Quality of Prostate MRI: Is the PI-RADS Standard Sufficient?

Quality of prostate MRI in early diagnosis-a national survey and reading evaluation

OBJECTIVES

The reliability of image-based recommendations in the prostate cancer pathway is partially dependent on prostate MRI image quality. We evaluated the current compliance with PI-RADSv2.1 technical recommendations and the prostate MRI image quality in the Netherlands. To aid image quality improvement, we identified factors that possibly influence image quality.

MATERIALS AND METHODS

A survey was sent to 68 Dutch medical centres to acquire information on prostate MRI acquisition. The responding medical centres were requested to provide anonymised prostate MRI examinations of biopsy-naive men suspected of prostate cancer. The images were evaluated for quality by three expert prostate radiologists. The compliance with PI-RADSv2.1 technical recommendations and the PI-QUALv2 score was calculated. Relationships between hardware, education of personnel, technical parameters, and/or patient preparation and both compliance and image quality were analysed using Pearson correlation, Mann-Whitney U-test, or Student's t-test where appropriate.

RESULTS

Forty-four medical centres submitted their compliance with PI-RADSv2.1 technical recommendations, and 26 medical centres completed the full survey. Thirteen hospitals provided 252 usable images. The mean compliance with technical recommendations was 79%. Inadequate PI-QUALv2 scores were given in 30.9% and 50.6% of the mp-MRI and bp-MRI examinations, respectively. Multiple factors with a possible relationship with image quality were identified.

CONCLUSION

In the Netherlands, the average compliance with PI-RADSv2.1 technical recommendations is high. Prostate MRI image quality was inadequate in 30-50% of the provided examinations. Many factors not covered in the PI-RADSv2.1 technical recommendations can influence image quality. Improvement of prostate MRI image quality is needed.

CRITICAL RELEVANCE STATEMENT

It is essential to improve the image quality of prostate MRIs, which can be achieved by addressing factors not covered in the PI-RADSv2.1 technical recommendations.

KEY POINTS

Prostate MRI image quality influences the diagnostic accuracy of image-based decisions. Thirty to fifty percent of Dutch prostate MRI examinations were of inadequate image quality. We identified multiple factors with possible influence on image quality.

Risk of clinically significant prostate cancer undercategorized by multiparametric magnetic resonance imaging

BACKGROUND

To investigative potential clinicopathological characteristics and imaging-related risk factors of clinically significant prostate cancer (csPCa) undercategorized in patients with negative or equivocal MRI.

METHODS

This retrospective study included 581 patients with pathologically confirmed csPCa (Gleason score ≥ 3 + 4), including 108 undercategorized csPCa and 473 detected csPCa. All patients underwent multiparametric MRI (mpMRI). The undercategorized csPCa was defined as a MRI result with PI-RADS ≤ 3. The clinicopathological characteristics and imaging-related factors were compared between the undercategorized group(Group A) (PI-RADS 1-3) and detected group (Group B) (PI-RADS 4-5).

RESULTS

The age, total PSA levels, PSAD, free PSA, prostate imaging quality (PI-QUAL) scores, and Gleason scores were significantly lower in the Group A than Group B. The lesions were larger and involved in peripheral and transition zones in the Group B. A significant difference in the second reading opinion. Age (odds ratio [OR], 0.94), PSAD (OR, 0.09), and PI-QUAL scores (OR, 0.25) were significantly associated with the undercategorized csPCa. The rate of undercategorized csPCa with these three risk factors (age, PSAD, and PI-QUAL scores of < 71, < 0.355, and < 3, respectively) was 68.62%. The lack of zoomed-DWI resulted in lower PI-QUAL scores. Finally, the probability of undercategorized csPCa without zoomed DWI was 3.186 times higher than that with zoomed DWI when the PSAD ratio is lower than 0.355.

CONCLUSIONS

Low image quality, younger age, and lower PSAD contribute to csPCa undercategorized by mpMRI. Moreover, the use of zoomed DWI decreased undercategorized csPCa by improving PI-QUAL scores of MRI images.

PI-QUAL version 2 image quality categorisation and inter-reader agreement compared to version 1

OBJECTIVES

Prostate imaging quality (PI-QUAL) was developed to standardise the evaluation of prostate MRI quality and has recently been updated to version 2. This study aims to assess inter-reader agreement for PI-QUAL v1 and v2 scores and investigates changes in MRI quality score categories.

MATERIALS AND METHODS

The study retrospectively analysed 350 multiparametric MRI (mpMRI) scans. Two expert uroradiologists independently assessed mpMRI quality using PI-QUAL v1 and v2 guidelines. Biparametric MRI (bpMRI) categorisation based on PI-QUAL v2 included only T2WI and diffusion-weighted imaging (DWI) results. Inter-reader agreement was determined using percentage agreement and kappa, and categorisation comparisons were made using the chi-square test.

RESULTS

Substantial inter-reader agreement was observed for the overall PI-QUAL v1 score (κ = 0.64) and moderate agreement for v2 mpMRI (κ = 0.54) and v2 bpMRI scores (κ = 0.57). Inter-reader agreements on individual sequences were similar between v1 and v2 (kappa for individual sequences: T2WI, 0.46 and 0.49; DWI, 0.66 and 0.70; DCE, 0.71 and 0.61). Quality levels shifted from predominantly "optimal" in v1 (65%) down to "acceptable" using v2 (55%); p < 0.001. The addition of DCE increased the proportion of cases with at least "adequate" quality at mpMRI (64%) compared to bpMRI (30%); p < 0.001.

CONCLUSION

This study shows consistent inter-reader agreement between PI-QUAL v1 and v2, encompassing overall and individual sequence categorisation. A notable shift from "optimal" to "acceptable" quality was demonstrated when moving from v1 to v2, with DCE tending improving quality from "inadequate" (bpMRI) to "acceptable" (mpMRI).

KEY POINTS

Question What are the agreement levels of image quality of prostate MRI by using PI-QUAL v1 and v2? Findings Inter-reader agreement based on PI-QUAL v1 and v2 is comparable. Dynamic contrast enhancement (DCE) enables an overall shift from inadequate quality (at bpMRI) to acceptable quality (mpMRI). Clinical relevance The inter-reader agreement on PI-QUAL v1 and v2 is equivalent. PI-QUAL v2 assesses prostate bpMRI as well as mpMRI quality. Transitioning from inadequate to acceptable between v2-bpMRI and v2-mpMRI highlights the role of DCE as an "image quality safety net."

PI-QUAL version 2: an update of a standardised scoring system for the assessment of image quality of prostate MRI

Multiparametric MRI is the optimal primary investigation when prostate cancer is suspected, and its ability to rule in and rule out clinically significant disease relies on high-quality anatomical and functional images. Avenues for achieving consistent high-quality acquisitions include meticulous patient preparation, scanner setup, optimised pulse sequences, personnel training, and artificial intelligence systems. The impact of these interventions on the final images needs to be quantified. The prostate imaging quality (PI-QUAL) scoring system was the first standardised quantification method that demonstrated the potential for clinical benefit by relating image quality to cancer detection ability by MRI. We present the updated version of PI-QUAL (PI-QUAL v2) which applies to prostate MRI performed with or without intravenous contrast medium using a simplified 3-point scale focused on critical technical and qualitative image parameters. CLINICAL RELEVANCE STATEMENT: High image quality is crucial for prostate MRI, and the updated version of the PI-QUAL score (PI-QUAL v2) aims to address the limitations of version 1. It is now applicable to both multiparametric MRI and MRI without intravenous contrast medium. KEY POINTS: High-quality images are essential for prostate cancer diagnosis and management using MRI. PI-QUAL v2 simplifies image assessment and expands its applicability to prostate MRI without contrast medium. PI-QUAL v2 focuses on critical technical and qualitative image parameters and emphasises T2-WI and DWI.

External Validation of a Previously Developed Deep Learning-based Prostate Lesion Detection Algorithm on Paired External and In-House Biparametric MRI Scans

Purpose To evaluate the performance of an artificial intelligence (AI) model in detecting overall and clinically significant prostate cancer (csPCa)-positive lesions on paired external and in-house biparametric MRI (bpMRI) scans and assess performance differences between each dataset. Materials and Methods This single-center retrospective study included patients who underwent prostate MRI at an external institution and were rescanned at the authors' institution between May 2015 and May 2022. A genitourinary radiologist performed prospective readouts on in-house MRI scans following the Prostate Imaging Reporting and Data System (PI-RADS) version 2.0 or 2.1 and retrospective image quality assessments for all scans. A subgroup of patients underwent an MRI/US fusion-guided biopsy. A bpMRI-based lesion detection AI model previously developed using a completely separate dataset was tested on both MRI datasets. Detection rates were compared between external and in-house datasets with use of the paired comparison permutation tests. Factors associated with AI detection performance were assessed using multivariable generalized mixed-effects models, incorporating features selected through forward stepwise regression based on the Akaike information criterion. Results The study included 201 male patients (median age, 66 years [IQR, 62-70 years]; prostate-specific antigen density, 0.14 ng/mL2 [IQR, 0.10-0.22 ng/mL2]) with a median interval between external and in-house MRI scans of 182 days (IQR, 97-383 days). For intraprostatic lesions, AI detected 39.7% (149 of 375) on external and 56.0% (210 of 375) on in-house MRI scans (P < .001). For csPCa-positive lesions, AI detected 61% (54 of 89) on external and 79% (70 of 89) on in-house MRI scans (P < .001). On external MRI scans, better overall lesion detection was associated with a higher PI-RADS score (odds ratio [OR] = 1.57; P = .005), larger lesion diameter (OR = 3.96; P < .001), better diffusion-weighted MRI quality (OR = 1.53; P = .02), and fewer lesions at MRI (OR = 0.78; P = .045). Better csPCa detection was associated with a shorter MRI interval between external and in-house scans (OR = 0.58; P = .03) and larger lesion size (OR = 10.19; P < .001). Conclusion The AI model exhibited modest performance in identifying both overall and csPCa-positive lesions on external bpMRI scans. Keywords: MR Imaging, Urinary, Prostate Supplemental material is available for this article. © RSNA, 2024.

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 United States 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 examinations meeting quality criteria (ie, 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 examinations with PI-QUAL ≥4 to at least 85%.

RESULTS

Across 2,380 examinations audited, the mean weekly rates of prostate MR examinations 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, personnel training, and development of an auditing process mechanism.

CONCLUSION

A learning network model, in which 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 road map for improvement on a broader scale.

Picture Perfect: The Status of Image Quality in Prostate MRI

Magnetic resonance imaging is the gold standard imaging modality for the diagnosis of prostate cancer (PCa). Image quality is a fundamental prerequisite for the ability to detect clinically significant disease. In this critical review, we separate the issue of image quality into quality improvement and quality assessment. Beginning with the evolution of technical recommendations for scan acquisition, we investigate the role of patient preparation, scanner factors, and more advanced sequences, including those featuring Artificial Intelligence (AI), in determining image quality. As means of quality appraisal, the published literature on scoring systems (including the Prostate Imaging Quality score), is evaluated. Finally, the application of AI and teaching courses as ways to facilitate quality assessment are discussed, encouraging the implementation of future image quality initiatives along the PCa diagnostic and monitoring pathway. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Deep Learning-Based T2-Weighted MR Image Quality Assessment and Its Impact on Prostate Cancer Detection Rates

BACKGROUND

Image quality evaluation of prostate MRI is important for successful implementation of MRI into localized prostate cancer diagnosis.

PURPOSE

To examine the impact of image quality on prostate cancer detection using an in-house previously developed artificial intelligence (AI) algorithm.

STUDY TYPE

Retrospective.

SUBJECTS

615 consecutive patients (median age 67 [interquartile range [IQR]: 61-71] years) with elevated serum PSA (median PSA 6.6 [IQR: 4.6-9.8] ng/mL) prior to prostate biopsy.

FIELD STRENGTH/SEQUENCE

3.0T/T2-weighted turbo-spin-echo MRI, high b-value echo-planar diffusion-weighted imaging, and gradient recalled echo dynamic contrast-enhanced.

ASSESSMENTS

Scans were prospectively evaluated during clinical readout using PI-RADSv2.1 by one genitourinary radiologist with 17 years of experience. For each patient, T2-weighted images (T2WIs) were classified as high-quality or low-quality based on evaluation of both general distortions (eg, motion, distortion, noise, and aliasing) and perceptual distortions (eg, obscured delineation of prostatic capsule, prostatic zones, and excess rectal gas) by a previously developed in-house AI algorithm. Patients with PI-RADS category 1 underwent 12-core ultrasound-guided systematic biopsy while those with PI-RADS category 2-5 underwent combined systematic and targeted biopsies. Patient-level cancer detection rates (CDRs) were calculated for clinically significant prostate cancer (csPCa, International Society of Urological Pathology Grade Group ≥2) by each biopsy method and compared between high- and low-quality images in each PI-RADS category.

STATISTICAL TESTS

Fisher's exact test. Bootstrap 95% confidence intervals (CI). A P value <0.05 was considered statistically significant.

RESULTS

385 (63%) T2WIs were classified as high-quality and 230 (37%) as low-quality by AI. Targeted biopsy with high-quality T2WIs resulted in significantly higher clinically significant CDR than low-quality images for PI-RADS category 4 lesions (52% [95% CI: 43-61] vs. 32% [95% CI: 22-42]). For combined biopsy, there was no significant difference in patient-level CDRs for PI-RADS 4 between high- and low-quality T2WIs (56% [95% CI: 47-64] vs. 44% [95% CI: 34-55]; P = 0.09).

DATA CONCLUSION

Higher quality T2WIs were associated with better targeted biopsy clinically significant cancer detection performance for PI-RADS 4 lesions. Combined biopsy might be needed when T2WI is lower quality.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

Deep Learning-Based Interpretable AI for Prostate T2W MRI Quality Evaluation

RATIONALE AND OBJECTIVES

Prostate MRI quality is essential in guiding prostate biopsies. However, assessment of MRI quality is subjective with variation. Quality degradation sources exert varying impacts based on the sequence under consideration, such as T2W versus DWI. As a result, employing sequence-specific techniques for quality assessment could yield more advantageous outcomes. This study aims to develop an AI tool that offers a more consistent evaluation of T2W prostate MRI quality, efficiently identifying suboptimal scans while minimizing user bias.

MATERIALS AND METHODS

This retrospective study included 1046 patients from three cohorts (ProstateX [n = 347], All-comer in-house [n = 602], enriched bad-quality MRI in-house [n = 97]) scanned between January 2011 and May 2022. An expert reader assigned T2W MRIs a quality score. A train-validation-test split of 70:15:15 was applied, ensuring equal distribution of MRI scanners and protocols across all partitions. T2W quality AI classification model was based on 3D DenseNet121 architecture using MONAI framework. In addition to multiclassification, binary classification was utilized (Classes 0/1 vs. 2). A score of 0 was given to scans considered non-diagnostic or unusable, a score of 1 was given to those with acceptable diagnostic quality with some usability but with some quality distortions present, and a score of 2 was given to those considered optimal diagnostic quality and usability. Partial occlusion sensitivity maps were generated for anatomical correlation. Three body radiologists assessed reproducibility within a subgroup of 60 test cases using weighted Cohen Kappa.

RESULTS

The best validation multiclass accuracy of 77.1% (121/157) was achieved during training. In the test dataset, multiclassification accuracy was 73.9% (116/157), whereas binary accuracy was 84.7% (133/157). Sub-class sensitivity for binary quality distortion classification for class 0 was 100% (18/18), and sub-class specificity for T2W classification of absence/minimal quality distortions for class 2 was 90.5% (95/105). All three readers showed moderate to substantial agreement with ground truth (R1-R3 κ = 0.588, κ = 0.649, κ = 0.487, respectively), moderate to substantial agreement with each other (R1-R2 κ = 0.599, R1-R3 κ = 0.612, R2-R3 κ = 0.685), fair to moderate agreement with AI (R1-R3 κ = 0.445, κ = 0.410, κ = 0.292, respectively). AI showed substantial agreement with ground truth (κ = 0.704). 3D quality heatmap evaluation revealed that the most critical non-diagnostic quality imaging features from an AI perspective related to obscuration of the rectoprostatic space (94.4%, 17/18).

CONCLUSION

The 3D AI model can assess T2W prostate MRI quality with moderate accuracy and translate whole sequence-level classification labels into 3D voxel-level quality heatmaps for interpretation. Image quality has a significant downstream impact on ruling out clinically significant cancers. AI may be able to help with reproducible identification of MRI sequences requiring re-acquisition with explainability.

Interactive training workshop to improve prostate mpMRI knowledge: results from the ESOR Nicholas Gourtsoyiannis teaching fellowship

PURPOSE

Prostate MRI is established for the investigation of patients presenting with suspected early prostate cancer. Outcomes are dependent on both image quality and interpretation. This study assessed the impact of an educational intervention on participants' theoretical knowledge of the technique.

METHODS

Eighty-one clinicians from two centers with varying experience in prostate MRI participated. Baseline knowledge was assessed with 10 written and image-based multiple-choice questions (MCQs) prior to a course including didactic lectures and hands-on interactive workshops on prostate MRI interpretation. Post-course, participants completed a second 10-question MCQ test, matched by format, themes, and difficulty, to assess for any improvement in knowledge and performance. Results were assessed using the Wilcoxon rank sum test, and the Wilcoxon signed-rank test for paired data.

RESULTS

Thirty-nine participants, including 25/49 (51.0%) and 14/32 (43.8%) at each center completed both assessments, with their results used for subsequent evaluation. Overall, there was a significant improvement from pre- (4.92 ± 2.41) to post-course scores (6.77 ± 1.46), p < 0.001 and at both Copenhagen (5.92 ± 2.25 to 7.36 ± 1.25) and Toronto (3.14 ± 1.51 to 5.71 ± 1.20); p = 0.005 and p = 0.002, respectively. Participants with no prostate MRI experience showed the greatest improvement (3.77 ± 1.97 to 6.18 ± 1.5, p < 0.001), followed by intermediate level (< 500 MRIs reported) experience (6.18 ± 1.99 to 7.46 ± 1.13, p = 0.058), then advanced (> 500 MRIs reported) experience (6.83 ± 2.48 to 7.67 ± 0.82, p = 0.339).

CONCLUSIONS

A dedicated prostate MRI teaching course combining didactic lectures and hands-on workshops significantly improved short-term theoretical knowledge of the technique for clinicians with differing levels of experience.

CRITICAL RELEVANCE STATEMENT

A dedicated teaching course significantly improved theoretical knowledge of the technique particularly for clinicians with less reporting experience and a lower baseline knowledge. The multiple-choice questions format mapped improved performance and may be considered as part of future MRI certification initiatives.

KEY POINTS

• Prostate MRI knowledge is important for image interpretation and optimizing acquisition sequences. • A dedicated teaching course significantly improved theoretical knowledge of the technique. • Improved performance was more apparent in clinicians with less reporting experience and a lower baseline knowledge.

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.

Update on Optimization of Prostate MR Imaging Technique and Image Quality

Prostate MR imaging quality has improved dramatically over recent times, driven by advances in hardware, software, and improved functional imaging techniques. MRI now plays a key role in prostate cancer diagnostic work-up, but outcomes of the MRI-directed pathway are heavily dependent on image quality and optimization. MR sequences can be affected by patient-related degradations relating to motion and susceptibility artifacts which may enable only partial mitigation. In this Review, we explore issues relating to prostate MRI acquisition and interpretation, mitigation strategies at a patient and scanner level, PI-QUAL reporting, and future directions in image quality, including artificial intelligence solutions.

The PRECISE Recommendations for Prostate MRI in Patients on Active Surveillance for Prostate Cancer: A Critical Review

The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) recommendations were published in 2016 to standardize the reporting of MRI examinations performed to assess for disease progression in patients on active surveillance for prostate cancer. Although a limited number of studies have reported outcomes from use of PRECISE in clinical practice, the available studies have demonstrated PRECISE to have high pooled NPV but low pooled PPV for predicting progression. Our experience in using PRECISE in clinical practice at two teaching hospitals has highlighted issues with its application and areas requiring clarification. This Clinical Perspective critically appraises PRECISE on the basis of this experience, focusing on the system's key advantages and disadvantages and exploring potential changes to improve the system's utility. These changes include consideration of image quality when applying PRECISE scoring, incorporation of quantitative thresholds for disease progression, adoption of a PRECISE 3F sub-category for progression not qualifying as substantial, and comparisons with both the baseline and most recent prior examinations. Items requiring clarification include derivation of a patient-level score in patients with multiple lesions, intended application of PRECISE score 5 (i.e., if requiring development of disease that is no longer organ-confined), and categorization of new lesions in patients with prior MRI-invisible disease.

The role of AI in prostate MRI quality and interpretation: Opportunities and challenges

Prostate MRI plays an important role in imaging the prostate gland and surrounding tissues, particularly in the diagnosis and management of prostate cancer. With the widespread adoption of multiparametric magnetic resonance imaging in recent years, the concerns surrounding the variability of imaging quality have garnered increased attention. Several factors contribute to the inconsistency of image quality, such as acquisition parameters, scanner differences and interobserver variabilities. While efforts have been made to standardize image acquisition and interpretation via the development of systems, such as PI-RADS and PI-QUAL, the scoring systems still depend on the subjective experience and acumen of humans. Artificial intelligence (AI) has been increasingly used in many applications, including medical imaging, due to its ability to automate tasks and lower human error rates. These advantages have the potential to standardize the tasks of image interpretation and quality control of prostate MRI. Despite its potential, thorough validation is required before the implementation of AI in clinical practice. In this article, we explore the opportunities and challenges of AI, with a focus on the interpretation and quality of prostate MRI.

Radiomics based automated quality assessment for T2W prostate MR images

PURPOSE

The guidelines for prostate cancer recommend the use of MRI in the prostate cancer pathway. Due to the variability in prostate MR image quality, the reliability of this technique in the detection of prostate cancer is highly variable in clinical practice. This leads to the need for an objective and automated assessment of image quality to ensure an adequate acquisition and hereby to improve the reliability of MRI. The aim of this study is to investigate the feasibility of Blind/referenceless image spatial quality evaluator (Brisque) and radiomics in automated image quality assessment of T2-weighted (T2W) images.

METHOD

Anonymized axial T2W images from 140 patients were scored for quality using a five-point Likert scale (low, suboptimal, acceptable, good, very good quality) in consensus by two readers. Images were dichotomized into clinically acceptable (very good, good and acceptable quality images) and clinically unacceptable (low and suboptimal quality images) in order to train and verify the model. Radiomics and Brisque features were extracted from a central cuboid volume including the prostate. A reduced feature set was used to fit a Linear Discriminant Analysis (LDA) model to predict image quality. Two hundred times repeated 5-fold cross-validation was used to train the model and test performance by assessing the classification accuracy, the discrimination accuracy as receiver operating curve - area under curve (ROC-AUC), and by generating confusion matrices.

RESULTS

Thirty-four images were classified as clinically unacceptable and 106 were classified as clinically acceptable. The accuracy of the independent test set (mean ± standard deviation) was 85.4 ± 5.5%. The ROC-AUC was 0.856 (0.851 - 0.861) (mean; 95% confidence interval).

CONCLUSIONS

Radiomics AI can automatically detect a significant portion of T2W images of suboptimal image quality. This can help improve image quality at the time of acquisition, thus reducing repeat scans and improving diagnostic accuracy.

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.

Quality assessment of prostate MRI by PI-QUAL score: Inter-reader agreement and impact on prostate cancer local staging at 3 Tesla

PURPOSE

To evaluate whether the Prostate Imaging Quality (PI-QUAL) score impacts prostate cancer (PCa) staging on MRI. The secondary goal was to test inter-reader agreement among radiologists experienced in prostate imaging.

METHOD

A retrospective, single-center study with patients who underwent 3 Tesla prostate MRI scans and were submitted to radical prostatectomy (RP) between January 2018 and November 2021 and were eligible for our study. Extraprostatic extension (EPE) data were collected from original MR reports (EPEm) and pathological reports of RP specimens (EPEp). Three expert prostate radiologists (ESUR/ESUI criteria R1, R2, R3) independently evaluated all MRI exams according to PI-QUAL score for image quality (1 to 5; 1: poor, 5: excellent), blinded to original imaging reports and clinical data. We studied the diagnostic performance of MRI using pooled data from PI-QUAL scores (≤3 vs. ≥4). We also performed univariate and multivariate analyses to assess the PI-QUAL score impact on local PCa staging. Cohen's K and Tau-b Kendall tests were used to assess the inter-reader agreement for PI-QUAL score, T2WI, DWI, and DCE.

RESULTS

Our final cohort included 146 patients, of which 27.4% presented EPE on pathology. We observed no impact of imaging quality on accuracy for EPE prediction: AUC of 0.750 (95% CI 0.26-1) for PI-QUAL ≤ 3 and 0.705 (95% CI 0.618-0.793) for PI-QUAL ≥ 4. The multivariate analysis demonstrated a correlation of EPEm (OR 3.25, p 0.001) and ISUP grade group (OR 1.89, p 0.012) to predict EPEp. The inter-reader agreement was moderate to substantial (0.539 for R1-R2, 0.522 for R2-R3, and 0.694 for R1-R3).

CONCLUSION

Our clinical impact evaluation showed no direct correlation between MRI quality by PI-QUAL score and accuracy in detecting EPE in patients undergoing RP. Additionally, we had moderate to a substantial inter-reader agreement for the PI-QUAL score.

Evaluating the Utility of Prostate-Specific Antigen Density in Risk Stratification of PI-RADS 3 Peripheral Zone Lesions on Non-Contrast-Enhanced Prostate MRI: An Exploratory Single-Institution Study

Objective This study aimed to explore the potential of prostate-specific antigen density (PSAD) as a supplementary tool for defining high-risk Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions in the peripheral zone on non-contrast-enhanced MRI. This additional stratification tool could supplement the decision-making process for biopsy, potentially helping in identifying higher-risk patients more accurately, minimizing unnecessary procedures in lower-risk patients, and limiting the need for dynamic contrast-enhanced (DCE) scans. Materials and methods Between January 2019 and April 2023, 30 patients with PI-RADS 3 lesions underwent MRI-ultrasound fusion biopsies at our institution. Age and PSAD values were investigated using logistic regression and chi-square automatic interaction detection (CHAID) analysis to discern their predictive value for malignancy. Results The mean patient age was 64.7 years, and the mean PSAD was 0.13 ng/mL2. Logistic regression demonstrated PSAD to be a significant predictor of cancer (p=0.012), but not age (p=0.855). CHAID analysis further identified a PSAD cut-off value of 0.12, below which the cancer detection rate was 23.1% and above which the rate increased to 76.5%. Conclusions This exploratory study suggests that PSAD might be utilized to enhance the stratification of high-risk PI-RADS 3 lesions in the peripheral zone on non-contrast-enhanced MRI, aiding in decision-making for biopsy. While biopsy remains the gold standard for definitive diagnosis, a high PSAD value may suggest a greater need for biopsy in this specific group. Although further validation in larger cohorts is required, our findings contribute to the ongoing discourse on optimizing PI-RADS 3 lesion management. Limitations include a small sample size, the retrospective nature of the study, and the single-center setting, which may impact the generalizability of our results.

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

BACKGROUND

Although 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.

OBJECTIVES

To 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.

METHODS

We 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.

RESULTS

The 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).

CONCLUSIONS

Deep 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.

Inter-reader agreement of the prostate imaging quality (PI-QUAL) score for basic readers in prostate MRI: A multi-center study

BACKGROUND

The Prostate Imaging Quality (PI-QUAL) score is the first step toward image quality assessment in multi-parametric prostate MRI (mpMRI). Previous studies have demonstrated moderate to excellent inter-rater agreement among expert readers; however, there is a need for studies to assess the inter-reader agreement of PI-QUAL scoring in basic prostate readers.

OBJECTIVES

To assess the inter-reader agreement of the PI-QUAL score amongst basic prostate readers on multi-center prostate mpMRI.

METHODS

Five basic prostate readers from different centers assessed the PI-QUAL scores independently using T2-weighted images, diffusion-weighted imaging (DWI) including apparent diffusion coefficient (ADC) maps, and dynamic-contrast-enhanced (DCE) images on mpMRI data obtained from five different centers following Prostate Imaging-Reporting and Data System Version 2.1. The inter-reader agreements amongst radiologists for PI-QUAL were evaluated using weighted Cohen's kappa. Further, the absolute agreements in assessing the diagnostic adequacy of each mpMRI sequence were calculated.

RESULTS

A total of 355 men with a median age of 71 years (IQR, 60-78) were enrolled in the study. The pair-wise kappa scores ranged from 0.656 to 0.786 for the PI-QUAL scores, indicating good inter-reader agreements between the readers. The pair-wise absolute agreements ranged from 0.75 to 0.88 for T2W imaging, from 0.74 to 0.83 for the ADC maps, and from 0.77 to 0.86 for DCE images.

CONCLUSIONS

Basic prostate radiologists from different institutions provided good inter-reader agreements on multi-center data for the PI-QUAL scores.

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.

Effect of a dedicated PI-QUAL curriculum on the assessment of prostate MRI quality

PURPOSE

The Prostate Imaging Quality (PI-QUAL) score is a metric to evaluate the diagnostic quality of multiparametric magnetic resonance imaging (MRI) of the prostate. This study evaluated the impact of a prostate MRI quality training lecture on the participant's ability to assess prostate MRI image quality.

METHODS

Eighteen in-training-radiologists of varying experience in reviewing diagnostic prostate MRI assessed the image quality of ten examinations. Then, they attended a dedicated lecture on MRI quality assessment using the PI-QUAL score. After the lecture, the same participants evaluated the image quality of a new set of ten scans applying the PI-QUAL score. Results were assessed using receiver operating characteristic (ROC) analysis. The reference standard was the PI-QUAL score assessed by a fellowship trained abdominal radiologist with experience in reading prostate MRI.

RESULTS

There was a significant improvement in the average area under the curve (AUC) for assessment of prostate MRI image quality from baseline (0.82; [0.576 - 0.888]) to post teaching (1.0; [0.954-1]), with an improvement of 0.18 (p < 0.03). When ROC curves were computed for different cohorts stratified based on year of training, difference ranged from 0.48 for second year residents to 0.32 for fourth year residents (p < 0.001-0.01). For abdominal imaging fellows, the pre-teaching AUC was 0.9 [0.557-1] and post teaching AUC was 1 [0.957-1], a difference of 0.1 (p = 0.20).

CONCLUSIONS

A dedicated lecture on PI-QUAL improved the ability of radiologists-in-training to assess prostate MRI image quality, with variable impact depending on year of training.

Prostate MRI and image Quality: It is time to take stock

Multiparametric magnetic resonance imaging (mpMRI) plays a vital role in prostate cancer diagnosis and management. With the increase in use of mpMRI, obtaining the best possible quality images has become a priority. The Prostate Imaging Reporting and Data System (PI-RADS) was introduced to standardize and optimize patient preparation, scanning techniques, and interpretation. However, the quality of the MRI sequences depends not only on the hardware/software and scanning parameters, but also on patient-related factors. Common patient-related factors include bowel peristalsis, rectal distension, and patient motion. There is currently no consensus regarding the best approaches to address these issues and improve the quality of mpMRI. New evidence has been accrued since the release of PI-RADS, and this review aims to explore the key strategies which aim to improve prostate MRI quality, such as imaging techniques, patient preparation methods, the new Prostate Imaging Quality (PI-QUAL) criteria, and artificial intelligence on prostate MRI quality.

The Low Rate of Adherence to Checklist for Artificial Intelligence in Medical Imaging Criteria Among Published Prostate MRI Artificial Intelligence Algorithms

OBJECTIVE

To determine the rigor, generalizability, and reproducibility of published classification and detection artificial intelligence (AI) models for prostate cancer (PCa) on MRI using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines, a 42-item checklist that is considered a measure of best practice for presenting and reviewing medical imaging AI research.

MATERIALS AND METHODS

This review searched English literature for studies proposing PCa AI detection and classification models on MRI. Each study was evaluated with the CLAIM checklist. The additional outcomes for which data were sought included measures of AI model performance (eg, area under the curve [AUC], sensitivity, specificity, free-response operating characteristic curves), training and validation and testing group sample size, AI approach, detection versus classification AI, public data set utilization, MRI sequences used, and definition of gold standard for ground truth. The percentage of CLAIM checklist fulfillment was used to stratify studies into quartiles. Wilcoxon's rank-sum test was used for pair-wise comparisons.

RESULTS

In all, 75 studies were identified, and 53 studies qualified for analysis. The original CLAIM items that most studies did not fulfill includes item 12 (77% no): de-identification methods; item 13 (68% no): handling missing data; item 15 (47% no): rationale for choosing ground truth reference standard; item 18 (55% no): measurements of inter- and intrareader variability; item 31 (60% no): inclusion of validated interpretability maps; item 37 (92% no): inclusion of failure analysis to elucidate AI model weaknesses. An AUC score versus percentage CLAIM fulfillment quartile revealed a significant difference of the mean AUC scores between quartile 1 versus quartile 2 (0.78 versus 0.86, P = .034) and quartile 1 versus quartile 4 (0.78 versus 0.89, P = .003) scores. Based on additional information and outcome metrics gathered in this study, additional measures of best practice are defined. These new items include disclosure of public dataset usage, ground truth definition in comparison to other referenced works in the defined task, and sample size power calculation.

CONCLUSION

A large proportion of AI studies do not fulfill key items in CLAIM guidelines within their methods and results sections. The percentage of CLAIM checklist fulfillment is weakly associated with improved AI model performance. Additions or supplementations to CLAIM are recommended to improve publishing standards and aid reviewers in determining study rigor.

Impact of Prostate Urethral Lift Device on Prostate Magnetic Resonance Image Quality

PURPOSE

Prostatic urethral lift with UroLift is a minimally invasive approach to treat symptomatic benign prostatic hypertrophy. This device causes artifacts on prostate magnetic resonance images. Our aim was to evaluate the impact of artifact on prostate magnetic resonance image quality.

MATERIALS AND METHODS

This was a single-center retrospective review of patients with UroLift who subsequently had prostate magnetic resonance imaging. Two readers graded UroLift artifact on each pulse sequence using a 5-point scale (1-nondiagnostic; 5-no artifact). Prostate Imaging Quality scores were assigned for the whole data set. The volume of gland obscured by artifact was measured. Linear and logistic regression models were used to identify predictors of poor image quality.

RESULTS

Thirty-seven patients were included. Poor image quality occurs more in the transition zone than the peripheral zone (15% vs 3%), at base/mid regions vs the apex (13%, 9%, and 5%, respectively) and on diffusion-weighted images vs T2-weighted and dynamic contrast-enhanced sequences (27%, 0.3%, 0%, respectively; P < .001). Suboptimal image quality (ie, Prostate Imaging Quality score <2) was found in 16%-24% of exams. The percentage of gland obscured by the UroLift artifact was higher on diffusion-weighted images and dynamic contrast-enhanced sequences than T2-weighted (32%, 9%, and 6%, respectively; P < .001).

CONCLUSIONS

UroLift artifact negatively affects prostate magnetic resonance image quality with greater impact in the mid-basal transition zone, obscuring a third of the gland on diffusion-weighted images. Patients considering this procedure should be counseled on the impact of this device on image quality and its potential implications for any image-guided prostate cancer workup.

[Prostate Imaging Reporting and Data System (PI-RADS) v 2.1: Overview and Critical Points]

The technical parameters and imaging interpretation criteria of the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) using multiparametric MRI (mpMRI) are updated in PI-RADS v2.1. These changes have been an expected improvement for prostate cancer evaluation, although some issues remain unsolved, and new issues have been raised. In this review, a brief overview of PI-RADS v2.1 is and several critical points are discussed as follows: the need for more detailed protocols of mpMRI, lack of validation of the revised transition zone interpretation criteria, the need for clarification for the revised diffusion-weighted imaging and dynamic contrast-enhanced imaging criteria, anterior fibromuscular stroma and central zone assessment, assessment of background signal and tumor aggressiveness, changes in the structured report, the need for the parameters for imaging quality and performance control, and indications for expansion of the system to include other indications.

Quality checkpoints in the MRI-directed prostate cancer diagnostic pathway

Multiparametric MRI of the prostate is now recommended as the initial diagnostic test for men presenting with suspected prostate cancer, with a negative MRI enabling safe avoidance of biopsy and a positive result enabling MRI-directed sampling of lesions. The diagnostic pathway consists of several steps, from initial patient presentation and preparation to performing and interpreting MRI, communicating the imaging findings, outlining the prostate and intra-prostatic target lesions, performing the biopsy and assessing the cores. Each component of this pathway requires experienced clinicians, optimized equipment, good inter-disciplinary communication between specialists, and standardized workflows in order to achieve the expected outcomes. Assessment of quality and mitigation measures are essential for the success of the MRI-directed prostate cancer diagnostic pathway. Quality assurance processes including Prostate Imaging-Reporting and Data System, template biopsy, and pathology guidelines help to minimize variation and ensure optimization of the diagnostic pathway. Quality control systems including the Prostate Imaging Quality scoring system, patient-level outcomes (such as Prostate Imaging-Reporting and Data System MRI score assignment and cancer detection rates), multidisciplinary meeting review and audits might also be used to provide consistency of outcomes and ensure that all the benefits of the MRI-directed pathway are achieved.

Automated Patient-level Prostate Cancer Detection with Quantitative Diffusion Magnetic Resonance Imaging

Background

Multiparametric magnetic resonance imaging (mpMRI) improves detection of clinically significant prostate cancer (csPCa), but the subjective Prostate Imaging Reporting and Data System (PI-RADS) system and quantitative apparent diffusion coefficient (ADC) are inconsistent. Restriction spectrum imaging (RSI) is an advanced diffusion-weighted MRI technique that yields a quantitative imaging biomarker for csPCa called the RSI restriction score (RSI_rs).

Objective

To evaluate RSI_rs for automated patient-level detection of csPCa.

Design setting and participants

We retrospectively studied all patients (n = 151) who underwent 3 T mpMRI and RSI (a 2-min sequence on a clinical scanner) for suspected prostate cancer at University of California San Diego during 2017-2019 and had prostate biopsy within 180 d of MRI.

Intervention

We calculated the maximum RSI_rs and minimum ADC within the prostate, and obtained PI-RADS v2.1 from medical records.

Outcome measurements and statistical analysis

We compared the performance of RSI_rs, ADC, and PI-RADS for the detection of csPCa (grade group ≥2) on the best available histopathology (biopsy or prostatectomy) using the area under the curve (AUC) with two-tailed α = 0.05. We also explored whether the combination of PI-RADS and RSI_rs might be superior to PI-RADS alone and performed subset analyses within the peripheral and transition zones.

Results and limitations

AUC values for ADC, RSI_rs, and PI-RADS were 0.48 (95% confidence interval: 0.39, 0.58), 0.78 (0.70, 0.85), and 0.77 (0.70, 0.84), respectively. RSI_rs and PI-RADS were each superior to ADC for patient-level detection of csPCa (p < 0.0001). RSI_rs alone was comparable with PI-RADS (p = 0.8). The combination of PI-RADS and RSI_rs had an AUC of 0.85 (0.78, 0.91) and was superior to either PI-RADS or RSI_rs alone (p < 0.05). Similar patterns were seen in the peripheral and transition zones.

Conclusions

RSI_rs is a promising quantitative marker for patient-level csPCa detection, warranting a prospective study.

Patient summary

We evaluated a rapid, advanced prostate magnetic resonance imaging technique called restriction spectrum imaging to see whether it could give an automated score that predicted the presence of clinically significant prostate cancer. The automated score worked about as well as expert radiologists' interpretation. The combination of the radiologists' scores and automated score might be better than either alone.

Role of the Prostate Imaging Quality PI-QUAL Score for Prostate Magnetic Resonance Image Quality in Pathological Upstaging After Radical Prostatectomy: A Multicentre European Study

Background

Increasing use of multiparametric magnetic resonance imaging (mpMRI) has come with heterogeneity in image quality. The Prostate Imaging Quality (PI-QUAL) score is under scrutiny to assess its usefulness in predicting clinical outcomes.

Objective

To compare upstaging of localized disease on mpMRI (mrT2) to locally invasive disease in radical prostatectomy (RP) specimens (≥pT3a) in relation to PI-QUAL.

Design setting and participants

Patients treated with RP between 2015 and 2020 who underwent 1.5-3-T mpMRI within 6 mo before surgery and had systematic and mpMRI-US targeted biopsies were included. mpMRI scans were retrospectively assigned a PI-QUAL score, and prospectively acquired Prostate Imaging-Recording and Data System (PI-RADS) scores (version 2.0 or 2.1) were used. PI-QUAL scores were categorized as nondiagnostic (PI-QUAL <3), sufficient (PI-QUAL 3), or optimal (PI-QUAL >3).

Outcome measurements and statistical analysis

We assessed the relationship between the PI-QUAL score and upstaging using multivariate logistic regression. mpMRI, clinical, and pathological findings were compared using χ² tests and analysis of variance.

Results and limitations

We identified 351 patients, of whom 40 (11.4%) had PI-QUAL <3, 57 (16.3%) had PI-QUAL 3, and 254 (72.3%) had PI-QUAL >3 scores. The distribution of PI-QUAL <3 (0-33.6%; p < 0.001) and PI-QUAL >3 (37.3-100%; p < 0.001) scores varied widely among centers. PI-QUAL ≥3 in comparison to PI-QUAL <3 was associated with a lower rate of upstaging (19% vs 35%; p = 0.02), greater detection of mrT3a and mrT3b prostate cancer (17.0% vs 2.5%; p = 0.016), a higher rate of PI-RADS 5 lesions (47% vs 27.5%; p = 0.002), a higher number of suspicious lesion (PI-RADS ≥3: 34.7% vs 15%; p = 0.012), and higher detection rates for aggregated (50.7% vs 22.5%; p = 0.001) and late (21.2% vs 0%; p < 0.001) extraprostatic extension. On multivariate analysis, PI-QUAL<3 was associated with more frequent upstaging in the RP specimen (odds ratio 3.4; p = 0.01).

Conclusions

In comparison to PI-QUAL ≥3, PI-QUAL <3 was significantly associated with a higher rate of upstaging from organ-confined disease on mpMRI to locally advanced disease on pathology, lower detection rates for PI-RADS 5 lesions and extraprostatic extension, and a lower number of suspicious lesions.

Patient summary

Poor image quality for magnetic resonance imaging (MRI) scans of the prostate is associated with underestimation of the stage of prostate cancer.

Prostate MRI Qualification: AJR Expert Panel Narrative Review

Prostate MRI is now established as a first-line investigation for individuals presenting with suspected localized or locally advanced prostate cancer. Successful delivery of the MRI-directed pathway for prostate cancer diagnosis relies on high-quality imaging as well as the interpreting radiologist's experience and expertise. Radiologist certification in prostate MRI may help limit interreader variability, optimize outcomes, and provide individual radiologists with documentation of meeting predefined standards. This AJR Expert Panel Narrative Review summarizes existing certification proposals, recognizing variable progress across regions in establishing prostate MRI certification programs. To our knowledge, Germany is the only country with a prostate MRI certification process that is currently available for radiologists. However, prostate MRI certification programs have also recently been proposed in the United States and United Kingdom and by European professional society consensus panels. Recommended qualification processes entail a multifaceted approach, incorporating components such as minimum case numbers, peer learning, course participation, continuing medical education credits, and feedback from pathology results. Given the diversity in health care systems, including in the provision and availability of MRI services, national organizations will likely need to take independent approaches to certification and accreditation. The relevant professional organizations should begin developing these programs or continue existing plans for implementation.

Canadian Association of Radiologists Prostate MRI White Paper

Prostate cancer is the most common malignancy and the third most common cause of death in Canadian men. In light of evolving diagnostic pathways for prostate cancer and the increased use of MRI, which now includes its use in men prior to biopsy, the Canadian Association of Radiologists established a Prostate MRI Working Group to produce a white paper to provide recommendations on establishing and maintaining a Prostate MRI Programme in the context of the Canadian healthcare system. The recommendations, which are based on available scientific evidence and/or expert consensus, are intended to maintain quality in image acquisition, interpretation, reporting and targeted biopsy to ensure optimal patient care. The paper covers technique, reporting, quality assurance and targeted biopsy considerations and includes appendices detailing suggested reporting templates, quality assessment tools and sample image acquisition protocols relevant to the Canadian healthcare context.

Utility of a Rule-Based Algorithm in the Assessment of Standardized Reporting in PI-RADS

RATIONALE AND OBJECTIVES

Adoption of the Prostate Imaging Reporting & Data System (PI-RADS) has been shown to increase detection of clinically significant prostate cancer on prostate mpMRI. We propose that a rule-based algorithm based on Regular Expression (RegEx) matching can be used to automatically categorize prostate mpMRI reports into categories as a means by which to assess for opportunities for quality improvement.

MATERIALS AND METHODS

All prostate mpMRIs performed in the Duke University Health System from January 2, 2015, to January 29, 2021, were analyzed. Exclusion criteria were applied, for a total of 5343 male patients and 6264 prostate mpMRI reports. These reports were then analyzed by our RegEx algorithm to be categorized as PI-RADS 1 through PI-RADS 5, Recurrent Disease, or "No Information Available." A stratified, random sample of 502 mpMRI reports was reviewed by a blinded clinical team to assess performance of the RegEx algorithm.

RESULTS

Compared to manual review, the RegEx algorithm achieved overall accuracy of 92.6%, average precision of 88.8%, average recall of 85.6%, and F1 score of 0.871. The clinical team also reviewed 344 cases that were classified as "No Information Available," and found that in 150 instances, no numerical PI-RADS score for any lesion was included in the impression section of the mpMRI report.

CONCLUSION

Rule-based processing is an accurate method for the large-scale, automated extraction of PI-RADS scores from the text of radiology reports. These natural language processing approaches can be used for future initiatives in quality improvement in prostate mpMRI reporting with PI-RADS.

Prostate MRI quality: clinical impact of the PI-QUAL score in prostate cancer diagnostic work-up

OBJECTIVE

To assess the reproducibility and impact of prostate imaging quality (PI-QUAL) scores in a clinical cohort undergoing prostate multiparametric MRI.

METHODS

PI-QUAL scores were independently recorded by three radiologists (two senior, one junior). Readers also recorded whether MRI was sufficient to rule-in/out cancer and if repeat imaging was required. Inter-reader agreement was assessed using Cohen's κ. PI-QUAL scores were further correlated to PI-RADS score, number of biopsy procedures, and need for repeat imaging.

RESULTS

Image quality was sufficient (≥PI-QUAL-3) in 237/247 (96%) and optimal (≥PI-QUAL-4) in 206/247 (83%) of males undergoing 3T-MRI. Overall PI-QUAL scores showed moderate inter-reader agreement for senior (K = 0.51) and junior-senior readers (K = 0.47), with DCE showing highest agreement (K = 0.47). With PI-QUAL-5 studies, the negative MRI calls increased from 50 to 87% and indeterminate PI-RADS-3 rates decreased from 31.8. to 10.4% compared to lower quality PI-QUAL-3 studies. More patients with PI-QUAL scores 1-3 underwent biopsy for negative (47%) and indeterminate probability (100%) MRIs compared to PI-QUAL score 4-5 (30 and 75%, respectively). Ability to rule-in cancer increased with PI-QUAL score, from 50% at PI-QUAL 1-2 to 90% for PI-QUAL 4-5, with a similarly, but greater effect for ruling-out cancer and at a lower threshold, from 0% for scans of PI-QUAL 1-2 to 67.1% for PI-QUAL 4 and 100% for PI-QUAL-5.

CONCLUSION

Higher PI-QUAL scores for image quality are associated with decreased uncertainty in MRI decision-making and improved efficiency of diagnostic pathway delivery.

ADVANCES IN KNOWLEDGE

This study demonstrates moderate inter-reader agreement for PI-QUAL scoring and validates the score in a clinical setting, showing correlation of image quality to certainty of decision making and clinical outcomes of repeat imaging and biopsy of low-to-intermediate risk cases.

Deep learning-based artificial intelligence applications in prostate MRI: brief summary

Prostate cancer (PCa) is the most common cancer type in males in the Western World. MRI has an established role in diagnosis of PCa through guiding biopsies. Due to multistep complex nature of the MRI-guided PCa diagnosis pathway, diagnostic performance has a big variation. Developing artificial intelligence (AI) models using machine learning, particularly deep learning, has an expanding role in radiology. Specifically, for prostate MRI, several AI approaches have been defined in the literature for prostate segmentation, lesion detection and classification with the aim of improving diagnostic performance and interobserver agreement. In this review article, we summarize the use of radiology applications of AI in prostate MRI.

Optimal biopsy approach for detection of clinically significant prostate cancer

Prostate cancer (PCa) diagnostic and therapeutic work-up has evolved significantly in the last decade, with pre-biopsy multiparametric MRI now widely endorsed within international guidelines. There is potential to move away from the widespread use of systematic biopsy cores and towards an individualised risk-stratified approach. However, the evidence on the optimal biopsy approach remains heterogeneous, and the aim of this review is to highlight the most relevant features following a critical assessment of the literature. The commonest biopsy approaches are via the transperineal (TP) or transrectal (TR) routes. The former is considered more advantageous due to its negligible risk of post-procedural sepsis and reduced need for antimicrobial prophylaxis; the more recent development of local anaesthetic (LA) methods now makes this approach feasible in the clinic. Beyond this, several techniques are available, including cognitive registration, MRI-Ultrasound fusion imaging and direct MRI in-bore guided biopsy. Evidence shows that performing targeted biopsies reduces the number of cores required and can achieve acceptable rates of detection whilst helping to minimise complications and reducing pathologist workloads and costs to health-care facilities. Pre-biopsy MRI has revolutionised the diagnostic pathway for PCa, and optimising the biopsy process is now a focus. Combining MR imaging, TP biopsy and a more widespread use of LA in an outpatient setting seems a reasonable solution to balance health-care costs and benefits, however, local choices are likely to depend on the expertise and experience of clinicians and on the technology available.

Convolutional Neural Networks for Automated Classification of Prostate Multiparametric Magnetic Resonance Imaging Based on Image Quality

Background

Prostate magnetic resonance imaging (MRI) is technically demanding, requiring high image quality to reach its full diagnostic potential. An automated method to identify diagnostically inadequate images could help optimize image quality.

Purpose

To develop a convolutional neural networks (CNNs) based analysis pipeline for the classification of prostate MRI image quality.

Study Type

Retrospective.

Subjects

Three hundred sixteen prostate mpMRI scans and 312 men (median age 67).

Field Strength/Sequence

A 3 T; fast spin echo T2WI, echo planar imaging DWI, ADC, gradient‐echo dynamic contrast enhanced (DCE).

Assessment

MRI scans were reviewed by three genitourinary radiologists (V.P., M.D.M., S.C.) with 21, 12, and 5 years of experience, respectively. Sequences were labeled as high quality (Q1) or low quality (Q0) and used as the reference standard for all analyses.

Statistical Tests

Sequences were split into training, validation, and testing sets (869, 250, and 120 sequences, respectively). Inter‐reader agreement was assessed with the Fleiss kappa. Following preprocessing and data augmentation, 28 CNNs were trained on MRI slices for each sequence. Model performance was assessed on both a per‐slice and a per‐sequence basis. A pairwise t‐test was performed to compare performances of the classifiers.

Results

The number of sequences labeled as Q0 or Q1 was 38 vs. 278 for T2WI, 43 vs. 273 for DWI, 41 vs. 275 for ADC, and 38 vs. 253 for DCE. Inter‐reader agreement was almost perfect for T2WI and DCE and substantial for DWI and ADC. On the per‐slice analysis, accuracy was 89.95% ± 0.02% for T2WI, 79.83% ± 0.04% for DWI, 76.64% ± 0.04% for ADC, 96.62% ± 0.01% for DCE. On the per‐sequence analysis, accuracy was 100% ± 0.00% for T2WI, DWI, and DCE, and 92.31% ± 0.00% for ADC. The three best algorithms performed significantly better than the remaining ones on every sequence (P‐value < 0.05).

Data Conclusion

CNNs achieved high accuracy in classifying prostate MRI image quality on an individual‐slice basis and almost perfect accuracy when classifying the entire sequences.

Evidence Level

4

Technical Efficacy

Stage 1

Prostate MRI quality: a critical review of the last 5 years and the role of the PI-QUAL score

There is increasing interest in the use of multiparametric magnetic resonance imaging (mpMRI) in the prostate cancer pathway. The European Association of Urology (EAU) and the British Association of Urological Surgeons (BAUS) now advise mpMRI prior to biopsy, and the Prostate Imaging Reporting and Data System (PI-RADS) recommendations set out the minimal technical requirements for the acquisition of mpMRI of the prostate.The widespread and swift adoption of this technique has led to variability in image quality. Suboptimal image acquisition reduces the sensitivity and specificity of mpMRI for the detection and staging of clinically significant prostate cancer.This critical review outlines the studies aimed at improving prostate MR quality that have been published over the last 5 years. These span from the use of specific MR sequences, magnets and coils to patient preparation. The rates of adherence of prostate mpMRI to technical standards in different cohorts across the world are also discussed.Finally, we discuss the first standardised scoring system (i.e., Prostate Imaging Quality, PI-QUAL) that has been created to evaluate image quality, although further iterations of this score are expected in the future.

Understanding PI-QUAL for prostate MRI quality: a practical primer for radiologists

Prostate magnetic resonance imaging (MRI) of high diagnostic quality is a key determinant for either detection or exclusion of prostate cancer. Adequate high spatial resolution on T2-weighted imaging, good diffusion-weighted imaging and dynamic contrast-enhanced sequences of high signal-to-noise ratio are the prerequisite for a high-quality MRI study of the prostate. The Prostate Imaging Quality (PI-QUAL) score was created to assess the diagnostic quality of a scan against a set of objective criteria as per Prostate Imaging-Reporting and Data System recommendations, together with criteria obtained from the image. The PI-QUAL score is a 1-to-5 scale where a score of 1 indicates that all MR sequences (T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced sequences) are below the minimum standard of diagnostic quality, a score of 3 means that the scan is of sufficient diagnostic quality, and a score of 5 implies that all three sequences are of optimal diagnostic quality. The purpose of this educational review is to provide a practical guide to assess the quality of prostate MRI using PI-QUAL and to familiarise the radiologist and all those involved in prostate MRI with this scoring system. A variety of images are also presented to demonstrate the difference between suboptimal and good prostate MR scans.

Standardisation of prostate multiparametric MRI across a hospital network: a London experience

OBJECTIVES

National guidelines recommend prostate multiparametric (mp) MRI in men with suspected prostate cancer before biopsy. In this study, we explore prostate mpMRI protocols across 14 London hospitals and determine whether standardisation improves diagnostic quality.

METHODS

An MRI physicist facilitated mpMRI set-up across several regional hospitals, working together with experienced uroradiologists who judged diagnostic quality. Radiologists from the 14 hospitals participated in the assessment and optimisation of prostate mpMRI image quality, assessed according to both PiRADSv2 recommendations and on the ability to "rule in" and/or "rule out" prostate cancer. Image quality and sequence parameters of representative mpMRI scans were evaluated across 23 MR scanners. Optimisation visits were performed to improve image quality, and 2 radiologists scored the image quality pre- and post-optimisation.

RESULTS

20/23 mpMRI protocols, consisting of 111 sequences, were optimised by modifying their sequence parameters. Pre-optimisation, only 15% of T2W images were non-diagnostic, whereas 40% of ADC maps, 50% of high b-value DWI and 41% of DCE-MRI were considered non-diagnostic. Post-optimisation, the scores were increased with 80% of ADC maps, 74% of high b-value DWI and 88% of DCE-MRI to be partially or fully diagnostic. T2W sequences were not optimised, due to their higher baseline quality scores.

CONCLUSIONS

Targeted intervention at a regional level can improve the diagnostic quality of prostate mpMRI protocols, with implications for improving prostate cancer detection rates and targeted biopsies.

Upcoming American College of Radiology prostate MRI designation launching: what to expect

The use of prostate MRI for prostate cancer evaluation continues to rise and ensuring minimum quality standards across practices will enable optimal diagnostic accuracy and thus, patient care. The American College of Radiology has been working on quality standards to meet Prostate MRI Designated Center, which is expected to launch in late 2020. We discuss the background of the American College of Radiology Prostate MRI working group's effort and summarize the criteria for Prostate MRI Designation.

Imaging quality and prostate MR: it is time to improve

The Prostate Imaging Reporting and Data System (PI-RADS) guidelines set out the minimal technical requirements for the acquisition of multiparametric MRI (mpMRI) of the prostate. However, the rapid diffusion of this technique has inevitably led to variability in scan quality among centres across the UK and the world. Suboptimal image acquisition reduces the sensitivity and specificity of this technique for the detection of clinically significant prostate cancer and results in clinicians losing confidence in the technique.Two expert panels, one from the UK and one from the European Society of Urogenital Radiology (ESUR)/EAU Section of Urologic Imaging (ESUI), have stressed the importance to establish quality criteria for the acquisition of mpMRI of the prostate. A first attempt to address this issue has been the publication of the Prostate Imaging Quality (PI-QUAL) score, which assesses the mpMRI quality against a set of objective criteria (PI-RADS guidelines) together with criteria obtained from the image.PI-QUAL represents the first step towards the standardisation of a scoring system to assess the quality of prostate mpMRI prior to reporting and allows clinicians to have more confidence in using the scan to determine patient care. Further refinements after robust consensus among experts at an international level need to be agreed before its widespread adoption in the clinical setting.

Measuring the Quality of Diagnostic Prostate Magnetic Resonance Imaging: A Urologist's Perspective

Focus on the quality of magnetic resonance imaging (MRI) by radiologists is welcome, but the clinical impacts that arise from MRI scans still need urological expertise. The urologist perspective is required in a multidisciplinary team setting when making decisions on whether to repeat a scan or perform a biopsy. This can ensure effective use of the prostate MRI diagnostic pathway in delivering desired clinical benefits for patients.