Negative mpMRI Rules Out Extra-Prostatic Extension in Prostate Cancer before Robot-Assisted Radical Prostatectomy

Intraoperative technologies to assess margin status during radical prostatectomy - a narrative review

Positive surgical margin (PSM) is a frequent concern for surgeons performing radical prostatectomy for prostate cancer (PCa). PSM are recognized as risk factors for earlier biochemical recurrence and expose patients to adjuvant or salvage treatments such as external radiotherapy and hormonotherapy. Several strategies have been established to reduce PSM rate, while still allowing safe nerve-sparing surgery. Precise preoperative staging by multiparametric magnetic resonance imaging (mpMRI) and fusion biopsy is recommended to identify suspicious areas of extracapsular extension (ECE) that warrant special attention during dissection. However, even with optimal imaging, ECE can be missed, some cancers are not well defined or visible, and capsular incision during surgery remains an issue. Hence, intraoperative frozen section techniques, such as the neurovascular structure-adjacent frozen section examination (NeuroSAFE) have been developed and lately widely disseminated. The NeuroSAFE technique reduces PSM rate while allowing higher rate of nerve-sparing surgery. However, its use is limited to high volume or expert center because of its high barrier-to-entry in terms of logistics, human resources and expertise, as well as cost. Also, NeuroSAFE is a time-consuming process, even in expert hands. To address these issues, several technologies have been developed for an ex vivo and in vivo use. Ex vivo technology such as fluorescent confocal microscopy and intraoperative PET-CT require the extraction of the specimen for preparation, and digital images acquisition. In vivo technology, such as augmented reality based on mpMRI images and PSMA-fluorescent guided surgery have the advantage to provide an intracorporeal analysis of the completeness of the resection. The current manuscript provides a narrative review of established techniques, and details several new and promising techniques for intraoperative PSM assessment.

A systematic review and meta-analysis of the impact of preoperative surgical planning in robotic-assisted radical prostatectomy on trifecta outcomes

INTRODUCTION

Surgical planning in robotic assisted radical prostatectomy (RARP) recommends the maximal use of function persevering techniques without compromising oncological outcomes. There is no consensus on how to define the optimal surgical approach. This review aims to collate available evidence on the impact of preoperative planning interventions on the trifecta of oncological, functional or operative outcomes.

EVIDENCE ACQUISITION

A systematic review according to the PRISMA guidelines was performed using the terms ((prostatectomy) AND (robot*)) AND (plan*) OR (image*) OR (decision*) for articles published between January 2000 and January 2024. Prospective studies reporting patients undergoing RARP with a preoperative planning intervention, compared to no planning, to determine at least one of trifecta outcome were included. Results were synthesized in a narrative review with a metanalysis when two or more studies reported the same outcomes.

EVIDENCE SYNTHESIS

Eight studies, one RCT and seven non-randomised prospective comparative studies, including 1945 patients, applying clinical nomograms, MRI and histology review were included. The outcomes reported were positive surgical margins (PSM) (oncological) and nerves sparing rates (functional). No operative outcomes were reported. Metanalysis demonstrated that positive surgical margins (PSM) were reduced in both clinical nomogram (RR=0.56, 95% CI: 0.37-0.87, P=0.009; two studies 563 patients) and MRI (RR=0.72, 95% CI: 0.54-0.96, P=0.02; three studies, 801 patients) intervention groups. Additionally, metanalysis of all nerve-sparing cases demonstrated lower PSM rates in the intervention group (RR=0.65, 95% CI: 0.47-0.90, P=0.01; three studies, 823 patients). No significant changes were seen in nerve-sparing rates.

CONCLUSIONS

Preoperative surgical planning with nomograms and MRI has the potential to improve PSM rates without compromising nerve sparing. It is not possible to identify the optimal approach, but it is likely that the incorporation of biopsy and MRI information will lead to the best outcomes. Further studies using universally accepted standards of the trifecta outcomes are needed.

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.

Accuracy of the LaserSAFE technique for detecting positive surgical margins during robot-assisted radical prostatectomy: blind assessment and inter-rater agreement analysis

INTRODUCTION AND OBJECTIVES

Fluorescence confocal microscopy (FCM) is a new imaging modality capable of generating digital microscopic resolution scans of fresh surgical specimens, and holds potential as an alternative to frozen section (FS) analysis for intra-operative assessment of surgical margins. Previously, we described the LaserSAFE technique as an application of FCM for margin assessment in robot-assisted radical prostatectomy (RARP) using the Histolog® scanner. This study describes the accuracy and inter-rater agreement of FCM imaging compared to corresponding paraffin-embedded analysis (PA) among four blinded pathologists for the presence of positive surgical margins (PSM).

MATERIALS AND METHODS

RARP specimens from patients enrolled in the control arm of the NeuroSAFE PROOF study (NCT03317990) were analysed from April 2022 to February 2023. Prostate specimens were imaged using the Histolog® scanner before formalin fixation and PA. Four trained assessors, blinded to PA, reviewed and analysed FCM images of the posterolateral prostatic surface.

RESULTS

A total of 31 prostate specimens were included in the study. PA per lateral side of the prostate identified 11 instances of positive margins. Among the four histopathologists included in our study, FCM achieved a sensitivity of 73-91 and specificity of 94-100% for the presence of PSM. Fleiss' Kappa for inter-rater agreement on PSM was 0.78 (95% confidence interval = 0.64-0.92), indicating substantial agreement.

CONCLUSION

This blinded analysis of FCM versus PA among histopathologists with different experience levels demonstrated high accuracy and substantial inter-rater agreement for diagnosing PSM. This supports the role of the FCM as an alternative to FS.

Impact of Endorectal Coil Use on Extraprostatic Extension Detection in Prostate MRI: A Retrospective Monocentric Study

RATIONALE AND OBJECTIVES

Accurate preoperative mpMRI-based detection of extraprostatic extension (EPE) in prostate cancer (PCa) is critical for surgical planning and patient outcomes. This study aims to evaluate the impact of endorectal coil (ERC) use on the diagnostic performance of mpMRI in detecting EPE.

MATERIALS AND METHODS

This retrospective study with prospectively collected data included participants who underwent mpMRI and subsequent radical prostatectomy for PCa between 2007 and 2024. Participants were divided based on ERC use on mpMRI: MRI without ERC and with ERC. Surgical pathology reports were used to determine the patients with pathologic EPE on whole-mount histopathology. One radiologist evaluated mpMRI using an in-house (National Cancer Institute [NCI]) EPE grading system. Logistic regression (LR) analyses were conducted to identify significant predictors of pathologic EPE, including ERC use and NCI EPE grades.

RESULTS

934 men (median age: 62 years [IQR = 57-67]) were included. For NCI EPE grade≥1, ERC MRI group (n = 612) had higher NPV (91% [320/353] vs. 83% [166/200], p = 0.01) and sensitivity (75% [101/134] vs. 62% [56/90], p = 0.04) compared to non-ERC group (n = 322). For NCI EPE grade = 3, ERC MRI group had higher NPV (83% [452/546] vs. 75% [221/294], p = 0.01) and accuracy (80% [492/612] vs. 74% [238/322], p = 0.03). In multivariable LR, higher NCI EPE grades were strong independent predictors of pathologic EPE, irrespective of ERC use (NCI EPE grade 2 with ERC: odds ratio [OR] = 2.01, p = 0.04; without ERC: OR = 5.63, p<0.001, NCI EPE grade 3 with ERC: OR = 4.53, p<0.001; without ERC: OR = 5.22, p = 0.002).

CONCLUSION

ERC improves sensitivity, NPV, accuracy of EPE detection with mpMRI at different NCI EPE thresholds. NCI EPE grading system remains the stronger independent predictor of pathologic EPE regardless of ERC use.

Effectiveness of cognitive fusion transrectal ultrasound prostate biopsy when compared with final prostatectomy histology

INTRODUCTION AND OBJECTIVES

Prostate cancer (PCa) is the second most commonly diagnosed cancer in men. Cognitive fusion transrectal ultrasound prostate biopsy is one of several modalities for diagnosing this disease. However, no existing studies have shown the clear superiority of one image-guided technique over another. This investigation aimed to evaluate the efficacy of targeted biopsy through cognitive guidance, as well as to assess the accuracy of multiparametric magnetic resonance imaging (mpMRI) in the detection of PCa compared to the specimen obtained by radical prostatectomy (RP).

MATERIALS AND METHODS

We conducted a retrospective observational single-center study approved by the ethical committee, including men with prostate-specific antigen (PSA) levels between 2-10 mg/ml who underwent RP and cognitive fusion biopsy (CFB) between 2017 January and 2022 January.

RESULTS

A total of 639 patients were analyzed, 83 of whom met the inclusion criteria and were enrolled in this study. The overall rate of PCa detection with CFB was 79.5% (median of specific PCa detection was 100%), and the rate of detecting clinically significant prostate cancer (csPCa) was 74.7%. In addition, there was 42.2% agreement between the International Society of Urological Pathology (ISUP) score of the CFB and the RP specimen, which increased to 56.6% when the systematic biopsy was added. Regarding the accuracy of mpMRI, several parameters were evaluated with respect to RP sample histology. Of these, tumor location had a total match rate of 39.8% and a partial match rate of 55.4%. Moreover, regarding extraprostatic extension (EPE), the present study found a significant association between the RP specimen and mpMRI (p = 0.002), with an agreement rate of 60% if it was present in the histology and 79.5% if it was not. Additionally, larger prostates and tumors located in the transition zone were significantly associated with a lower CFB accuracy (p = 0.001 and p = 0.030, respectively). After adjusting for all variables evaluated, only prostate volume remains statistically significant (p = 0.029).

CONCLUSIONS

In this study, we conclude that mpMRI is highly accurate, allowing good characterization of suspicious tumors and reasonably guiding cognitive biopsy. However, the use of both targeted biopsy through cognitive guidance and systematic biopsy increases the diagnostic accuracy for PCa. Although there is no recommendation in the current literature for one guiding technique over another, we believe that cognitive-guided biopsy should only be reserved for centers with no access to ultrasound or magnetic resonance fusion software.

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.

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.

Preoperative prostate magnetic resonance imaging does not impact surgical outcomes of radical prostatectomy

Objective

We reviewed our institutional experience of radical prostatectomy with and without preoperative multiparametric magnetic resonance imaging (mpMRI) to assess the impact of preoperative prostate mpMRI on surgical outcomes of radical prostatectomy.

Methods

We identified patients at our institution who underwent radical prostatectomy for prostate cancer (PCa) between January 2012 and December 2017 (n = 1044). Using propensity scoring analysis, patients who underwent preoperative mpMRI (n = 285) were matched 1:1 to patients who did not receive preoperative mpMRI (n = 285). Multivariable regression analysis was performed to identify factors predictive of operative time, estimated blood loss (EBL), lymph node yield, rates of complications within 30 days, and positive surgical margin (PSM).

Results

There were no significant differences in operative time, EBL, PSM, lymph node yield, or complication rates between the two cohorts. Multivariable analysis demonstrated that preoperative mpMRI was not predictive of the measured perioperative outcomes. Significant comorbidity (Charlson Comorbidity Index ≥3) was the sole predictor of perioperative complications (P = 0.015). Increasing biopsy Gleason score predicted increased lymph node yield (P < 0.001). The probability of PSM was associated with increasing preoperative prostate-specific antigen (odds ratio 1.036, P = 0.009). Body mass index was a predictor of operative time (P = 0.016) and EBL (P = 0.001).

Conclusions

Although preoperative mpMRI has an important role in the diagnosis and staging of PCa, it does not impact perioperative radical prostatectomy outcomes. Our findings do not support the routine use of preoperative mpMRI for surgical planning in patients already diagnosed with clinically localized PCa.

Deep learning-based image quality assessment: impact on detection accuracy of prostate cancer extraprostatic extension on MRI

OBJECTIVE

To assess impact of image quality on prostate cancer extraprostatic extension (EPE) detection on MRI using a deep learning-based AI algorithm.

MATERIALS AND METHODS

This retrospective, single institution study included patients who were imaged with mpMRI and subsequently underwent radical prostatectomy from June 2007 to August 2022. One genitourinary radiologist prospectively evaluated each patient using the NCI EPE grading system. Each T2WI was classified as low- or high-quality by a previously developed AI algorithm. Fisher's exact tests were performed to compare EPE detection metrics between low- and high-quality images. Univariable and multivariable analyses were conducted to assess the predictive value of image quality for pathological EPE.

RESULTS

A total of 773 consecutive patients (median age 61 [IQR 56-67] years) were evaluated. At radical prostatectomy, 23% (180/773) of patients had EPE at pathology, and 41% (131/318) of positive EPE calls on mpMRI were confirmed to have EPE. The AI algorithm classified 36% (280/773) of T2WIs as low-quality and 64% (493/773) as high-quality. For EPE grade ≥ 1, high-quality T2WI significantly improved specificity for EPE detection (72% [95% CI 67-76%] vs. 63% [95% CI 56-69%], P = 0.03), but did not significantly affect sensitivity (72% [95% CI 62-80%] vs. 75% [95% CI 63-85%]), positive predictive value (44% [95% CI 39-49%] vs. 38% [95% CI 32-43%]), or negative predictive value (89% [95% CI 86-92%] vs. 89% [95% CI 85-93%]). Sensitivity, specificity, PPV, and NPV for EPE grades ≥ 2 and ≥ 3 did not show significant differences attributable to imaging quality. For NCI EPE grade 1, high-quality images (OR 3.05, 95% CI 1.54-5.86; P < 0.001) demonstrated a stronger association with pathologic EPE than low-quality images (OR 1.76, 95% CI 0.63-4.24; P = 0.24).

CONCLUSION

Our study successfully employed a deep learning-based AI algorithm to classify image quality of prostate MRI and demonstrated that better quality T2WI was associated with more accurate prediction of EPE at final pathology.

Self-supervised multicontrast super-resolution for diffusion-weighted prostate MRI

PURPOSE

This study addresses the challenge of low resolution and signal-to-noise ratio (SNR) in diffusion-weighted images (DWI), which are pivotal for cancer detection. Traditional methods increase SNR at high b-values through multiple acquisitions, but this results in diminished image resolution due to motion-induced variations. Our research aims to enhance spatial resolution by exploiting the global structure within multicontrast DWI scans and millimetric motion between acquisitions.

METHODS

We introduce a novel approach employing a "Perturbation Network" to learn subvoxel-size motions between scans, trained jointly with an implicit neural representation (INR) network. INR encodes the DWI as a continuous volumetric function, treating voxel intensities of low-resolution acquisitions as discrete samples. By evaluating this function with a finer grid, our model predicts higher-resolution signal intensities for intermediate voxel locations. The Perturbation Network's motion-correction efficacy was validated through experiments on biological phantoms and in vivo prostate scans.

RESULTS

Quantitative analyses revealed significantly higher structural similarity measures of super-resolution images to ground truth high-resolution images compared to high-order interpolation (p< $$ < $$ 0.005). In blind qualitative experiments,96 . 1 % $$ 96.1\% $$ of super-resolution images were assessed to have superior diagnostic quality compared to interpolated images.

CONCLUSION

High-resolution details in DWI can be obtained without the need for high-resolution training data. One notable advantage of the proposed method is that it does not require a super-resolution training set. This is important in clinical practice because the proposed method can easily be adapted to images with different scanner settings or body parts, whereas the supervised methods do not offer such an option.

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.

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.

MR image quality in local staging of prostate cancer: Role of PI-QUAL in the detection of extraprostatic extension

PURPOSE

To assess the impact of prostate MRI image quality by means of the Prostate Imaging Quality (PI-QUAL) score, on the identification of extraprostatic extension of disease (EPE), predicted using the EPE Grade Score, Likert Scale Score (LSS) and a clinical nomogram (MSKCCn).

METHODS

We retrospectively included 105 patients with multiparametric prostate MRI prior to prostatectomy. Two radiologists evaluated image quality using PI-QUAL (≥4 was considered high quality) in consensus. All cases were also scored using the EPE Grade, the LSS, and the MSKCCn (dichotomized). Inter-rater reproducibility for each score was also assessed. Accuracy was calculated for the entire population and by image quality, considering two thresholds for EPE Grade (≥2 and = 3) and LSS (≥3 and ≥ 4) and using McNemar's test for comparison.

RESULTS

Overall, 66 scans achieved high quality. The accuracy of EPE Grade ranged from 0.695 to 0.743, while LSS achieved values between 0.705 and 0.733. Overall sensitivity for the radiological scores (range = 0.235-0.529) was low irrespective of the PI-QUAL score, while specificity was higher (0.775-0.986). The MSKCCn achieved an AUC of 0.76, outperforming EPE Grade (=3 threshold) in studies with suboptimal image quality (0.821 vs 0.564, p = 0.016). EPE Grade (=3 threshold) accuracy was also better in high image quality studies (0.849 vs 0.564, p = 0.001). Reproducibility was good to excellent overall (95 % Confidence Interval range = 0.782-0.924).

CONCLUSION

Assessing image quality by means of PI-QUAL is helpful in the evaluation of EPE, as a scan of low quality makes its performance drop compared to clinical staging tools.

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.

Monoparametric high-resolution diffusion weighted MRI as a possible first step in an MRI-directed diagnostic pathway for men with suspicion of prostate cancer

Purpose

To explore if a high-resolution diffusion weighted MRI sequence (DWI-only) could be used as a first step in an MRI-directed diagnostic pathway.

Methods

Prospective single center study that between December 2017 and August 2018 included 129 consecutive patients with suspicion of prostate cancer into a PI-RADS-based MRI-directed diagnostic pathway. All patients had multiparametric MRI (mpMRI). Based on only the transversal high-resolution DWI images two consultant radiologists prospectively categorized the findings as positive, equivocal, or negative for clinically significant cancer. The radiologists then interpreted the mpMRI and assigned a PI-RADS score. A third independent reader retrospectively categorized the DWI-only exams without access to the mpMRI. The interpretations of DWI-only were compared to the PI-RADS classification from mpMRI and the histopathology from the biopsies. Non-biopsied patients were followed in a safety net monitoring for 56 months.

Results

Based on DWI-only, 29 (22.5%) of the exams were categorized as negative, 38 (29.5%) as equivocal and 62 (48.1%) as positive. Of the 56 patients with PI-RADS 4-5 at mpMRI, 55 were also categorized as positive at DWI-only. All patients diagnosed with clinically significant cancer were identified using DWI-only. 56 months of safety net monitoring did not reveal any clinically significant cancers among patients with exams categorized as negative or equivocal. There was high inter-reader agreement on positive findings, but less agreement on negative and equivocal findings.

Conclusions

In this concept study, the monoparametric DWI-only identified all patients with clinically significant cancer in a mpMRI-directed diagnostic pathway.

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.