Evaluating the size criterion for PI-RADSv2 category 5 upgrade: is 15 mm the best threshold?

Effects of the lesion size on clinically significant prostate cancer detection rates in PI-RADS category 3-5 lesions

INTRODUCTION

Prostate cancer (PCa) ranks second among prevalent cancers in men, necessitating effective screening tools such as multiparametric magnetic resonance imaging (mpMRI) with the prostate imaging reporting and data system (PI-RADS) classification. This study explores the impact of lesion volume on clinically significant prostate cancer (csPCa) detection rates in PI-RADS 3-5 lesions, aiming to contribute insights into the underexplored relationship between lesion size and csPCa detection.

MATERIALS AND METHODS

A retrospective analysis was conducted on data from 754 patients undergoing mpMRI-guided transrectal ultrasound (TRUS) prostate biopsy between January 2016 and 2023. Patients with PI-RADS 3, 4, and 5 lesions were included. Lesion size and PI-RADS categories were assessed through mpMRI, followed by MR fusion biopsy.

RESULTS

Of the patients, 33.7%, 52.3%, and 14.1% had PI-RADS 3, 4, and 5 lesions, respectively. Lesion sizes correlated significantly with csPCa detection in PI-RADS 4 and 5 categories. For PI-RADS 3 lesions, no significant differences in csPCa rates were observed based on lesion size. However, in PI-RADS 4 and 5 groups, larger lesions showed higher csPCa rates.

CONCLUSION

This study suggests that subgroup categorizations based on lesion volume could predict clinically significant PCa with high accuracy, potentially reducing unnecessary biopsies and associated overtreatment. Future research should further explore the relationship between lesion size and csPCa, clarifying discussions regarding the inclusion of systematic biopsies in diagnostic protocols.

ProLesA-Net: A multi-channel 3D architecture for prostate MRI lesion segmentation with multi-scale channel and spatial attentions

Prostate cancer diagnosis and treatment relies on precise MRI lesion segmentation, a challenge notably for small (<15 mm) and intermediate (15-30 mm) lesions. Our study introduces ProLesA-Net, a multi-channel 3D deep-learning architecture with multi-scale squeeze and excitation and attention gate mechanisms. Tested against six models across two datasets, ProLesA-Net significantly outperformed in key metrics: Dice score increased by 2.2%, and Hausdorff distance and average surface distance improved by 0.5 mm, with recall and precision also undergoing enhancements. Specifically, for lesions under 15 mm, our model showed a notable increase in five key metrics. In summary, ProLesA-Net consistently ranked at the top, demonstrating enhanced performance and stability. This advancement addresses crucial challenges in prostate lesion segmentation, enhancing clinical decision making and expediting treatment processes.

Extra-prostatic extension grading system: correlation with MRI features and integration of capsular enhancement sign for "enhanced" detection of T3a lesions

PURPOSE

This study aims to confirm the diagnostic accuracy of extra-prostatic extension (EPE) grading system and to explore the predictive capabilities of the prostate MRI while considering various MRI features such as lesion location, apparent diffusion coefficient (ADC) values and capsular enhancement sign (CES).

METHODS

Our monocentric study is based on a retrospective analysis of 99 patients who underwent radical prostatectomy from January 2021 to January 2023. The observers reviewed for each lesion, including location (transitional or peripheral zone, anterior or posterior location), capsular contact length, irregular bulging of the capsule, asymmetry of the neurovascular bundle, obliteration of the recto-prostatic angle, macroscopic EPE, ADC value, and CES.

RESULTS

Among 99 patients, 31 patients had EPE. Lesions with EPE have broadercapsule contact (24 mm vs 12 mm) with contact ≥14 mm being the optimal cut-off for EPE discrimination. Among the morphological MRI criteria used to determine the EPE, the one with major sensitivity was shown to be bulging (sen 81%), while macroscopic extension had highest specificity (100%). Univariate analysis showed as significative risk factors for EPE: capsular contact ≥14 mm (P < .001), International Society of Urological Pathology score ≥3 (P = .005), CES (P < .001), bulging (P = .001), neurovascular bundle asymmetry (P < .001) and EPE score ≥2 (P < .001), and in multivariate analysis CES (P = .001) and EPE score ≥2 (P = .004) were significant. The AUC of the EPE score was 0.76, raised to 0.83 when combining it with CES (P = .11).

CONCLUSION

CES in the setting of multiparametric MRI can increase diagnostic accuracy for the prediction of extracapsular disease.

ADVANCES IN KNOWLEDGE

This study highlights the potential of contrast media in prostate cancer local staging.

Optimal size threshold for PIRADSv2 category 5 upgrade and its positive predictive value: is it predictive of "very high" likelihood of clinically-significant cancer?

AIM

To identify the optimal size metric and threshold for Prostate Imaging Reporting and Data System (PIRADS) 5 upgrade, calculate its positive predictive value (PPV) for clinically-significant prostate cancer (csPCA), and determine if it is indicative of a "very high" likelihood of csPCA.

MATERIALS AND METHODS

One hundred and forty-three PIRADS 4 or 5 lesions were evaluated. Lesion diameters were used to calculate lesion volume (LV). Pearson correlation between maximum lesion diameter (MLD) and LV was calculated. Area under the curve (AUC) for discriminating csPCA (Gleason grade ≥ 3 + 4) was calculated using MLD and LV. Optimal size thresholds (using Youden index) and highly predictive size thresholds were identified for the whole prostate (WP), peripheral zone (PZ), and transitional zone (TZ).

RESULTS

There was high correlation between MLD and LV (r=0.77-0.81), with comparable AUCs for MLD and LV in the identification of csPCA in the WP (0.73, 0.72), PZ (0.73, 0.73), and TZ (0.79, 0.75). Optimal MLD thresholds were 1.4, 1.4, and 1.6 cm in the WP, PZ, and TZ respectively, with PPVs of 76%, 81%, and 69%, respectively. An MLD threshold of 2.7 cm would be needed in the WP to achieve a PPV approaching 90%, with sensitivity decreasing to 10%.

CONCLUSIONS

There is high correlation between MLD and LV with comparable discrimination of csPCA using each. PIRADSv2's 1.5 cm MLD threshold is near the optimal threshold for PIRADS 5 upgrade but has moderate PPV. A much higher threshold would be needed to increase its PPV, with significant sacrifice in sensitivity.

Higher Preoperative Maximum Standardised Uptake Values (SUVmax) Are Associated with Higher Biochemical Recurrence Rates after Robot-Assisted Radical Prostatectomy for [68Ga]Ga-PSMA-11 and [18F]DCFPyL Positron Emission Tomography/Computed Tomography

This study aimed to investigate the association between the 68Ga- or 18F-radiolabeled prostate-specific membrane antigen (PSMA) tracer expression, represented by the maximum standardised uptake value (SUVmax) of the dominant intraprostatic lesion, and biochemical recurrence (BCR) in primary prostate cancer (PCa) patients prior to robot-assisted radical prostatectomy (RARP). This was a retrospective, multi-centre cohort study of 446 patients who underwent [68Ga]Ga-PSMA-11 (n = 238) or [18F]DCFPyL (n = 206) Positron Emission Tomography/Computed Tomography (PET/CT) imaging prior to RARP. SUVmax was measured in the dominant intraprostatic PCa lesions. [18F]DCFPyL patients were scanned 60 ([18F]DCFPyL-60; n = 106) or 120 ([18F]DCFPyL-120; n = 120) minutes post-injection of a radiotracer and were analysed separately. To normalise the data, SUVmax was log transformed for further analyses. During a median follow-up of 24 months, 141 (30.4%) patients experienced BCR. Log2SUVmax was a significant predictor for BCR (p < 0.001). In the multivariable analysis accounting for these preoperative variables: initial prostate-specific antigen (PSA), radiologic tumour stage (mT), the biopsy International Society of Urological Pathology grade group (bISUP) and the prostate imaging and reporting data system (PI-RADS), Log2SUVmax was found to be an independent predictor for BCR in [68Ga]Ga-PSMA-11 (HR 1.32, p = 0.04) and [18F]DCFPyL-120 PET/CT scans (HR 1.55, p = 0.04), but not in [18F]DCFPyL-60 ones (HR 0.92, p = 0.72). The PSMA expression of the dominant intraprostatic lesion proved to be an independent predictor for BCR in patients with primary PCa who underwent [68Ga]Ga-PSMA-11 or [18F]DCFPyL-120 PET/CT scans, but not in those who underwent [18F]DCFPyL-60 PET/CT scans.

Utilizing lesion diameter and prostate specific antigen density to decide on magnetic resonance imaging guided confirmatory biopsy of prostate imaging reporting and data system score three lesions in African American prostate cancer patients managed with active surveillance

OBJECTIVE

The objective of the study is to identify the rate of significant prostate cancer (PCa) detection in PI-RADS3 lesions in AA patients stratified by PSAD threshold of < 0.15 vs. ≥ 0.15 ng/ml² and lesion diameter of < 1 cm vs ≥ 1 cm.

METHODS

We analyzed our institutional database of MRI-TB to identify the rate of significant prostate cancer (PCa) detection in PI-RADS3 lesions in AA patients stratified by PSAD threshold of < 0.15 vs. ≥ 0.15 ng/ml² and lesion diameter of < 1 cm vs ≥ 1 cm. Significant prostate cancer was defined as Gleason grade group 2 or higher on MRI-TB of the PI-RADS 3 lesion.

RESULTS

Of 768 patients included in the database, 211 (27.5%) patients identified themselves as AAs. Mean age of AA patients was 63 years and mean PSAD was 0.21. Sixty nine (32.7%) AA patients were found to have PI-RADS 3 lesions. Mean PSAD of AA patients with PI-RADS 3 lesions was 0.21 ng/ml² as well. Fifty percent of AA patients with PI-RADS 3 lesions had PSAD ≥ 0.15 ng/ml². Significant PCa detection rate for AA patients with PI-RADS 3 lesions was 9% for PSAD of ≥ 0.15 vs. 0.03% percent for AA patients with PSAD < 0.15 ng/ml² (OR 7.056, CI 1.017-167.9, P = 0.04). Stratification by lesion diameter (< 1 cm vs. > 1 cm) resulted in missing 0% of significant PCa when only AA patients with PSAD ≥ 0.15 ng/ml² and lesion diameter ≥ 1 cm received MRI-TB.

CONCLUSIONS

We report on the performance of a reported PSAD density threshold in detecting significant PCa in one of the largest series of AA patients receiving MRI-TB of the prostate. Our results have direct clinical implications when counseling AA patients with PI-RADS 3 lesion on whether they should undergo MRI-TB of such lesions.

Pharmacokinetic modeling of dynamic contrast-enhanced (DCE)-MRI in PI-RADS category 3 peripheral zone lesions: preliminary study evaluating DCE-MRI as an imaging biomarker for detection of clinically significant prostate cancers

PURPOSE

To determine if pharmacokinetic modeling of DCE-MRI can diagnose CS-PCa in PI-RADS category 3 PZ lesions with subjective negative DCE-MRI.

MATERIALS AND METHODS

In the present IRB approved, bi-institutional, retrospective, case-control study, we identified 73 men with 73 PZ PI-RADS version 2.1 category 3 lesions with MRI-directed-TRUS-guided targeted biopsy yielding: 12 PZ CS-PCa (ISUP Grade Group 2; N = 9, ISUP 3; N = 3), 27 ISUP 1 PCa and 34 benign lesions. An expert blinded radiologist segmented lesions on ADC and DCE images; segmentations were overlayed onto pharmacokinetic DCE-MRI maps. Mean values were compared between groups using univariate analysis. Diagnostic accuracy was assessed by ROC.

RESULTS

There were no differences in age, PSA, PSAD or clinical stage between groups (p = 0.265-0.645). Mean and 10th percentile ADC did not differ comparing CS-PCa to ISUP 1 PCa and benign lesions (p = 0.376 and 0.598) but was lower comparing ISUP ≥ 1 PCa to benign lesions (p < 0.001). Mean Ktrans (p = 0.003), Ve (p = 0.003) but not Kep (p = 0.387) were higher in CS-PCa compared to ISUP 1 PCa and benign lesions. There were no differences in DCE-MRI metrics comparing ISUP ≥ 1 PCa and benign lesions (p > 0.05). AUC for diagnosis of CS-PCa using Ktrans and Ve were: 0.69 (95% CI 0.52-0.87) and 0.69 (0.49-0.88).

CONCLUSION

Pharmacokinetic modeling of DCE-MRI parameters in PI-RADS category 3 lesions with subjectively negative DCE-MRI show significant differences comparing CS-PCa to ISUP 1 PCa and benign lesions, in this study outperforming ADC. Studies are required to further evaluate these parameters to determine which patients should undergo targeted biopsy for PI-RADS 3 lesions.

Ancillary imaging and clinical features for the characterization of prostate lesions: A proposed approach to reduce false positives

The relatively low specificity and positive predictive value of the Prostate Imaging-Reporting and Data System (PI-RADS) can lead to considerable false-positive results and unnecessary biopsies. The aim of this study was to propose ancillary features (AFs) indicating clinically significant prostate cancer (csPCa) or benign tissues in PI-RADS category ≥3 lesions and determine the usefulness of these AFs in reducing false-positive assessments of suspicious lesions in men at csPCa risk. This was a retrospective study, which included 199 men. A 3T, including turbo spin echo T₂ -weighted, echo-planar diffusion-weighted, and spoiled gradient echo dynamic contrast-enhanced (DCE) images, was used. Five AFs (prostate-specific antigen density ≥0.15 ng/mL² ; size ≥10 mm; heterogeneous T2 signal intensity; circumscribed nodule in the junction of peripheral and transition zone; and DCE time curves) indicating csPCa or non-csPCa were evaluated by three independent readers. The sensitivity and specificity of each AF were calculated. Inter-reader agreement was evaluated using κ statistics. Univariate and multivariate logistic regression analyses were conducted to determine significant AFs. The reduction in positive call rates and csPCa detection rates with combined AF use were calculated and compared with the findings obtained with PI-RADS use alone. The sensitivities and specificities of the AFs indicating csPCa were 72.1%-96.5% and 27.4%-75.2% for reader 1, 66.3%-96.5% and 23.9%-62.0% for reader 2, and 67.4%-96.5% and 34.5%-78.8% for reader 3, with moderate to substantial inter-reader agreement (Fleiss κ, 0.551-0.643). The combined use of two or more AFs for assessing PI-RADS ≥3 lesions resulted in a 19.6%-30.7% reduction in positive calls (p < .05) compared to PI-RADS use alone while preserving the csPCa detection rates (p ≥ .06) for three readers. The use of AFs in combination with PI-RADS can reduce positive calls and false positives without csPCa under-detection.

Comparison of Likert and PI-RADS version 2 MRI scoring systems for the detection of clinically significant prostate cancer

OBJECTIVE

To compare the performance of Likert and Prostate Imaging-Reporting and Data System (PI-RADS) multiparametric (mp) MRI scoring systems for detecting clinically significant prostate cancer (csPCa).

METHODS

199 biopsy-naïve males undergoing prostate mpMRI were prospectively scored with Likert and PI-RADS systems by four experienced radiologists. A binary cut-off (threshold score ≥3) was used to analyze histological results by three groups: negative, insignificant disease (Gleason 3 + 3; iPCa), and csPCa (Gleason ≥3 +4). Lesion-level results and prostate zonal location were also compared.

RESULTS

129/199 (64.8%) males underwent biopsy, 96 with Likert or PI-RADS score ≥3, and 21 with negative MRI. A further 12 patients were biopsied during follow-up (mean 507 days). Prostate cancer was diagnosed in 87/199 (43.7%) patients, 65 with (33.6%) csPCa. 30/92 (32.6%) patients with negative MRI were biopsied, with an NPV of 83.3% for cancer and 86.7% for csPCa. Likert and PI-RADS score differences were observed in 92 patients (46.2%), but only for 16 patients (8%) at threshold score ≥3. Likert scoring had higher specificity than PI-RADS (0.77 vs 0.66), higher area under the curve (0.92 vs 0.87, p = 0.002) and higher PPV (0.66 vs 0.58); NPV and sensitivity were the same. Likert had more five score results (58%) compared to PI-RADS (36%), but with similar csCPa detection (81.0 and 80.6% respectively). Likert demonstrated lower proportion of false positive in the predominately AFMS-involving lesions.

CONCLUSION

Likert and PI-RADS systems both demonstrate high cancer detection rates. Likert scoring had a higher AUC with moderately higher specificity and lower positive call rate and could potentially help to reduce the number of unnecessary biopsies performed.

ADVANCES IN KNOWLEDGE

This paper illustrates that the Likert scoring system has potential to help urologists reduce the number of prostate biopsies performed.

Optimizing size thresholds for detection of clinically significant prostate cancer on MRI: Peripheral zone cancers are smaller and more predictable than transition zone tumors

PURPOSE

To evaluate if size-based cut-offs based on MR imaging can successfully assess clinically significant prostate cancer (csPCA). The goal was to improve the currently applied size-based differentiation criterion in PI-RADS.

METHODS AND MATERIALS

MRIs of 293 patients who had undergone 3 T MR imaging with subsequent confirmation of prostate cancer on systematic and targeted MRI/TRUS-fusion biopsy were re-read by three radiologists. All identifiable tumors were measured on T2WI for lesions originating in the transition zone (TZ) and on DWI for lesions from the peripheral zone (PZ) and tabulated against their Gleason grade.

RESULTS

309 lesions were analyzed, 213 (68.9 %) in the PZ and 96 (31.1 %) in the TZ. ROC-Analysis showed a stronger correlation between lesion size and clinically significant (defined as Gleason Grade Group ≥ 2) prostate cancer (PCa) for the PZ (AUC = 0.73) compared to the TZ (AUC = 0.63). The calculated Youden index resulted in size cut-offs of 14 mm for PZ and 21 mm for TZ tumors.

CONCLUSION

Size cut-offs can be used to stratify prostate cancer with different optimal size thresholds in the peripheral zone and transition zone. There was a clearer separation of clinically significant tumors in peripheral zone cancers compared to transition zone cancers. Future iterations of PI-RADS could therefore take different size-based cut-offs for peripheral zone and transition zone cancers into account.

Quantitative Prostate MRI

Prostate MRI is reported in clinical practice using the Prostate Imaging and Data Reporting System (PI-RADS). PI-RADS aims to standardize, as much as possible, the acquisition, interpretation, reporting, and ultimately the performance of prostate MRI. PI-RADS relies upon mainly subjective analysis of MR imaging findings, with very few incorporated quantitative features. The shortcomings of PI-RADS are mainly: low-to-moderate interobserver agreement and modest accuracy for detection of clinically significant tumors in the transition zone. The use of a more quantitative analysis of prostate MR imaging findings is therefore of interest. Quantitative MR imaging features including: tumor size and volume, tumor length of capsular contact, tumor apparent diffusion coefficient (ADC) metrics, tumor T₁ and T₂ relaxation times, tumor shape, and texture analyses have all shown value for improving characterization of observations detected on prostate MRI and for differentiating between tumors by their pathological grade and stage. Quantitative analysis may therefore improve diagnostic accuracy for detection of cancer and could be a noninvasive means to predict patient prognosis and guide management. Since quantitative analysis of prostate MRI is less dependent on an individual users' assessment, it could also improve interobserver agreement. Semi- and fully automated analysis of quantitative (radiomic) MRI features using artificial neural networks represent the next step in quantitative prostate MRI and are now being actively studied. Validation, through high-quality multicenter studies assessing diagnostic accuracy for clinically significant prostate cancer detection, in the domain of quantitative prostate MRI is needed. This article reviews advances in quantitative prostate MRI, highlighting the strengths and limitations of existing and emerging techniques, as well as discussing opportunities and challenges for evaluation of prostate MRI in clinical practice when using quantitative assessment. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Effect of observation size and apparent diffusion coefficient (ADC) value in PI-RADS v2.1 assessment category 4 and 5 observations compared to adverse pathological outcomes

OBJECTIVE

To compare observation size and apparent diffusion coefficient (ADC) values in Prostate Imaging Reporting and Data System (PI-RADS) v2.1 category 4 and 5 observations to adverse pathological features.

MATERIALS AND METHODS

With institutional review board approval, 267 consecutive men with 3-T MRI before radical prostatectomy (RP) between 2012 and 2018 were evaluated by two blinded radiologists who assigned PI-RADS v2.1 scores. Discrepancies were resolved by consensus. A third blinded radiologist measured observation size and ADC (ADC.mean, ADC.min [lowest ADC within an observation], ADC.ratio [ADC.mean/ADC.peripheral zone {PZ}]). Size and ADC were compared to pathological stage and Gleason score (GS) using t tests, ANOVA, Pearson correlation, and receiver operating characteristic (ROC) analysis.

RESULTS

Consensus review identified 267 true positive category 4 and 5 observations representing 83.1% (222/267) PZ and 16.9% (45/267) transition zone (TZ) tumors. Inter-observer agreement for PI-RADS v2.1 scoring was moderate (K = 0.45). Size was associated with extra-prostatic extension (EPE) (19 ± 8 versus 14 ± 6 mm, p < 0.001) and seminal vesicle invasion (SVI) (24 ± 9 versus 16 ± 7 mm, p < 0.001). Size ≥ 15 mm optimized the accuracy for EPE with area under the ROC curve (AUC) and sensitivity/specificity of 0.68 (CI 0.62-0.75) and 63.2%/65.6%. Size ≥ 19 mm optimized the accuracy for SVI with AUC/sensitivity/specificity of 0.75 (CI 0.66-0.83)/69.4%/70.6%. ADC metrics were not associated with pathological stage. Larger observation size (p = 0.032), lower ADC.min (p = 0.010), and lower ADC.ratio (p = 0.010) were associated with higher GS. Size correlated better to higher Gleason scores (p = 0.002) compared to ADC metrics (p = 0.09-0.11).

CONCLUSION

Among PI-RADS v2.1 category 4 and 5 observations, size was associated with higher pathological stage whereas ADC metrics were not. Size, ADC.minimum, and ADC.ratio differed in tumors stratified by Gleason score.

KEY POINTS

• Among PI-RADS category 4 and 5 observations, size but not ADC can differentiate between tumors by pathological stage. • An observation size threshold of 15 mm and 19 mm optimized the accuracy for diagnosis of extra-prostatic extension and seminal vesicle invasion. • Among PI-RADS category 4 and 5 observations, size, ADC.minimum, and ADC.ratio differed comparing tumors by Gleason score.

Similarities and differences between Likert and PIRADS v2.1 scores of prostate multiparametric MRI: a pictorial review of histology-validated cases

The UK National Institute for Health and Care Excellence (NICE) 2019 "Prostate cancer: diagnosis and management" guidelines have recommended that all patients suspected of prostate cancer undergo multiparametric magnetic resonance imaging (mpMRI) prior to biopsy. The Likert scoring system is advocated for mpMRI reporting based on multicentre studies that have demonstrated its effectiveness within the National Health Service (NHS). In recent years, there has been considerable drive towards standardised prostate reporting, which led to the development of "Prostate Imaging-Reporting And Data System" (PI-RADS). The PI-RADS system has been adopted by the majority of European countries and within the US. This paper reviews these systems indicating the similarities and specific differences that exist between PI-RADS and Likert assessment through a series of histologically proven clinical cases.