior experience, showed high segmentation accuracy. Novices showed larger interindividual variation and lower segmentation accuracy than radiology residents. To prepare datasets for artificial intelligence (AI) development, employing radiology residents seems safe and provides a good balance between cost-effectiveness and segmentation accuracy. Employing novices should only be considered on an individual basis.Clinical relevance statementEmploying radiology residents for prostate MRI segmentation seems safe and can potentially reduce the workload of expert radiologists. Employing novices should only be considered on an individual basis.Key Points• Using less experienced readers for prostate MRI segmentation is cost-effective but may reduce quality. • Radiology residents provided high accuracy segmentations while novices showed large inter-reader variability. • To prepare datasets for AI development, employing radiology residents seems safe and might provide a good balance between cost-effectiveness and segmentation accuracy while novices should only be employed on an individual basis. Graphical abstract

Bi-parametric prostate MRI with a recall system for contrast enhanced imaging: Improving accessibility while maintaining quality

PurposeTo review the efficacy of a recall system for bi-parametric non-contrast prostate MRI (bp-MRI). MethodsA bi-parametric protocol was instituted in July 2020 for all patients who had a prostate MRI requested, excluding those after treatment of prostate cancer, patients with hip prosthesis or pacemaker, and those who lived out-of-town. The protocol consisted of tri-planar T2-weighted and diffusion weighted images (DWI) (b = 50, 800 s/mm2 for ADC map; b = 1,500 s/mm2 acquired separately) in accordance with the Prostate Imaging Reporting & Data system (PI-RADS) v2.1 guidelines. After interpretation of bp-MRI exams, patients with equivocal (PI-RADS 3) lesions in peripheral zone (PZ) or any technical limitations were recalled for contrast administration. ResultsOut of 909 bp-MRI scans performed from July 2020 to April 2021, only 52 (5.7 %) were recalled, of which 46 (88.5 %) attended. Amongst these, 41/52 (78.8 %) were recalled for PZ PI-RADS 3 lesions, while the rest of 11 (21.2 %) cases were recalled for technical reasons. Mean time to subsequent recall scan was 11.6 days. On assessment of post-contrast imaging, 29/46 (63 %) cases were upgraded to PI-RADS 4 while 17/46 (37 %) remained PI-RADS 3. This system avoided contrast-agent use in 857 patients, with contrast cost savings of €64,620 (US$68,560) and 214 hours 15 minutes of scanner time was saved. This allowed 255 additional bp-MRI scans to be performed, reducing the waitlist from 1 year to 2–3 weeks. ConclusionA bi-parametric prostate MRI protocol with a robust recall system for contrast administration not only saved time eliminating the marked backlog but was also more cost efficient without compromising the quality of patient care.

A pilot study of an organised population-based testing programme for prostate cancer.

To determine the feasibility of a digitally automated population-based programme for organised prostate cancer testing (OPT) in Southern Sweden. A pilot project for a regional OPT was conducted between September 2020 and February 2021, inviting 999 randomly selected men aged 50, 56, or 62 years. Risk stratification was based on prostate-specific antigen (PSA) level, PSA density (PSAD), and bi-parametric prostate magnetic resonance imaging (MRI). Men with a PSA level of 3-99 ng/mL had an MRI, and men with elevated PSA level (≥3 ng/mL) had a urological check-up, including a digital rectal examination and transrectal ultrasonography (TRUS). Indications for targeted and/or systematic transrectal prostate biopsies were suspicious lesions on MRI (Prostate Imaging-Reporting and Data System [PI-RADS] 4-5) and/or PSAD > 0.15 ng/mL/mL. Additional indications for prostate biopsies were palpable tumours, PSA ratio < 0.1, or cancer suspicion on TRUS. Patient selection, mail correspondence, data collection, and algorithm processing were performed by an automated digital management system. Feasibility is reported descriptively. A total of 418 men had a PSA test (42%), with increasing participation rates by age (50 years, 38%; 56 years, 44%; and 62 years, 45%). Among these, 35 men (8%) had elevated PSA levels (≥3 ng/mL: one of 139, aged 50 years; 10/143, aged 56 years; and 24/146, aged 62 years). On MRI, 16 men (48%) had a negative scan (PI-RADS < 3), seven men (21%) had PI-RADS 3, nine men (27%) had PI-RADS 4, and one man (3%) had PI-RADS 5. All men with PI-RADS 4 or 5 underwent prostate biopsies, as well as two men with PI-RADS 3 due to PSAD > 0.15 ng/mL/mL or a suspicious finding on TRUS. Prostate cancer was diagnosed in 10 men. Six men underwent active treatment, whereas four men were assigned to active surveillance. Our OPT model is feasible from an operational point of view, but due to the limited scale of this study no conclusions can be made regarding the efficacy of the diagnostic model or outcome.

Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability

ObjectivesTo evaluate a fully automatic deep learning system to detect and segment clinically significant prostate cancer (csPCa) on same-vendor prostate MRI from two different institutions not contributing to training of the system.Materials and methodsIn this retrospective study, a previously bi-institutionally validated deep learning system (UNETM) was applied to bi-parametric prostate MRI data from one external institution (A), a PI-RADS distribution-matched internal cohort (B), and a csPCa stratified subset of single-institution external public challenge data (C). csPCa was defined as ISUP Grade Group ≥ 2 determined from combined targeted and extended systematic MRI/transrectal US-fusion biopsy. Performance of UNETM was evaluated by comparing ROC AUC and specificity at typical PI-RADS sensitivity levels. Lesion-level analysis between UNETM segmentations and radiologist-delineated segmentations was performed using Dice coefficient, free-response operating characteristic (FROC), and weighted alternative (waFROC). The influence of using different diffusion sequences was analyzed in cohort A.ResultsIn 250/250/140 exams in cohorts A/B/C, differences in ROC AUC were insignificant with 0.80 (95% CI: 0.74–0.85)/0.87 (95% CI: 0.83–0.92)/0.82 (95% CI: 0.75–0.89). At sensitivities of 95% and 90%, UNETM achieved specificity of 30%/50% in A, 44%/71% in B, and 43%/49% in C, respectively. Dice coefficient of UNETM and radiologist-delineated lesions was 0.36 in A and 0.49 in B. The waFROC AUC was 0.67 (95% CI: 0.60–0.83) in A and 0.7 (95% CI: 0.64–0.78) in B. UNETM performed marginally better on readout-segmented than on single-shot echo-planar-imaging.ConclusionFor same-vendor examinations, deep learning provided comparable discrimination of csPCa and non-csPCa lesions and examinations between local and two independent external data sets, demonstrating the applicability of the system to institutions not participating in model training.Clinical relevance statementA previously bi-institutionally validated fully automatic deep learning system maintained acceptable exam-level diagnostic performance in two independent external data sets, indicating the potential of deploying AI models without retraining or fine-tuning, and corroborating evidence that AI models extract a substantial amount of transferable domain knowledge about MRI-based prostate cancer assessment.Key Points• A previously bi-institutionally validated fully automatic deep learning system maintained acceptable exam-level diagnostic performance in two independent external data sets.• Lesion detection performance and segmentation congruence was similar on the institutional and an external data set, as measured by the weighted alternative FROC AUC and Dice coefficient.• Although the system generalized to two external institutions without re-training, achieving expected sensitivity and specificity levels using the deep learning system requires probability thresholds to be adjusted, underlining the importance of institution-specific calibration and quality control.

Deep learning for assessing image quality in bi-parametric prostate MRI: A feasibility study

BackgroundAlthough systems such as Prostate Imaging Quality (PI-QUAL) have been proposed for quality assessment, visual evaluations by human readers remain somewhat inconsistent, particularly among less-experienced readers. ObjectivesTo assess the feasibility of deep learning (DL) for the automated assessment of image quality in bi-parametric MRI scans and compare its performance to that of less-experienced readers. MethodsWe used bi-parametric prostate MRI scans from the PI-CAI dataset in this study. A 3-point Likert scale, consisting of poor, moderate, and excellent, was utilized for assessing image quality. Three expert readers established the ground-truth labels for the development (500) and testing sets (100). We trained a 3D DL model on the development set using probabilistic prostate masks and an ordinal loss function. Four less-experienced readers scored the testing set for performance comparison. ResultsThe kappa scores between the DL model and the expert consensus for T2W images and ADC maps were 0.42 and 0.61, representing moderate and good levels of agreement. The kappa scores between the less-experienced readers and the expert consensus for T2W images and ADC maps ranged from 0.39 to 0.56 (fair to moderate) and from 0.39 to 0.62 (fair to good). ConclusionsDeep learning (DL) can offer performance comparable to that of less-experienced readers when assessing image quality in bi-parametric prostate MRI, making it a viable option for an automated quality assessment tool. We suggest that DL models trained on more representative datasets, annotated by a larger group of experts, could yield reliable image quality assessment and potentially substitute or assist visual evaluations by human readers.

Does deep learning software improve the consistency and performance of radiologists with various levels of experience in assessing bi-parametric prostate MRI?

ObjectiveTo investigate whether commercially available deep learning (DL) software improves the Prostate Imaging-Reporting and Data System (PI-RADS) scoring consistency on bi-parametric MRI among radiologists with various levels of experience; to assess whether the DL software improves the performance of the radiologists in identifying clinically significant prostate cancer (csPCa).MethodsWe retrospectively enrolled consecutive men who underwent bi-parametric prostate MRI at a 3 T scanner due to suspicion of PCa. Four radiologists with 2, 3, 5, and > 20 years of experience evaluated the bi-parametric prostate MRI scans with and without the DL software. Whole-mount pathology or MRI/ultrasound fusion-guided biopsy was the reference. The area under the receiver operating curve (AUROC) was calculated for each radiologist with and without the DL software and compared using De Long’s test. In addition, the inter-rater agreement was investigated using kappa statistics.ResultsIn all, 153 men with a mean age of 63.59 ± 7.56 years (range 53–80) were enrolled in the study. In the study sample, 45 men (29.80%) had clinically significant PCa. During the reading with the DL software, the radiologists changed their initial scores in 1/153 (0.65%), 2/153 (1.3%), 0/153 (0%), and 3/153 (1.9%) of the patients, yielding no significant increase in the AUROC (p > 0.05). Fleiss’ kappa scores among the radiologists were 0.39 and 0.40 with and without the DL software (p = 0.56).ConclusionsThe commercially available DL software does not increase the consistency of the bi-parametric PI-RADS scoring or csPCa detection performance of radiologists with varying levels of experience.

Evaluating the Downstream Revenues of a Self-Pay Bi-Parametric Prostate MRI Program

To quantify downstream healthcare utilization and revenue associated with a self-pay bi-parametric prostate MRI (bpMRI) program. Medical records of 592 patients who underwent bpMRI between August 2017 and March 2020 were examined for follow-up clinical activities. These include prostate biopsy, radical prostatectomy, external beam radiation therapy, brachytherapy, androgen deprivation therapy, CT Chest, Abdomen and Pelvis, PET/CT, MRI Pelvis, and Nuclear Medicine Bone Scans. The charges for each clinical activity were derived from the Medicare Physician Fee Schedule to conservatively estimate revenues. This patient population was further divided into four groups: Group A, patients who demonstrated an MRI lesion and underwent prostatectomy; Group B, patients who did not demonstrate lesion but underwent prostatectomy; Group C, patients who demonstrated lesion but did not undergo prostatectomy; and Group D, patients who neither demonstrated lesion nor underwent prostatectomy. Revenues for each group were categorized by Urology, Radiation Oncology and Radiology subspecialties. Conservative estimates yielded $520 of downstream revenue per patient who underwent bpMRI. Group A patients yielded 47% of total revenue ($1974 per patient). Group B patients, the smallest group, yielded $1828 per patient. Group C patients made up the largest group and grossed $398 per patient. Group D demonstrated the lowest per patient revenue of $179. Groups A and B yielded most relative revenue for Urology. Group C yielded most relative revenue for Radiation Oncology, and Group D yielded most relative revenue for Radiology. A self-pay bpMRI program has the potential to improve patient access to prostate cancer screening while remaining financial sustainable.

Biparametric prostate MRI: impact of a deep learning-based software and of quantitative ADC values on the inter-reader agreement of experienced and inexperienced readers

ObjectiveTo investigate the impact of an artificial intelligence (AI) software and quantitative ADC (qADC) on the inter-reader agreement, diagnostic performance, and reporting times of prostate biparametric MRI (bpMRI) for experienced and inexperienced readers.Materials and methodsA total of 170 multiparametric MRI (mpMRI) of patients with suspicion of prostate cancer (PCa) were retrospectively reviewed by one experienced and one inexperienced reader three times, following a wash-out period. First, only the bpMRI sequences, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) sequences, and apparent diffusion coefficient (ADC) maps, were used. Then, bpMRI and quantitative ADC values were used. Lastly, bpMRI and the AI software were used. Inter-reader agreement between the two readers and between each reader and the mpMRI original reports was calculated. Detection rates and reporting times were calculated for each group.ResultsInter-reader agreement with respect to mpMRI was moderate for bpMRI, Quantib, and qADC for both the inexperienced (weighted k of 0.42, 0.45, and 0.41, respectively) and the experienced radiologists (weighted k of 0.44, 0.46, and 0.42, respectively). Detection rate of PCa was similar between the inexperienced (0.24, 0.26, and 0.23) and the experienced reader (0.26, 0.27 and 0.27), for bpMRI, Quantib, and qADC, respectively. Reporting times were lower for Quantib (8.23, 7.11, and 9.87 min for the inexperienced reader and 5.62, 5.07, and 6.21 min for the experienced reader, for bpMRI, Quantib, and qADC, respectively).ConclusionsAI and qADC did not have a significant impact on the diagnostic performance of both readers. The use of Quantib was associated with lower reporting times.

Current Value of Biparametric Prostate MRI with Machine-Learning or Deep-Learning in the Detection, Grading, and Characterization of Prostate Cancer: A Systematic Review.

Prostate cancer detection with magnetic resonance imaging is based on a standardized MRI-protocol according to the PI-RADS guidelines including morphologic imaging, diffusion weighted imaging, and perfusion. To facilitate data acquisition and analysis the contrast-enhanced perfusion is often omitted resulting in a biparametric prostate MRI protocol. The intention of this review is to analyze the current value of biparametric prostate MRI in combination with methods of machine-learning and deep learning in the detection, grading, and characterization of prostate cancer; if available a direct comparison with human radiologist performance was performed. PubMed was systematically queried and 29 appropriate studies were identified and retrieved. The data show that detection of clinically significant prostate cancer and differentiation of prostate cancer from non-cancerous tissue using machine-learning and deep learning is feasible with promising results. Some techniques of machine-learning and deep-learning currently seem to be equally good as human radiologists in terms of classification of single lesion according to the PIRADS score.

A Cascaded Deep Learning–Based Artificial Intelligence Algorithm for Automated Lesion Detection and Classification on Biparametric Prostate Magnetic Resonance Imaging

Prostate MRI improves detection of clinically significant prostate cancer; however, its diagnostic performance has wide variation. Artificial intelligence (AI) has the potential to assist radiologists in the detection and classification of prostatic lesions. Herein, we aimed to develop and test a cascaded deep learning detection and classification system trained on biparametric prostate MRI using PI-RADS for assisting radiologists during prostate MRI read out. T2-weighted, diffusion-weighted (ADC maps, high b value DWI) MRI scans obtained at 3 Tesla from two institutions (n=1043 in-house and n=347 Prostate-X, respectively) acquired between 2015 to 2019 were used for model training, validation, testing. All scans were retrospectively reevaluated by one radiologist. Suspicious lesions were contoured and assigned a PI-RADS category. A 3D U-Net-based deep neural network was used to train an algorithm for automated detection and segmentation of prostate MRI lesions. Two 3D residual neural network were used for a 4-class classification task to predict PI-RADS categories 2 to 5 and BPH. Training and validation used 89% (n=1290 scans) of the data using 5 fold cross-validation, the remaining 11% (n=150 scans) were used for independent testing. Algorithm performance at lesion level was assessed using sensitivities, positive predictive values (PPV), false discovery rates (FDR), classification accuracy, Dice similarity coefficient (DSC). Additional analysis was conducted to compare AI algorithm's lesion detection performance with targeted biopsy results. Median age was 66 years (IQR=60-71), PSA 6.7 ng/ml (IQR=4.7-9.9) from in-house cohort. In the independent test set, algorithm correctly detected 111 of 198 lesions leading to 56.1% (49.3%-62.6%) sensitivity. PPV was 62.7% (95% CI 54.7%-70.7%) with FDR of 37.3% (95% CI 29.3%-45.3%). Of 79 true positive lesions, 82.3% were tumor positive at targeted biopsy, whereas of 57 false negative lesions, 50.9% were benign at targeted biopsy. Median DSC for lesion segmentation was 0.359. Overall PI-RADS classification accuracy was 30.8% (95% CI 24.6%-37.8%). Our cascaded U-Net, residual network architecture can detect, classify cancer suspicious lesions at prostate MRI with good detection, reasonable classification performance metrics.

Detection of Prostate Cancer Using Biparametric Prostate MRI, Radiomics, and Kallikreins: A Retrospective Multicenter Study of Men With a Clinical Suspicion of Prostate Cancer.

Accurate detection of clinically significant prostate cancer (csPCa), Gleason Grade Group ≥ 2, remains a challenge. Prostate MRI radiomics and blood kallikreins have been proposed as tools to improve the performance of biparametric MRI (bpMRI). To develop and validate radiomics and kallikrein models for the detection of csPCa. Retrospective. A total of 543 men with a clinical suspicion of csPCa, 411 (76%, 411/543) had kallikreins available and 360 (88%, 360/411) did not take 5-alpha-reductase inhibitors. Two data splits into training, validation (split 1: single center, n=72; split 2: random 50% of pooled datasets from all four centers), and testing (split 1: 4 centers, n=288; split 2: remaining 50%) were evaluated. A 3 T/1.5 T, TSE T2-weighted imaging, 3x SE DWI. In total, 20,363 radiomic features calculated from manually delineated whole gland (WG) and bpMRI suspicion lesion masks were evaluated in addition to clinical parameters, prostate-specific antigen, four kallikreins, MRI-based qualitative (PI-RADSv2.1/IMPROD bpMRI Likert) scores. For the detection of csPCa, area under receiver operating curve (AUC) was calculated using the DeLong's method. A multivariate analysis was conducted to determine the predictive power of combining variables. The values of P-value < 0.05 were considered significant. The highest prediction performance was achieved by IMPROD bpMRI Likert and PI-RADSv2.1 score with AUC=0.85 and 0.85 in split 1, 0.85 and 0.83 in split 2, respectively. bpMRI WG and/or kallikreins demonstrated AUCs ranging from 0.62 to 0.73 in split 1 and from 0.68 to 0.76 in split 2. AUC of bpMRI lesion-derived radiomics model was not statistically different to IMPROD bpMRI Likert score (split 1: AUC=0.83, P-value=0.306; split 2: AUC=0.83, P-value=0.488). The use of radiomics and kallikreins failed to outperform PI-RADSv2.1/IMPROD bpMRI Likert and their combination did not lead to further performance gains. 1 TECHNICAL EFFICACY: Stage 2.

A systematic review and meta-analysis of the diagnostic accuracy of biparametric prostate MRI for prostate cancer in men at risk.

Multiparametric magnetic resonance imaging (mpMRI), the use of three multiple imaging sequences, typically T2-weighted, diffusion weighted (DWI) and dynamic contrast enhanced (DCE) images, has a high sensitivity and specificity for detecting significant cancer. Current guidance now recommends its use prior to biopsy. However, the impact of DCE is currently under debate regarding test accuracy. Biparametric MRI (bpMRI), using only T2 and DWI has been proposed as a viable alternative. We conducted a contemporary systematic review and meta-analysis to further examine the diagnostic performance of bpMRI in the diagnosis of any and clinically significant prostate cancer. A systematic review of the literature from 01/01/2017 to 06/07/2019 was performed by two independent reviewers using predefined search criteria. The index test was biparametric MRI and the reference standard whole-mount prostatectomy or prostate biopsy. Quality of included studies was assessed by the QUADAS-2 tool. Statistical analysis included pooled diagnostic performance (sensitivity; specificity; AUC), meta-regression of possible covariates and head-to-head comparisons of bpMRI and mpMRI where both were performed in the same study. Forty-four articles were included in the analysis. The pooled sensitivity for any cancer detection was 0.84 (95% CI, 0.80-0.88), specificity 0.75 (95% CI, 0.68-0.81) for bpMRI. The summary ROC curve yielded a high AUC value (AUC = 0.86). The pooled sensitivity for clinically significant prostate cancer was 0.87 (95% CI, 0.78-0.93), specificity 0.72 (95% CI, 0.56-0.84) and the AUC value was 0.87. Meta-regression analysis revealed no difference in the pooled diagnostic estimates between bpMRI and mpMRI. This meta-analysis on contemporary studies shows that bpMRI offers comparable test accuracies to mpMRI in detecting prostate cancer. These data are broadly supportive of the bpMRI approach but heterogeneity does not allow definitive recommendations to be made. There is a need for prospective multicentre studies of bpMRI in biopsy naïve men.

The Added Value of Dynamic Contrast-enhanced Sequences in Prostate MRI.

HomeRadiology: Imaging CancerVol. 2, No. 5 PreviousNext Research HighlightsFree AccessThe Added Value of Dynamic Contrast-enhanced Sequences in Prostate MRISamuel J Withey, Ashik AmlaniSamuel J Withey, Ashik AmlaniSamuel J WitheyAshik AmlaniPublished Online:Sep 25 2020https://doi.org/10.1148/rycan.2020209031MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In Take-Away Points■ Major Focus: To assess the additional value of dynamic contrast-enhanced (DCE) sequences above T2-weighted and diffusion-weighted imaging (DWI) at MRI for suspected prostate cancer.■ Key Result: Addition of DCE to T2-weighted and DWI did not improve diagnostic accuracy.■ Impact: Noncontrast biparametric prostate MRI appears to be an acceptable alternative to multiparametric MRI in assessment of suspected prostate cancer.Prostate MRI has become an established test in assessing patients suspected of having prostate cancer and is performed prior to biopsy. Imaging allows biopsy to be targeted to areas of concern and safely avoided in patients with a low probability of clinically significant cancer. However, the use of contrast agent, while recommended in Prostate Imaging Reporting and Data System (PI-RADS) v2.1, remains contentious. If MRI could be performed without contrast agent without it having a substantial impact on diagnostic performance, it would lead to quicker, cheaper, and safer imaging.El-Shater Bosaily and colleagues compared the performance of biparametric MRI (T2-weighted and DWI) with multiparametric MRI (T2-weighted, DWI, and DCE) using systematic prostate biopsies as the ground truth. This was a large, multicenter, prospective study including 497 biopsy-naive men referred for investigation of suspected prostate cancer. Patients underwent prostate MRI with a 1.5-T system using a pelvic phased-array coil. Radiology assessment was on the likelihood of clinically significant prostate cancer being present. The prostate was divided into 12 segments, each of which was scored using a five-point Likert scale. Scores were assigned to the following imaging groups: (a) T2-weighted alone, (b) T2-weighted with DWI, and (c) T2-weighted, DWI, and DCE, with no retrospective revisions allowed. Overall Likert score was based on the highest segment score given. A limitation of the study was that MRI examinations received only a single read at their local center by radiologists of varying experience. While the radiologists were blinded to the histopathologic results, they had knowledge of the serum prostate specific antigen level, which could inflate the performance of MRI.A major strength of this work was the robust reference standard, with transperineal mapping biopsies performed at 5-mm intervals in all patients. Two pathologic definitions for clinically significant cancer were used: (a) Gleason score of greater than or equal to 4 + 3 or greater than or equal to 6-mm maximum length of any grade, and a lower threshold and (b) Gleason score of greater than or equal to 3 + 4 or greater than or equal to 4-mm maximum length of any grade.For both pathologic criteria, there was no significant difference in sensitivity, specificity, positive predictive value, or negative predictive value when using biparametric MRI versus multiparametric MRI. All (n = 53) dominant Gleason pattern 4 lesions were identified (scored Likert ≥3) with multiparametric MRI compared with 98.1% (52 of 53) without use of DCE. There were fewer Likert 3 reports using multiparametric MRI compared with biparametric MRI (28% and 32%, respectively), indicating that there can be a role for DCE, particularly in more equivocal cases.This well-designed study encourages us to question the necessity of performing DCE sequences as part of standard prostate MRI. Further work is required to determine if similar results will be seen when using PI-RADS v2.1, as one would expect.Highlighted ArticleEl-Shater Bosaily A, Frangou E, Ahmed HU, et al. Additional value of dynamic contrast-enhanced sequences in multiparametric prostate magnetic resonance imaging: data from the PROMIS study. Eur Urol 2020;78(4):503–513. doi: https://doi.org/10.1016/j.eururo.2020.03.002Highlighted ArticleEl-Shater Bosaily A, Frangou E, Ahmed HU, et al. Additional value of dynamic contrast-enhanced sequences in multiparametric prostate magnetic resonance imaging: data from the PROMIS study. Eur Urol 2020;78(4):503–513. doi: https://doi.org/10.1016/j.eururo.2020.03.002 Crossref, Medline, Google ScholarArticle HistoryPublished online: Sept 25 2020 FiguresReferencesRelatedDetailsRecommended Articles The Current State of MR Imaging–targeted Biopsy Techniques for Detection of Prostate CancerRadiology2017Volume: 285Issue: 2pp. 343-356Abbreviated Biparametric Prostate MR Imaging in Men with Elevated Prostate-specific AntigenRadiology2017Volume: 285Issue: 2pp. 493-505Cost-effectiveness of MR Imaging–guided Strategies for Detection of Prostate Cancer in Biopsy-Naive MenRadiology2017Volume: 285Issue: 1pp. 157-166Focal Therapy for Prostate Cancer with In-Bore MR–guided Focused Ultrasound: Two-Year Follow-up of a Phase I Trial—Complications and Functional OutcomesRadiology2017Volume: 285Issue: 2pp. 620-628Prostate Cancer: PI-RADS Version 2 Helps Preoperatively Predict Clinically Significant CancersRadiology2016Volume: 280Issue: 1pp. 108-116See More RSNA Education Exhibits When There is No Rectum: CT-Guided Targeted and Sextant Prostate Biopsy Digital Posters2019Everything a Radiologist Should Know About European Prostate Cancer Guidelines: A Multimodal ReviewDigital Posters2020Clinical Implementation of MRI-Ultrasound Fusion-Guided Prostate Biopsy in the Management of Prostate Cancer: What Radiologists Should KnowDigital Posters2019 RSNA Case Collection Anterior transition zone prostate adenocarcinomaRSNA Case Collection2020Peripheral Zone Prostate Cancer with PseudocapsuleRSNA Case Collection2022Locally advanced, metastatic prostate adenocarcinomaRSNA Case Collection2020 Vol. 2, No. 5 Metrics Altmetric Score PDF download

275 - Accuracy and variation of biparametric prostate MRI reporting across a range of reader experience: The global BooMeR Study - initial results

275 - Accuracy and variation of biparametric prostate MRI reporting across a range of reader experience: The global BooMeR Study - initial results

Roundtable: arguments in support of using multi-parametric prostate MRI protocol.

There is increasing evidence to suggest the value of bi-parametric prostate MRI to replace multi-parametric MRI, which includes the dynamic contrast enhancement (DCE) sequence. In this review, we discuss the value of DCE in select scenarios, specifically in resolving equivocal cases, improving the diagnostic accuracy in the inexperienced reader, rescuing exams in the settings of failed T2W and DWI, detecting biochemical recurrence, while imposing minimal to no risk to the patient with respect to IV gadolinium use, specifically group II agents.

Round table: arguments in supporting abbreviated or biparametric MRI of the prostate protocol.

Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 update, in the attempt to improve clinical guidelines for multiparametric magnetic resonance imaging (mpMRI) of the prostate, has clear limitations. The role of dynamic contrast-enhanced sequences is not defined, precise guidance on the clinical management (biopsy or clinical surveillance) for score 3 lesions [equivocal for clinical significant prostate cancer (sPCa)] is not offered and criteria for lesions interpretation remain difficult and subjective. We report criteria and arguments in supporting the use of abbreviated or biparametric prostate MRI protocol in clinical practice for detection and management of PCa.

Biparametric prostate MRI and clinical indicators predict clinically significant prostate cancer in men with “gray zone” PSA levels

PurposeTo predict clinically significant prostate cancer (cs-PCa) by combining the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) score based on biparametric magnetic resonance imaging (bp-MRI) and clinical indicators in men with prostate-specific antigen (PSA) levels in the gray zone of 4−10 ng/mL. MethodWe retrospectively analyzed 364 patients with elevated PSA levels in the gray zone who had pathologically confirmed disease and had undergone MRI examinations from January 2015 to October 2019; a training group (n = 255) and validation group (n = 109) were randomly established. Multivariate logistic regression analysis of the training group was performed to identify the independent predictors for cs-PCa, thereby establishing a predictive model that was evaluated in the training and validation groups by analyzing the receiver operating characteristic (ROC) curve. ResultsIn the training group, the PI-RADS v2 score and prostate volume (PV) were independent predictors of cs-PCa (P < 0.05). The prediction model comprising the PI-RADS v2 score and PV had a larger AUC than the other predictors alone in the training group. The diagnostic sensitivity and specificity of the prediction model were 84.1 % and 83.4 %, respectively. The prediction model was indicated to have better predictive performance in the validation group. ConclusionsThe prediction model exhibits a satisfactory predictive value for cs-PCa in men with PSA levels in the gray zone. PI-RADS v2 is the strongest univariate predictor for the detection of cs-PCa in men with PSA in the gray zone, but combining this with the PV can provide superior predictive ability.