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

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.

Ultra-fast biparametric MRI in prostate cancer assessment: Diagnostic performance and image quality compared to conventional multiparametric MRI

Purpose

To compare the diagnostic performance and image quality of a deep-learning-assisted ultra-fast biparametric MRI (bpMRI) with the conventional multiparametric MRI (mpMRI) for the diagnosis of clinically significant prostate cancer (csPCa).

Methods

This prospective single-center study enrolled 123 biopsy-naïve patients undergoing conventional mpMRI and additionally ultra-fast bpMRI at 3 T between 06/2023-02/2024. Two radiologists (R1: 4 years and R2: 3 years of experience) independently assigned PI-RADS scores (PI-RADS v2.1) and assessed image quality (mPI-QUAL score) in two blinded study readouts. Weighted Cohen's Kappa (κ) was calculated to evaluate inter-reader agreement. Diagnostic performance was analyzed using clinical data and histopathological results from clinically indicated biopsies.

Results

Inter-reader agreement was good for both mpMRI (κ = 0.83) and ultra-fast bpMRI (κ = 0.87). Both readers demonstrated high sensitivity (≥94 %/≥91 %, R1/R2) and NPV (≥96 %/≥95 %) for csPCa detection using both protocols. The more experienced reader mostly showed notably higher specificity (≥77 %/≥53 %), PPV (≥62 %/≥45 %), and diagnostic accuracy (≥82 %/≥65 %) compared to the less experienced reader. There was no significant difference in the diagnostic performance of correctly identifying csPCa between both protocols (p > 0.05). The ultra-fast bpMRI protocol had significantly better image quality ratings (p < 0.001) and achieved a reduction in scan time of 80 % compared to conventional mpMRI.

Conclusion

Deep-learning-assisted ultra-fast bpMRI protocols offer a promising alternative to conventional mpMRI for diagnosing csPCa in biopsy-naïve patients with comparable inter-reader agreement and diagnostic performance at superior image quality. However, reader experience remains essential for diagnostic performance.

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.

A pictorial essay of PI-RADS pearls and pitfalls: toward less ambiguity and better practice

Prostate Imaging Reporting and Data System (PI-RADS) was designed to standardize the interpretation of multiparametric magnetic resonance imaging (MRI) of the prostate, aiding in assessing the probability of clinically significant prostate cancer. By providing a structured scoring system, it enables better risk stratification, guiding decisions regarding the need for biopsy and subsequent treatment options. In this article, we explore both the strengths and weaknesses of PI-RADS, offering insights into its updated diagnostic performance and clinical applications, while also addressing potential pitfalls using diverse, representative MRI cases.

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.