Impact of prostate imaging quality (PI-QUAL) score on the detection of clinically significant prostate cancer at biopsy

Diagnostic performance of biparametric MRI according to prostate imaging quality (PI-QUAL) version 2: Analysis of Multi-Institutional Data

PURPOSE

To evaluate the impact of image quality on the diagnostic performance of biparametric MRI (bpMRI) for detecting clinically significant prostate cancer (csPCa).

PATIENTS AND METHODS

This retrospective study included patients who underwent bpMRI at outside imaging facilities and were referred to our tertiary centre between January 2020 and November 2021. The image quality of bpMRI was assessed by two radiologists in consensus using Prostate Imaging Quality Score version 2 (PI-QUAL v2). Technical parameters of T2WI and DWI were extracted, and their associations with imaging quality criteria were evaluated. Sensitivity, specificity, and positive and negative predictive values for detecting csPCa were compared according to PI-QUAL v2 scores.

RESULTS

Among 112 men who underwent bpMRI at 69 different imaging facilities, 47 (42.0 %) MRIs were considered not applicable for PI-QUAL v2 scoring. Of the remaining MRIs, 30 (26.8 %), 17 (15.2 %), and and 18 (16.1 %) were assigned PI-QUAL v2 scores of 1, 2, and 3, respectively. MRIs with PI-QUAL v2 scores ≤ 1 demonstrated significantly lower sensitivity (74.3 %) than those with scores of 2 or 3 (100.0 %; P = 0.045). In-plane resolution on T2WI and the number of b values on DWI were significantly associated with image quality assessment (P = 0.037 and 0.028).

CONCLUSIONS

The diagnostic accuracy of bpMRI for detecting csPCa is influenced by image quality, as assessed by the PI-QUAL v2 scoring system. Adequate in-plane resolution on T2WI and the use of at least three b values on DWI should be emphasised to achieve optimal image quality and diagnostic performance.

Performing liver imaging at a high level: quality and adequacy in LI-RADS

Imaging is critical to HCC management, including surveillance, diagnosis, staging, and treatment response assessment, which requires it be performed consistently at a high level. The Liver Imaging Reporting and Data System (LI-RADS) was developed to standardize the acquisition, interpretation, and reporting of liver imaging, but until now, has not addressed the essential component of exam quality and adequacy. In this manuscript, we discuss the concepts of quality and adequacy and their clinical significance in the setting of HCC diagnostic imaging and treatment response assessment. We describe prior and current efforts to improve image quality and adequacy. We review common sources of image degradation that need to be addressed and the rationale behind LI-RADS technical recommendations. Finally, we offer a glimpse into preliminary efforts to develop an adequacy scoring system and make a call to action for all stakeholders to contribute to this important goal.

Multi-reader evaluation of the prostate imaging quality score system version 2 (PI-QUAL V2) and its clinical application

OBJECTIVES

The objectives of this study were: (1) to investigate the consistency of PI-QUAL V2 and (2) to observe the impact of PI-QUAL V2 on the diagnostic performance for csPCa.

METHODS

This retrospective study included 581 patients who underwent prostate MRI exams and had definitive pathological reports between January and December 2023. All histopathological specimens were evaluated, and an ISUP Grade Group ≥ 2 was considered as csPCa. Four radiologists independently assigned PI-QUAL V2 scores to each patient, with one of the readers re-scoring after a three-month interval. Cohen's and Fleiss' Kappa were used to determine inter-reader agreement, and the area under the receiver operating characteristic curve (AUC-ROC) was used to compare diagnostic performance. A p < 0.05 was considered statistically significant.

RESULTS

The study comprised 581 individuals (median [IQR] age, 70 [66-75] years). The consistency was moderate for T2WI, DWI, and PI-QUAL V2, and substantial for DCE. For PI-RADS V2.1 categorization tasks, the proportion of PI-RADS 3 gradually decreased with improving image quality, and a statistical difference was found between PI-QUAL 1 and 3 (p < 0.001). However, regardless of whether PI-RADS ≥ 3 or ≥ 4 was used as the "suspicious for cancer" threshold, there was no significant difference in overall diagnostic performance for csPCa among different PI-QUAL V2 scores (AUC 0.741-0.844).

CONCLUSION

PI-QUAL V2 plays a positive role in inter-reader reproducibility and radiologists' MRI diagnosis. Regarding the limited value of PI-QUAL V2 in the overall diagnosis of csPCa, further exploration with larger sample sizes and studies may be needed in the future.

KEY POINTS

Question PI-QUAL V2 has overcome the limitations of V1, but its clinical application status remains uncertain. Findings PI-QUAL V2 exhibits a favorable impact on inter-reader reproducibility and radiologists' MRI diagnosis, albeit its overall impact on diagnosing clinically significant prostate cancer remains limited. Clinical relevance While simplifying the scoring system, PI-QUAL V2 retains high inter-reader agreement. A suboptimal PI-QUAL V2 score may undermine radiologists' confidence in image interpretation, resulting in a decreased detection rate of clinically significant prostate cancer.

The added value of artificial intelligence using Quantib Prostate for the detection of prostate cancer at multiparametric magnetic resonance imaging

PURPOSE

Artificial intelligence (AI) has been proposed to assist radiologists in reporting multiparametric magnetic resonance imaging (mpMRI) of the prostate. We evaluate the diagnostic performance of radiologists with different levels of experience when reporting mpMRI with the support of available AI-based software (Quantib Prostate).

MATERIAL AND METHODS

This is a single-center study (NCT06298305) involving 110 patients. Those with a positive mpMRI (PI-RADS ≥ 3) underwent targeted plus systematic biopsy (TBx plus SBx), while those with a negative mpMRI but a high clinical suspicion of prostate cancer (PCa) underwent SBx. Three readers with different levels of experience, identified as R1, R2, and R3 reviewed all mpMRI. Inter-reader agreement among the three readers with or without the assistance of Quantib Prostate as well as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for the detection of clinically significant PCa (csPCa) were assessed.

RESULTS

102 patients underwent prostate biopsy and the csPCa detection rate was 47%. Using Quantib Prostate resulted in an increased number of lesions identified for R3 (101 vs. 127). Inter-reader agreement slightly increased when using Quantib Prostate from 0.37 to 0.41 without vs. with Quantib Prostate, respectively. PPV, NPV and diagnostic accuracy (measured by the area under the curve [AUC]) of R3 improved (0.51 vs. 0.55, 0.65 vs.0.82 and 0.56 vs. 0.62, respectively). Conversely, no changes were observed for R1 and R2.

CONCLUSIONS

Using Quantib Prostate did not enhance the detection rate of csPCa for readers with some experience in prostate imaging. However, for an inexperienced reader, this AI-based software is demonstrated to improve the performance.

TRIAL REGISTRATION

Name of registry: clinicaltrials.gov.

TRIAL REGISTRATION NUMBER

NCT06298305. Date of registration: 2022-09.

Deep learning for quality assessment of axial T2-weighted prostate MRI: a tool to reduce unnecessary rescanning

BACKGROUND

T2-weighted images are a critical component of prostate magnetic resonance imaging (MRI), and it would be useful to automatically assess image quality (IQ) on a patient-specific basis without radiologist oversight.

METHODS

This retrospective study comprised 1,412 axial T2-weighted prostate scans. Four experienced uroradiologists graded IQ using a 0-to-3 scale (0 = uninterpretable; 1 = marginally interpretable; 2 = adequately diagnostic; 3 = more than adequately diagnostic), binarized into nondiagnostic (IQ0 or IQ1), requiring rescanning, and diagnostic (IQ2 or IQ3), not requiring rescanning. The deep learning (DL) model was trained on 1,006 scans; 203 other scans were used for validation of multiple convolutional neural networks; the remaining 203 exams were used as a test set. 3D-DenseNet_169 was chosen among 11 models based on multiple evaluation criteria. The rescan predictions were compared to the number of rescans performed on a subset of 174 exams.

RESULTS

The model accurately predicts radiologist IQ scores (Cohen κ = 0.658), similar to the human inter-rater reliability (κ = 0.688-0.791). The model also predicts rescanning necessity similarly to radiologists: model κ = 0.537; reviewer κ = 0.577-0.703. The rescan model prediction area under the curve was 0.867.

CONCLUSION

The DL model showed a strong ability to differentiate diagnostic from nondiagnostic axial T2-weighted prostate images, accurately mimicking expert radiologists' IQ scores. Using the model, the clinical unnecessary rescan rate could be reduced from over 50% to less than 30%.

RELEVANCE STATEMENT

DL assessment of T2-weighted prostate MRI scans can accurately assess IQ, determining the need to repeat inadequate scans as well as avoiding repeat scans of those with adequate diagnostic quality, resulting in reduced unnecessary rescanning.

KEY POINTS

Artificial intelligence assessment of prostate MRI T2-weighted image quality can improve exam time management. The model showed over 75% accuracy in assessing prostate MRI T2-weighted image quality. Expert radiologists have a substantial agreement in evaluating prostate MRI T2-weighted image quality.

Optimizing biopsy decisions in PI-RADS 3 lesions: cross-institutional validation of a local clinical risk model

PURPOSE

To compare a locally developed risk model based on clinical parameters with previously published models and strategies to reduce MRI-targeted biopsies of indeterminate PI-RADS 3 lesions without missing clinically significant prostate cancer (csPCa) (ISUP ≥ grade 2).

METHODS

Retrospective, two-center study including 278 patients without prior PCa who underwent multiparametric MRI and MRI-targeted biopsy. For robust parameter estimation, a risk model based on clinical parameters was developed in a high-prevalence cohort (institution 1; n = 202; PI-RADS 3-5) and recalibrated to PI-RADS 3 subgroup (n = 115). The validation cohort (institution 2, same metropolitan area) consisted of 76 men with PI-RADS 3 index lesions. Model performance was compared to previously suggested strategies and risk models using decision curve analysis.

RESULTS

The local risk model provided the highest net benefit across all clinically relevant risk thresholds in the validation cohort. At a 10% risk threshold, the model could safely avoid biopsies in 547 per 1,000 men with PI-RADS 3 index lesions without missing csPCa, outperforming other strategies in number of biopsies avoided: normalized ADC (223), PSA density (210), MRI-ERSPC risk calculator (164), lesion volume (55) and the Radtke risk model (0). At low risk thresholds < 10% both normalized ADC (0.81) and PSA density (0.08 ng/ml/ml) were clinically useful.

CONCLUSION

A locally fit risk model based on clinical parameters could safely reduce unnecessary biopsies in men with PI-RADS 3 index lesions, with normalized ADC and PSA density providing useful and easy-to-use alternatives.

Micro-enema immediately prior to prostate MRI: effects on rectal gas, image quality and PI-QUAL score

PURPOSE

Our aim was to determine whether the administration of a micro-enema immediately prior to prostate MRI is associated with a reduction in rectal gas, gas related artifacts and an improvement in image quality and PI-QUAL score.

METHOD

This retrospective analysis enrolled 171 patients who underwent multiparametric 3T prostate MRI at our institution between January 2021 and September 2022. 86 patients received a micro-enema, and a further 85 patients did not. Two fellowship trained abdominal radiologists were blinded and independently reviewed each prostate MRI, recording scores on a dedicated scoring sheet. The quality of T2 weighted (T2W), diffusion weighted (DWI), and dynamic contrast enhancement (DCE) images were assessed according to standardised scales supported in the literature. In addition, gas related artifacts and rectal gas level were examined. An independent-samples Mann-Whitney U and t-test were performed, comparing both the median and mean score between micro-enema and no micro-enema groups for each reader. Spearman's correlation was used to determine the strength of relationship between variables. A quadratic weighted Cohen's Kappa and percent agreement were used to assess inter-observer agreement.

RESULTS

Image quality was improved in those who received the micro-enema compared to those who did not according to the visual grading scale on the DWI sequence for both readers (reader 1: median 4 vs. 4, p < 0.001, mean 4.27 vs. 3.92, p < 0.001; reader 2: median 5 vs. 4, p < 0.001, mean 4.74 vs. 4.14, p < 0.001). PI-QUAL score was significantly improved in the micro-enema group for reader 2 only (reader 1: median 4 vs. 4, p = 0.25, mean 3.99 vs. 4.08, p = 0.21; reader 2: median 5 vs. 5, p = 0.01, mean 4.95 vs. 4.78, p = 0.01). Visual grading score for both the T2W and DCE images was not significantly different. Rectal gas level was lower in patients who received the micro-enema for both readers (reader 1: median 2 vs. 4, p < 0.001, mean 2.12 vs. 3.60, p < 0.001; reader 2: median 1 vs. 2, p < 0.001, mean 1.37 vs. 2.48, p < 0.001), correlating with a lower score for gas-related artifacts (reader 1: median 1 vs. 2, p < 0.001, mean 1.50 vs. 1.92, p < 0.001; reader 2: median 1 vs. 1, p < 0.001, mean 1.16 vs. 1.71, p < 0.001) in this group. Correlation between rectal gas level and gas-related artifacts on DWI regardless of whether a micro-enema was given was strong (rs = 0.71, p < 0.001). Correlation was moderate to strong between rectal gas level and image quality on DWI (rs = -0.63, p < 0.001). There was only 1 (1.2%) borderline diagnostic or non-diagnostic DWI sequence in those who received the micro-enema, compared to 9 (10.6%) in those who did not (p = 0.009). Interobserver agreement was moderate for image quality on DWI, gas related artifacts and rectal gas level (weighted kappa values of 0.52, 0.49 and 0.53 respectively).

CONCLUSIONS

The administration of a micro-enema immediately prior to prostate MRI is associated with a significant improvement in image quality on the DWI sequence compared to no bowel preparation. This is mediated through a reduction in rectal gas and gas related artifacts. Improvements in PI-QUAL score was mixed between readers.

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

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

Impact of software-assisted structured reporting on radiology residents approaching prostate MRI

PURPOSE

To evaluate the potential advantages of software-assisted structured reporting for radiology residents approaching multiparametric prostate MRI (mpMRI).

METHODS

MpMRI scans from 100 patients, performed for prostate cancer (PCa) detection or staging, were anonymized, and reviewed by six second-year radiology residents without previous experience in prostate MRI, following 6 h of intensive training. The dataset was split into two subsets of 50 cases each. All residents were asked to report scans from the first subset using a basic text processor (narrative reports -NR-). For the second subset, one group used a dedicated software to produce structured reports (SR) while the other continued with NR. Report completeness was assessed using a PI-RADS-based checklist, and statistical analyses, including Wilcoxon rank sum and Pearson's Chi-squared tests, were performed to compare word count, reporting time, and concordance with an expert radiologist's findings.

RESULTS

All readers adopting SR in the second batch demonstrated a significant increase in word count and a decrease in reporting time compared to the first batch. Image quality and final impressions were missing from all NR, while gland size, lesion description, and PI-RADS score were consistently included in nearly all reports (96-100 %). One of the three residents using SR showed a statistically significant improvement in concordance with the expert radiologist on index lesion location and clinically significant PCa presence (p = 0.001), while the other two exhibited positive trends (p = 0.061-0.078).

CONCLUSIONS

The adoption of SR allowed radiology residents to decrease their reporting time and improve the comprehensiveness of their reports, while increasing concordance with an expert radiologist.

Prostate Imaging Quality System Version 2 - A Practical Guide

High-quality prostate magnetic resonance imaging (MRI) is required for accurate prostate cancer detection, localization, and staging. Variability in image quality exists in practice, influenced by inconsistent adherence to technical standards, lack of patient preparation, variability in scanner performance, patient characteristics, and knowledge gaps in personnel scheduling and performing prostate MRI exams. The Prostate Imaging Quality (PI-QUAL) scoring system is a well-established tool for assessing the diagnostic quality of prostate MRI. An updated PI-QUAL version 2 was published in 2024 by a group of experts to address the limitations of the original system. This pictorial review highlights the key changes introduced in PI-QUAL version 2 and provides illustrative examples of each assessment category.

High-resolution Diffusion-weighted Imaging of the Prostate Using Multiplexed Sensitivity-encoding: Comparison with the Conventional and Reduced Field-of-view Techniques

PURPOSE

To compare objective and subjective image quality, lesion conspicuity, and apparent diffusion coefficient (ADC) of high-resolution multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) with conventional DWI (c-DWI) and reduced FOV DWI (rFOV-DWI) in prostate MRI.

METHODS

Forty-seven patients who underwent prostate MRI, including c-DWI, rFOV-DWI, and MUSE-DWI, were retrospectively evaluated. SNR and ADC of normal prostate tissue and contrast-to-noise ratio (CNR) and ADC of prostate cancer (PCa) were measured and compared between the three sequences. Image quality and lesion conspicuity were independently graded by two radiologists using a 5-point scale and compared between the three sequences.

RESULTS

The SNR of normal prostate tissue was significantly higher with rFOV-DWI than with the other two DWI techniques (P ≤ 0.01). The CNR of the PCa was significantly higher with rFOV-DWI than with MUSE-DWI (P < 0.05). The ADC of normal prostate tissue measured by rFOV-DWI was lower than that measured by MUSE-DWI and c-DWI (P < 0.01), while there was no difference in the ADC of cancers. In the qualitative analysis, MUSE-DWI showed significantly higher scores than rFOV-DWI and c-DWI for visibility of anatomy and overall image quality in both readers, and significantly higher scores for distortion in one of the two readers (P < 0.001). There was no difference in lesion conspicuity between the three sequences.

CONCLUSION

High-resolution MUSE-DWI showed higher image quality and reduced distortion compared to c-DWI, while maintaining a wide FOV and similar ADC quantification, although no difference in lesion conspicuity was observed.

Preoperative detection of extraprostatic tumor extension in patients with primary prostate cancer utilizing [68Ga]Ga-PSMA-11 PET/MRI

OBJECTIVES

Radical prostatectomy (RP) is a common intervention in patients with localized prostate cancer (PCa), with nerve-sparing RP recommended to reduce adverse effects on patient quality of life. Accurate pre-operative detection of extraprostatic extension (EPE) remains challenging, often leading to the application of suboptimal treatment. The aim of this study was to enhance pre-operative EPE detection through multimodal data integration using explainable machine learning (ML).

METHODS

Patients with newly diagnosed PCa who underwent [68Ga]Ga-PSMA-11 PET/MRI and subsequent RP were recruited retrospectively from two time ranges for training, cross-validation, and independent validation. The presence of EPE was measured from post-surgical histopathology and predicted using ML and pre-operative parameters, including PET/MRI-derived features, blood-based markers, histology-derived parameters, and demographic parameters. ML models were subsequently compared with conventional PET/MRI-based image readings.

RESULTS

The study involved 107 patients, 59 (55%) of whom were affected by EPE according to postoperative findings for the initial training and cross-validation. The ML models demonstrated superior diagnostic performance over conventional PET/MRI image readings, with the explainable boosting machine model achieving an AUC of 0.88 (95% CI 0.87-0.89) during cross-validation and an AUC of 0.88 (95% CI 0.75-0.97) during independent validation. The ML approach integrating invasive features demonstrated better predictive capabilities for EPE compared to visual clinical read-outs (Cross-validation AUC 0.88 versus 0.71, p = 0.02).

CONCLUSION

ML based on routinely acquired clinical data can significantly improve the pre-operative detection of EPE in PCa patients, potentially enabling more accurate clinical staging and decision-making, thereby improving patient outcomes.

CRITICAL RELEVANCE STATEMENT

This study demonstrates that integrating multimodal data with machine learning significantly improves the pre-operative detection of extraprostatic extension in prostate cancer patients, outperforming conventional imaging methods and potentially leading to more accurate clinical staging and better treatment decisions.

KEY POINTS

Extraprostatic extension is an important indicator guiding treatment approaches. Current assessment of extraprostatic extension is difficult and lacks accuracy. Machine learning improves detection of extraprostatic extension using PSMA-PET/MRI and histopathology.

Strategies for improving image quality in prostate MRI

Prostate magnetic resonance imaging (MRI) stands as the cornerstone in diagnosing prostate cancer (PCa), offering superior detection capabilities while minimizing unnecessary biopsies. Despite its critical role, global disparities in MRI diagnostic performance persist, stemming from variations in image quality and radiologist expertise. This manuscript reviews the challenges and strategies for enhancing image quality in prostate MRI, spanning patient preparation, MRI unit optimization, and radiology team engagement. Quality assurance (QA) and quality control (QC) processes are pivotal, emphasizing standardized protocols, meticulous patient evaluation, MRI unit workflow, and radiology team performance. Additionally, artificial intelligence (AI) advancements offer promising avenues for improving image quality and reducing acquisition times. The Prostate-Imaging Quality (PI-QUAL) scoring system emerges as a valuable tool for assessing MRI image quality. A comprehensive approach addressing technical, procedural, and interpretative aspects is essential to ensure consistent and reliable prostate MRI outcomes.

Is MRI ready to replace biopsy during active surveillance?

Active surveillance (AS) is a conservative management option recommended for patients diagnosed with low-risk prostate cancer (PCa) and selected cases with intermediate-risk PCa. The adoption of prostate MRI in the primary diagnostic setting has sparked interest in its application during AS. This review aims to examine the role and performance of multiparametric MRI (mpMRI) across the entire AS pathway, from initial stratification to follow-up, also relative to the utilization of the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) criteria. Given the high negative predictive value of mpMRI in detecting clinically significant PCa (csPCa), robust evidence supports its use in patient selection and risk stratification at the time of diagnosis or confirmatory biopsy. However, conflicting results have been observed when using MRI in evaluating disease progression during follow-up. Key areas requiring clarification include addressing the clinical significance of MRI-negative csPCa, optimizing MRI quality, determining the role of biparametric MRI (bpMRI) or mpMRI protocols, and integrating artificial intelligence (AI) for improved performance. CLINICAL RELEVANCE STATEMENT: MRI plays an essential role in the selection, stratification, and follow up of patients in active surveillance (AS) for prostate cancer. However, owing to existing limitations, it cannot fully replace biopsies in the context of AS. KEY POINTS: Multiparametric MRI (mpMRI) has become a crucial tool in active surveillance (AS) for prostate cancer (PCa). Conflicting results have been observed regarding multiparametric MRI efficacy in assessing disease progression. Standardizing MRI-guided protocols will be critical in addressing current limitations in active surveillance for prostate cancer.

ESR Essentials: using the right scoring system in prostate MRI-practice recommendations by ESUR

MRI has gained prominence in the diagnostic workup of prostate cancer (PCa) patients, with the Prostate Imaging Reporting and Data System (PI-RADS) being widely used for cancer detection. Beyond PI-RADS, other MRI-based scoring tools have emerged to address broader aspects within the PCa domain. However, the multitude of available MRI-based grading systems has led to inconsistencies in their application within clinical workflows. The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) assesses the likelihood of clinically significant radiological changes of PCa during active surveillance, and the Prostate Imaging for Local Recurrence Reporting (PI-RR) scoring system evaluates the risk of local recurrence after whole-gland therapies with curative intent. Underlying any system is the requirement to assess image quality using the Prostate Imaging Quality Scoring System (PI-QUAL). This article offers practicing radiologists a comprehensive overview of currently available scoring systems with clinical evidence supporting their use for managing PCa patients to enhance consistency in interpretation and facilitate effective communication with referring clinicians. KEY POINTS: Assessing image quality is essential for all prostate MRI interpretations and the PI-QUAL score represents  the standardized tool for this purpose. Current urological clinical guidelines for prostate cancer diagnosis and localization recommend adhering to the PI-RADS recommendations. The PRECISE and PI-RR scoring systems can be used for assessing radiological changes of prostate cancer during active surveillance and the likelihood of local recurrence after radical treatments respectively.

Impact of prostate MRI image quality on diagnostic performance for clinically significant prostate cancer (csPCa)

OBJECTIVES

With the widespread clinical application of prostate magnetic resonance imaging (MRI), there has been an increasing demand for lesion detection and accurate diagnosis in prostate MR, which relies heavily on satisfactory image quality. Focusing on the primary sequences involved in Prostate Imaging Reporting and Data System (PI-RADS), this study have evaluated common quality issues in clinical practice (such as signal-to-noise ratio (SNR), artifacts, boundaries, and enhancement). The aim of the study was to determine the impact of image quality on clinically significant prostate cancer (csPCa) detection, positive predictive value (PPV) and radiologist's diagnosis in different sequences and prostate zones.

METHODS

This retrospective study included 306 patients who underwent prostate MRI with definitive pathological reports from February 2021 to December 2022. All histopathological specimens were evaluated according to the recommendations of the International Society of Urological Pathology (ISUP). An ISUP Grade Group ≥ 2 was considered as csPCa. Three radiologists from different centers respectively performed a binary classification assessment of image quality in the following ten aspects: (1) T2WI in the axial plane: SNR, prostate boundary conditions, the presence of artifacts; (2) T2WI in the sagittal or coronal plane: prostate boundary conditions; (3) DWI: SNR, delineation between the peripheral and transition zone, the presence of artifacts, the matching of DWI and T2WI images; (4) DCE: the evaluation of obturator artery enhancement, the evaluation of dynamic contrast enhancement. Fleiss' Kappa was used to determine the inter-reader agreement. Wilson's 95% confidence interval (95% CI) was used to calculate PPV. Chi-square test was used to calculate statistical significance. A p-value < 0.05 was considered statistically significant.

RESULTS

High-quality images had a higher csPCa detection rate (56.5% to 64.3%) in axial T2WI, DWI, and DCE, with significant statistical differences in SNR in axial T2WI (p 0.002), the presence of artifacts in axial T2WI (p 0.044), the presence of artifacts in DWI (p < 0.001), and the matching of DWI and T2WI images (p < 0.001). High-quality images had a higher PPV (72.5% to 78.8%) and showed significant statistical significance in axial T2WI, DWI, and DCE. Additionally, we found that PI-RADS 3 (24.0% to 52.9%) contained more low-quality images compared to PI-RADS 4-5 (20.6% to 39.3%), with significant statistical differences in the prostate boundary conditions in axial T2WI (p 0.048) and the presence of artifacts in DWI (p 0.001). Regarding the relationship between csPCa detection and image quality in different prostate zones, this study found that significant statistical differences were only observed between high- (63.5% to 75.7%) and low-quality (30.0% to 50.0%) images in the peripheral zone (PZ).

CONCLUSION

Prostate MRI quality may have an impact on the diagnostic performance. The poorer image quality is associated with lower csPCa detection rates and PPV, which can lead to an increase in radiologist's ambiguous diagnosis (PI-RADS 3), especially for the lesions located at PZ.

Image quality comparison of 1.5T and 3T prostate MRIs of the same post-hip arthroplasty patients: multi-rater assessments including PI-QUAL version 2

OBJECTIVES

To compare the image quality of 1.5T and 3T prostate MRIs of the same post-hip arthroplasty patients, with a specific focus on the degree of susceptibility artifacts.

METHODS

This single-center retrospective study included post-hip arthroplasty patients who underwent 1.5T prostate MRIs between 2021 and 2023, as well as comparative 3T prostate MRIs. Three blinded abdominal radiologists retrospectively reviewed their diffusion-weighted imaging (DWI, 50 s/mm2), T2-weighted imaging (T2WI), and dynamic contrast-enhanced imaging (DCE) to evaluate the image quality. The degree of susceptibility artifacts was categorized using a three-point scale, with 3 indicating the least artifact and 1 indicating the most. Image quality was also evaluated using Prostate Imaging Quality (PI-QUAL) version 2. The median of the three raters' scores was compared between 1.5T and 3T prostate MRIs using the Wilcoxon signed-rank test. The inter-rater agreement was evaluated using the multi-rater generalized kappa.

RESULTS

Twenty pairs of 1.5T and 3T prostate MRI examinations from 20 unique patients were included. The DWI susceptibility artifact score at 1.5T was significantly higher than at 3T (mean score ± standard deviation, 2.80 ± 0.41 vs. 2.35 ± 0.93, p = 0.014). In contrast, no significant differences were observed in the susceptibility artifact scores in T2WI and DCE, or in the PI-QUAL score. The inter-reader agreement in the susceptibility artifact score was moderate (multi-rater generalized kappa: 0.60) in DWI, perfect in T2WI (not applicable), and substantial (0.65) in DCE. The inter-reader agreement was fair (0.27) in the PI-QUAL score.

CONCLUSION

Using 1.5T scanners may be preferable to reduce susceptibility artifacts from hip prostheses in DWI.

Emerging and anticipated innovations in prostate cancer MRI and their impact on patient care

Prostate cancer (PCa) remains the leading malignancy affecting men, with over 3 million men living with the disease in the US, and an estimated 288,000 new cases and almost 35,000 deaths in 2023 in the United States alone. Over the last few decades, imaging has been a cornerstone in PCa care, with a crucial role in the detection, staging, and assessment of PCa recurrence or by guiding diagnostic or therapeutic interventions. To improve diagnostic accuracy and outcomes in PCa care, remarkable advancements have been made to different imaging modalities in recent years. This paper focuses on reviewing the main innovations in the field of PCa magnetic resonance imaging, including MRI protocols, MRI-guided procedural interventions, artificial intelligence algorithms and positron emission tomography, which may impact PCa care in the future.

PI-QUAL version 2: A Multi-Reader reproducibility study on multiparametric MRI from a tertiary referral center

PURPOSE

To assess the inter-reader and intra-reader agreement of the Prostate imaging quality version 2 (PI-QUAL v.2) for multiparametric magnetic resonance imaging (mpMRI) among radiologists with varying levels of expertise.

METHODS

Fifty men underwent 3 T mpMRI scans in a tertiary referral center. Images were anonymized and assessed by six readers of different expertise (2 expert, 2 basic and 2 beginners) in two sessions: first using PI-QUAL v.2, and then using both PI-QUAL v.2 and v.1 after a 2-week interval. PI-QUAL v.2 scores were considered overall and, for comparison with PI-QUAL v.1, dichotomized according to the threshold of acceptable image quality. Gwet AC1 index was used to calculate the inter-reader and intra-reader agreement of the scores.

RESULTS

The inter-reader agreement for PI-QUAL v.2 scores was overall moderate (Gwet's AC1 = 0.55), being higher for expert readers compared to the beginner and basic ones (Gwet's AC1 = 0.66 versus 0.45-0-58). Intra-reader agreement varied from moderate to perfect (Gwet's AC1 = 0.43-1.00) and improved with increasing levels of expertise. The ratings were more reproducible for DWI and DCE sequences (Gwet's AC1 = 0.62-1.00) compared to T2w (Gwet's AC1 = 0.24-0.70). The intra-reader agreement between PI-QUAL v.2 and v.1 scores across readings ranged from almost perfect to perfect (Gwet's AC1 = 0.96-1.00).

CONCLUSIONS

In a tertiary referral center context, PI-QUAL v.2 is a moderately reliable tool for standardizing prostate mpMRI quality evaluations among readers with varying expertise.

Recent trends in AI applications for pelvic MRI: a comprehensive review

Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders affecting the prostate, bladder, uterus, ovaries, and/or rectum. Since the diagnostic pathway of pelvic MRI can involve various complex procedures depending on the affected organ, the Reporting and Data System (RADS) is used to standardize image acquisition and interpretation. Artificial intelligence (AI), which encompasses machine learning and deep learning algorithms, has been integrated into both pelvic MRI and the RADS, particularly for prostate MRI. This review outlines recent developments in the use of AI in various stages of the pelvic MRI diagnostic pathway, including image acquisition, image reconstruction, organ and lesion segmentation, lesion detection and classification, and risk stratification, with special emphasis on recent trends in multi-center studies, which can help to improve the generalizability of AI.

Added Value of Prostate-specific Antigen Density in Selecting Prostate Biopsy Candidates Among Men with Elevated Prostate-specific Antigen and PI-RADS ≥3 Lesions on Multiparametric Magnetic Resonance Imaging of the Prostate: A Systematic Assessment by PI-RADS Score

BACKGROUND

A significant proportion of patients with positive multiparametric magnetic resonance imaging (mpMRI; Prostate Imaging-Reporting and Data System [PI-RADS] scores of 3-5) have negative biopsy results.

OBJECTIVE

To systematically assess all prostate-specific antigen density (PSAD) values and identify an appropriate cutoff for identification of patients with positive mpMRI who could potentially avoid biopsy on the basis of their PI-RADS score.

DESIGN, SETTING, AND PARTICIPANTS

The study included a cohort of 1341 patients with positive mpMRI who underwent combined targeted and systematic biopsies.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Multivariable logistic regression analysis (MVA) was used to assess the association between PSAD and the risk of clinically significant prostate cancer (csPCa, grade group ≥2) after adjusting for confounders. We used locally weighted scatterplot smoothing to explore csPCa risk according to PSAD and PI-RADS scores. PSAD utility was observed only for patients with PI-RADS 3 lesions, so we plotted the effect of each PSAD value as a cutoff for this subgroup in terms of biopsies saved, csPCa cases missed, and clinically insignificant PCa (ciPCa, grade group 1) cases not detected.

RESULTS AND LIMITATIONS

Overall, 667 (50%) csPCa cases were identified. On MVA, PSAD independently predicted csPCa (odds ratio 1.57; p < 0.001). For PI-RADS ≥4 lesions, the csPCa risk was ≥40% regardless of PSAD. Conversely, among patients with PI-RADS 3 lesions, csPCa risk ranged from 0% to 60% according to PSAD values, and a PSAD cutoff of 0.10 ng/ml/cm3 corresponded to a threshold probability of 10% for csPCa. Using this PSAD cutoff for patients with PI-RADS 3 lesions would have saved 32% of biopsies, missed 7% of csPCa cases, and avoided detection of 34% of ciPCa cases. Limitations include selection bias and the high experience of the radiologists and urologists involved.

CONCLUSIONS

Patients with PI-RADS ≥4 lesions should undergo prostate biopsy regardless of their PSAD, while PSAD should be used to stratify patients with PI-RADS 3 lesions. Using a threshold probability of 10% for csPCa, our data suggest that the appropriate strategy is to avoid biopsy in patients with PI-RADS 3 lesions and PSAD <0.10 ng/ml/cm3. Our results also provide information to help in tailoring an appropriate strategy for every patient with positive mpMRI findings.

PATIENT SUMMARY

We investigated whether a cutoff value for PSAD (prostate-specific antigen density) could identify patients with suspicious prostate lesions on MRI (magnetic resonance imaging) who could avoid biopsy according to the PI-RADS score for their scan. We found that patients with PI-RADS ≥4 should undergo prostate biopsy regardless of their PSAD. A PSAD cutoff of 0.10 should be used to stratify patients with PI-RADS 3.

Expect the unexpected: investigating discordant prostate MRI and biopsy results

OBJECTIVES

To evaluate discrepant radio-pathological outcomes in biopsy-naïve patients undergoing prostate MRI and to provide insights into the underlying causes.

MATERIALS AND METHODS

A retrospective analysis was conducted on 2780 biopsy-naïve patients undergoing prostate MRI at a tertiary referral centre between October 2015 and June 2022. Exclusion criteria were biopsy not performed, indeterminate MRI findings (PI-RADS 3), and clinically insignificant PCa (Gleason score 3 + 3). Patients with discrepant findings between MRI and biopsy results were categorised into two groups: MRI-negative/Biopsy-positive and MRI-positive/Biopsy-negative (biopsy-positive defined as Gleason score ≥ 3 + 4). An expert uroradiologist reviewed discrepant cases, retrospectively re-assigning PI-RADS scores, identifying any missed MRI targets, and evaluating the quality of MRI scans. Potential explanations for discrepancies included MRI overcalls (including known pitfalls), benign pathology findings, and biopsy targeting errors.

RESULTS

Patients who did not undergo biopsy (n = 1258) or who had indeterminate MRI findings (n = 204), as well as those with clinically insignificant PCa (n = 216), were excluded, with a total of 1102 patients analysed. Of these, 32/1,102 (3%) were classified as MRI-negative/biopsy-positive and 117/1102 (11%) as MRI-positive/biopsy-negative. In the MRI-negative/Biopsy-positive group, 44% of studies were considered non-diagnostic quality. Upon retrospective image review, target lesions were identified in 28% of cases. In the MRI-positive/Biopsy-negative group, 42% of cases were considered to be MRI overcalls, and 32% had an explanatory benign pathological finding, with biopsy targeting errors accounting for 11% of cases.

CONCLUSION

Prostate MRI demonstrated a high diagnostic accuracy, with low occurrences of discrepant findings as defined. Common reasons for MRI-positive/Biopsy-negative cases included explanatory benign findings and MRI overcalls.

CLINICAL RELEVANCE STATEMENT

This study highlights the importance of optimal prostate MRI image quality and expertise in reducing diagnostic errors, improving patient outcomes, and guiding appropriate management decisions in the prostate cancer diagnostic pathway.

KEY POINTS

• Discrepancies between prostate MRI and biopsy results can occur, with higher numbers of MRI-positive/biopsy-negative relative to MRI-negative/biopsy-positive cases. • MRI-positive/biopsy-negative cases were mostly overcalls or explainable by benign biopsy findings. • In about one-third of MRI-negative/biopsy-positive cases, a target lesion was retrospectively identified.

Multiparametric Prostate MRI Accuracy of Prostate Imaging Reporting and Data System (v2.1) Scores 4 and 5: The Influence of Image Quality According to the Prostate Imaging Quality Score

Purpose: The accuracy of multiparametric magnetic resonance imaging (mpMRI) heavily relies on image quality, as evidenced by the evolution of the prostate imaging quality (PI-QUAL) scoring system for the evaluation of clinically significant prostate cancer (csPC). This study aims to evaluate the impact of PI-QUAL scores in detecting csPC within PI-RADS 4 and 5 lesions. Methods: We retrospectively selected from our database all mpMRI performed from January 2019 to March 2022. Inclusion criteria were as follows: (1) mpMRI acquired in our institution according to the technical requirements from the PI-RADS (v2.1) guidelines; (2) single lesion scored as PI-RADS (v2.1) 4 or 5; (3) MRI-TBx performed in our institution; (4) complete histology report; and (5) complete clinical record. Results: A total of 257 male patients, mean age 70.42 ± 7.6 years, with a single PI-RADS 4 or 5 lesion undergoing MRI-targeted biopsy, were retrospectively studied. Of these, 61.5% were PI-RADS 4, and 38.5% were PI-RADS 5, with 84% confirming neoplastic cells. In high-quality image lesions (PI-QUAL ≥ 4), all PI-RADS 5 lesions were accurately identified as positive at the final histological examination (100% of CDR). For PI-RADS 4 lesions, 37 (23%) were negative, resulting in a cancer detection rate of 77% (95% CI: 67.51-84.83). Conclusions: The accuracy of mpMRI, independently of the PI-RADS score, progressively decreased according to the decreasing PI-QUAL score. These findings emphasize the crucial role of the PI-QUAL scoring system in evaluating PI-RADS 4 and 5 lesions, influencing mpMRI accuracy.

Diagnostic performance of MRI in detecting prostate cancer in patients with prostate-specific antigen levels of 4-10 ng/mL: a systematic review and meta-analysis

OBJECTIVE

To investigate the diagnostic performance of MRI in detecting clinically significant prostate cancer (csPCa) and prostate cancer (PCa) in patients with prostate-specific antigen (PSA) levels of 4-10 ng/mL.

METHODS

A computerized search of PubMed, Embase, Cochrane Library, Medline, and Web of Science was conducted from inception until October 31, 2023. We included articles on the use of MRI to detect csPCa or PCa at 4-10 ng/mL PSA. The primary and secondary outcomes were MRI performance in csPCa and PCa detection, respectively; the estimates of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were pooled in a bivariate random-effects model.

RESULTS

Among the 19 studies (3879 patients), there were 10 (2205 patients) and 13 studies (2965 patients) that reported MRI for detecting csPCa or PCa, respectively. The pooled sensitivity and specificity for csPCa detection were 0.84 (95% confidence interval [CI], 0.79-0.88) and 0.76 (95%CI, 0.65-0.84), respectively, for PCa detection were 0.82 (95%CI, 0.75-0.87) and 0.74 (95%CI, 0.65-0.82), respectively. The pooled NPV for csPCa detection was 0.91 (0.87-0.93). Biparametric magnetic resonance imaging also showed a significantly higher sensitivity and specificity relative to multiparametric magnetic resonance imaging (both p < 0.01).

CONCLUSION

Prostate MRI enables the detection of csPCa and PCa with satisfactory performance in the PSA gray zone. The excellent NPV for csPCa detection indicates the possibility of biopsy decision-making in patients in the PSA gray zone, but substantial heterogeneity among the included studies should be taken into account.

CLINICAL RELEVANCE STATEMENT

Prostate MRI can be considered a reliable and satisfactory tool for detecting csPCa and PCa in patients with PSA in the "gray zone", allowing for reducing unnecessary biopsy and optimizing the overall examination process.

KEY POINTS

Prostate-specific antigen (PSA) is a common screening tool for prostate cancer but risks overdiagnosis. MRI demonstrated excellent negative predictive value for prostate cancer in the PSA gray zone. MRI can influence decision-making for these patients, and biparametric MRI should be further evaluated.

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.

Decreased prostate MRI cancer detection rate due to moderate to severe susceptibility artifacts from hip prosthesis

OBJECTIVES

To evaluate the impact of susceptibility artifacts from hip prosthesis on cancer detection rate (CDR) in prostate MRI.

MATERIALS AND METHODS

This three-center retrospective study included prostate MRI studies for patients without known prostate cancer between 2017 and 2021. Exams with hip prosthesis were searched on MRI reports. The degree of susceptibility artifact on diffusion-weighted images was retrospectively categorized into mild, moderate, and severe (> 66%, 33-66%, and < 33% of the prostate volume are evaluable) by blind reviewers. CDR was defined as the number of exams with Gleason score ≥7 detected by MRI (PI-RADS ≥3) divided by the total number of exams. For each artifact grade, control exams without hip prosthesis were matched (1:6 match), and CDR was compared. The degree of CDR reduction was evaluated with ratio, and influential factors were evaluated by expanding the equation.

RESULTS

Hip arthroplasty was present in 548 (4.8%) of the 11,319 MRI exams. CDR of the cases and matched control exams for each artifact grade were as follows: mild (n = 238), 0.27 vs 0.25, CDR ratio = 1.09 [95% CI: 0.87-1.37]; moderate (n = 143), 0.18 vs 0.27, CDR ratio = 0.67 [95% CI: 0.46-0.96]; severe (n = 167), 0.22 vs 0.28, CDR ratio = 0.80 [95% CI: 0.59-1.08]. When moderate and severe artifact grades were combined, CDR ratio was 0.74 [95% CI: 0.58-0.93]. CDR reduction was mostly attributed to the increased frequency of PI-RADS 1-2.

CONCLUSION

With moderate to severe susceptibility artifacts from hip prosthesis, CDR was decreased to 74% compared to the matched control.

CLINICAL RELEVANCE STATEMENT

Moderate to severe susceptibility artifacts from hip prosthesis may cause a non-negligible CDR reduction in prostate MRI. Expanding indications for systematic prostate biopsy may be considered when PI-RADS 1-2 was assigned.

KEY POINTS

• We proposed cancer detection rate as a diagnostic performance metric in prostate MRI. • With moderate to severe susceptibility artifacts secondary to hip arthroplasty, cancer detection rate decreased to 74% compared to the matched control. • Expanding indications for systematic prostate biopsy may be considered when PI-RADS 1-2 is assigned.

Prostate MRI for the detection of clinically significant prostate cancer: Update and future directions

PURPOSE OF REVIEW

In recent decades, there has been an increasing role for magnetic resonance imaging (MRI) in the detection of clinically significant prostate cancer (csPC). The purpose of this review is to provide an update and outline future directions for the role of MRI in the detection of csPC.

RECENT FINDINGS

In diagnosing clinically significant prostate cancer pre-biopsy, advances include our understanding of MRI-targeted biopsy, the role of biparametric MRI (non-contrast) and changing indications, for example the role of MRI in screening for prostate cancer. Furthermore, the role of MRI in identifying csPC is maturing, with emphasis on standardization of MRI reporting in active surveillance (PRECISE), clinical staging (EPE grading, MET-RADS-P) and recurrent disease (PI-RR, PI-FAB). Future directions of prostate MRI in detecting csPC include quality improvement, artificial intelligence and radiomics, positron emission tomography (PET)/MRI and MRI-directed therapy.

SUMMARY

The utility of MRI in detecting csPC has been demonstrated in many clinical scenarios, initially from simply diagnosing csPC pre-biopsy, now to screening, active surveillance, clinical staging, and detection of recurrent disease. Continued efforts should be undertaken not only to emphasize the reporting of prostate MRI quality, but to standardize reporting according to the appropriate clinical setting.

MRI-based monitoring of prostate cancer after HIFU: Inter-reader agreement and diagnostic performance of the PI-FAB score

PURPOSE

To investigate inter-reader agreement, and diagnostic performance of the Prostate Imaging after Focal Ablation (PI-FAB) score applied to multiparametric MRI (mpMRI) in patients who underwent focal high-intensity focused ultrasound (HIFU) therapy for localized prostate cancer.

METHODS

In this retrospective, IRB-approved, single-center study, 73 men, who underwent focal HIFU treatment and received follow-up mpMRIs with subsequent prostate biopsies, were included. The PI-FAB score was applied to follow-up MRIs at 6, 12, and 36 months post-HIFU by two radiologists with different experience levels. Inter-reader agreement was assessed using Gwet's AC1, and the diagnostic performance of the PI-FAB score was assessed in relation to histopathologic results of subsequent prostate biopsies for each reader.

RESULTS

PI-FAB scores showed substantial to almost perfect inter-reader agreement (AC1: 0.80-0.95) and demonstrated high specificity (Reader 1: 90-98 %, Reader 2: 87-98 %) and NPVs (Reader 1: 91-100 %, Reader 2: 88-97 %) in ruling out residual or recurrent in-field prostate cancer post-HIFU. Sensitivity (Reader 1: ≥43 %, Reader 2: ≥14 %) and PPVs (Reader 1: ≥33 %, Reader 2: ≥14 %) were mostly relatively lower, with notable disparities between the two readers, indicating the potential influence of radiologist experience.

CONCLUSIONS

The PI-FAB score provides a consistent and reliable tool for post-HIFU monitoring of prostate cancer using mpMRI. It demonstrates substantial to almost perfect inter-reader agreement and is particularly effective in excluding in-field residual or recurrent prostate cancer post-HIFU treatment. Its application can potentially enhance post-treatment patient care, emphasizing its value as a non-invasive MRI-based monitoring approach after focal ablative therapy of the prostate.

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.

Cancer Detection Rate and Abnormal Interpretation Rate of Prostate MRI Performed for Clinical Suspicion of Prostate Cancer

PURPOSE

To report cancer detection rate (CDR) and abnormal interpretation rate (AIR) in prostate MRI performed for clinical suspicion of prostate cancer (PCa).

MATERIALS AND METHODS

This retrospective single-institution, three-center study included patients who underwent MRI for clinical suspicion of PCa between 2017 and 2021. Patients with known PCa were excluded. Patient-level Prostate Imaging-Reporting and Data System (PI-RADS) score was extracted from the radiology report. AIR was defined as number of abnormal MRI (PI-RADS score 3-5) / total number of MRIs. CDR was defined as number of clinically significant PCa (csPCa: Gleason score ≥7) detected at abnormal MRI / total number of MRI. AIR, CDR, and CDR adjusted for pathology confirmation rate were calculated for each of three centers and pre-MRI biopsy status (biopsy-naive and previous negative biopsy).

RESULTS

A total of 9,686 examinations (8,643 unique patients) were included. AIR, CDR, and CDR adjusted for pathology confirmation rate were 45.4%, 23.8%, and 27.6% for center I; 47.2%, 20.0%, and 22.8% for center II; and 42.3%, 27.2%, and 30.1% for center III, respectively. Pathology confirmation rate ranged from 81.6% to 88.0% across three centers. AIR and CDR for biopsy-naive patients were 45.5% to 52.6% and 24.2% to 33.5% across three centers, respectively, and those for previous negative biopsy were 27.2% to 39.8% and 11.7% to 14.2% across three centers, respectively.

CONCLUSION

We reported CDR and AIR in prostate MRI for clinical suspicion of PCa. CDR needs to be adjusted for pathology confirmation rate and pre-MRI biopsy status for interfacility comparison.

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.

Inter-reader variability and reproducibility of the PI-QUAL score in a multicentre setting

PURPOSE

To assess the inter-reader reproducibility of the Prostate Imaging Quality (PI-QUAL) score between readers with varying clinical experience and its reproducibility at assessing imaging quality between different institutions.

METHODS

Following IRB approval, we assessed 60 consecutive prostate MRI scans performed at different academic teaching and non-academic hospitals uploaded to our institutes' PACS for second opinion or discussion in case conferences. Anonymized scans were independently reviewed using the PI-QUAL scoring sheet by three readers - two radiologists (with 1 and 12 years Prostate MRI reporting experience), and an experienced MRI technician with interest in image acquisition and quality. All readers were blinded to the site where scans were acquired.

RESULTS

Agreement coefficients between the 3 readers in paired comparison for each individual PI-QUAL score was moderate. When the scans were clustered into 2 groups according to their ability to rule in or rule out clinically significant prostate cancer [i.e., PI-QUAL score 1-3 vs PI-QUAL score 4-5], the Gwet AC1 coefficients between the three readers in paired comparison was good to very good [Gwet AC 1:0.77, 0.67, 0.836 respectively] with agreement percentage of 88.3%, 83.3% and 91.7% respectively. Agreement coefficient was higher between the experienced radiologist and the experienced MRI technician than between the less experienced trainee radiologist and the other two readers. The mean PI-QUAL score provided by each reader for the scans was significantly higher in the academic hospitals (n = 32) compared to the community hospital (n = 28) [experienced radiologist 4.6 vs 2.9; trainee radiologist 4.5 vs 2.4; experienced technologist 4.4 vs 2.4; p value < 0.001].

CONCLUSION

We observed good to very good reproducibility in the assessment of each MRI sequence and when scans were clustered into two groups [PI-QUAL 1-3 vs PI-QUAL 4-5] between readers with varying clinical experience. However, the reproducibility for each single PI-QUAL score between readers was moderate. Better definitions for each PI-QUAL score criteria may further improve reproducibility between readers. Additionally, the mean PI-QUAL score provided by all three readers was significantly higher for scans performed at academic teaching hospitals compared to community hospital.

Global Variation in Magnetic Resonance Imaging Quality of the Prostate

Background High variability in prostate MRI quality might reduce accuracy in prostate cancer detection. Purpose To prospectively evaluate the quality of MRI scanners taking part in the quality control phase of the global PRIME (Prostate Imaging Using MRI ± Contrast Enhancement) trial using the Prostate Imaging Quality (PI-QUAL) standardized scoring system, give recommendations on how to improve the MRI protocols, and establish whether MRI quality could be improved by these recommendations. Materials and Methods In the prospective clinical trial (PRIME), for each scanner, centers performing prostate MRI submitted five consecutive studies and the MRI protocols (phase I). Submitted data were evaluated in consensus by two expert genitourinary radiologists using the PI-QUAL scoring system that evaluates MRI diagnostic quality using five points (1 and 2 = nondiagnostic; 3 = sufficient; 4 = adequate, 5 = optimal) between September 2021 and August 2022. Feedback was provided for scanners not achieving a PI-QUAL 5 score, and centers were invited to resubmit new imaging data using the modified protocol (phase II). Descriptive comparison of outcomes was made between the MRI scanners, feedback provided, and overall PI-QUAL scores. Results In phase I, 41 centers from 18 countries submitted a total of 355 multiparametric MRI studies from 71 scanners, with nine (13%) scanners achieving a PI-QUAL score of 3, 39 (55%) achieving a score of 4, and 23 (32%) achieving a score of 5. Of the 48 (n = 71 [68%]) scanners that received feedback to improve, the dynamic contrast-enhanced sequences were those that least adhered to the Prostate Imaging Reporting and Data System, version 2.1, criteria (44 of 48 [92%]), followed by diffusion-weighted imaging (20 of 48 [42%]) and T2-weighted imaging (19 of 48 [40%]). In phase II, 36 centers from 17 countries resubmitted revised studies, resulting in a total of 62 (n = 64 [97%]) scanners with a final PI-QUAL score of 5. Conclusion Substantial variation in global prostate MRI acquisition parameters as a measure of quality was observed, particularly with DCE sequences. Basic evaluation and modifications to MRI protocols using PI-QUAL can lead to substantial improvements in quality. Clinical trial registration no. NCT04571840 Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Almansour and Chernyak in this issue.