The role of multiparametric MRI in active surveillance for low-risk prostate cancer: The ROMAS randomized controlled trial

Immunohistochemistry analysis of PSMA expression at prostatic biopsy in high-risk prostate cancer: potential implications for PSMA-PET patient selection

Introduction

Prostate-specific membrane antigen (PSMA) is a transmembrane protein expressed by normal prostatic tissue. Therefore, molecular imaging targeting PSMA (PSMA-PET) has gained particular interest and diffusion for PCa staging and restaging. Several factors may affect PSMA-PET results, and many tools have been proposed to improve patient selection. Furthermore, PSMA expression is not homogeneous among different tissues and within the prostate itself. The aims of this study were to evaluate immunohistochemistry (IHC) features of prostate biopsy samples and to assess their correlation with whole-mount specimens and PSMA-PET parameters.

Methods

We included consecutive high-risk PCa patients who underwent PSMA-PET for staging proposal at our institution from January 2022 to December 2022. The PET parameters selected were SUVmax, total volume (TV), and total lesion activity (TL). Each patient underwent multiparametric MRI (mpMRI) and fusion-targeted prostate biopsy prior to surgery. IHC analyses were performed on the index lesion cores. IHC visual score (VS) (1, 2, 3) and visual pattern (VP) (membranous, cytoplasmic, and combined) and the percentage of PSMA-negative tumor areas (PSMA%neg) within biopsy cores were evaluated.

Results

Forty-three patients who underwent robotic radical prostatectomy after PSMA-PET were available for analyses. Concordance between VS and VP at biopsy and final pathology showed a Cohen's kappa coefficient of 0.39 and 0.38, respectively. Patients with PSMA%neg <20% had a higher concordance in VS and VP (Cohen's kappa 0.49 and 0.4, respectively). No difference emerged in terms of median PSMA-TV (p = 0.3) and PSMA-TL (p = 0.9) according to VS at biopsy, while median SUVmax was higher in patients with VS 3 (p = 0.04). Higher SUVmax was associated with membranous and combined VP expression (p = 0.008). No difference emerged between patients with PSMA%neg <20% or PSMA%neg >20% on biopsy cores in terms of SUVmax, PSMA-TL, and PSMA-TV (p = 0.5, p = 0.5, and p = 0.9 respectively).

Conclusions

We found a correlation between IHC VS and VP on targeted biopsy cores and SUVmax at PSMA-PET. However, the correlation between the IHC parameters of biopsy cores and final pathology was not as high as expected. Nevertheless, the presence of PSMA%neg <20% seems to have a better concordance in terms of visual score.

Annual mpMRI surveillance: PI-RADS upgrading and increasing trend correlated with patients who harbor clinically significant disease

INTRODUCTION

Prostate cancer screening has routinely identified men with very low- or low-risk disease, per the National Comprehensive Cancer Network guidelines. Current literature has demonstrated that the most appropriate management strategy for these patients is active surveillance (AS). The mainstay of AS includes periodic biopsies and biannual prostate-specific antigen tests. However, multiparametric magnetic resonance imaging (mpMRI) is uniquely posed to improve patient surveillance. This study aimed to evaluate the utility of an annual mpMRI in patients on AS, focusing on radiologic upgrading and Prostate Imaging-Reporting and Data System (PI-RADS) trends as indicators of clinically significant disease.

METHODS

This prospective, single intuition, study enrolled 208 patients on AS who had at least two biopsies and 1 mpMRI with a median follow-up of 5.03 years. The main outcome variable was time to Gleason grade (GG) reclassification.

RESULTS

After delineating patients on their initial PI-RADS score, men with score 3 and 5 lesions at first MRI had comparable GG reclassification-free survival to their counterparts. Conversely, men with initial PI-RADS 4 lesions showed a lower 5-year GG reclassification-free survival compared to those with PI-RADS score 1-2. The cohort was then subset to 70 patients who obtained ≥2 mpMRIs on protocol. Men experiencing uptrending mpMRI scores had an increased risk of GG reclassification, with a 35.4% difference in 5 year GG reclassification-free survival probability on the Kaplan-Meier curve analysis.

CONCLUSION

In conclusion, this study demonstrates that for men on AS with stable recapitulated disease, an annual MRI may replace repeat biopsies after confirmatory sampling has been obtained. On the other hand, men who initiate AS with PI-RADS 4 and/or who display uptrending mpMRI scores require periodic biopsies along with repeat imaging. This study highlights the utility of integrating an annual MRI into AS protocols, thus promising a more effective approach to management.

Cancer Detection Rate and Abnormal Interpretation Rate of Prostate MRI in Patients With Low-Grade Cancer

PURPOSE

The aim of this study was to evaluate the utility of cancer detection rate (CDR) and abnormal interpretation rate (AIR) in prostate MRI for patients with low-grade prostate cancer (PCa).

METHODS

This three-center retrospective study included patients who underwent prostate MRI from 2017 to 2021 with known low-grade PCa (Gleason score 6) without prior treatment. Patient-level highest Prostate Imaging Reporting & Data System (PI-RADS®) score and pathologic diagnosis within 1 year after MRI were used to evaluate the diagnostic performance of prostate MRI in detecting clinically significant PCa (csPCa; Gleason score ≥ 7). The metrics AIR, CDR, and CDR adjusted for pathologic confirmation rate were calculated. Radiologist-level AIR-CDR plots were shown. Simulation AIR-CDR lines were created to assess the effects of different diagnostic performances of prostate MRI and the prevalence of csPCa.

RESULTS

A total of 3,207 examinations were interpreted by 33 radiologists. Overall AIR, CDR, and CDR adjusted for pathologic confirmation rate at PI-RADS 3 to 5 (PI-RADS 4 and 5) were 51.7% (36.5%), 22.1% (18.8%), and 30.7% (24.6%), respectively. Radiologist-level AIR and CDR at PI-RADS 3 to 5 (PI-RADS 4 and 5) were in the 36.8% to 75.6% (21.9%-57.5%) range and the 16.3%-28.7% (10.9%-26.5%) range, respectively. In the simulation, changing parameters of diagnostic performance or csPCa prevalence shifted the AIR-CDR line.

CONCLUSIONS

The authors propose CDR and AIR as performance metrics in prostate MRI and report reference performance values in patients with known low-grade PCa. There was variability in radiologist-level AIR and CDR. Combined use of AIR and CDR could provide meaningful feedback for radiologists to improve their performance by showing relative performance to other radiologists.

Role of targeted biopsy, perilesional biopsy, random biopsy, and their combination in the detection of clinically significant prostate cancer by mpMRI/transrectal ultrasonography fusion biopsy in confirmatory biopsy during active surveillance program

BACKGROUND

Based on the findings of different trials in biopsy naïve patients, target biopsy (TB) plus random biopsy (RB) during mpMRI-guided transrectal ultrasound fusion biopsy (FB) are often also adopted for the biopsy performed during active surveillance (AS) programs. At the moment, a clear consensus on the extent and modalities of the procedure is lacking.

OBJECTIVE

To evaluate the increase in diagnostic accuracy achieved by perilesional biopsy (PL) and different RB schemes during FB performed in AS protocol.

DESIGN, SETTING, AND PARTICIPANTS

We collected prospectively the data of 112 consecutive patients with low- or very-low-risk prostate cancer; positive mpMRI underwent biopsy at a single academic institution in the context of an AS protocol.

INTERVENTION(S)

mpMRI/transrectal US FB with Hitachi RVS system with 3 TB and concurrent transrectal 24-core RB.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The diagnostic yield of the different possible biopsy schemes (TB only; TB + 4 perilesional (PL) cores; TB + 12-core RB; TB + 24-core RB) was compared by the McNemar test. Univariable and multivariable regression analyses were adopted to identify predictors of any cancer, Gleason grade group (GGG) ≥2 cancers, and the presence of GGG≥2 cancers in the larger schemes only.

RESULTS AND LIMITATIONS

The detection rate of GGG ≥2 cancers increased to 30%, 39%, and 49% by adding 4 PL cores, 14, and 24 RB cores, respectively, to TB cores (all p values <0.01). On the whole, TB alone, 14-core RB, and 24-core-RB identified 38%, 47%, and 56% of all the GGG ≥2 cancers. Such figures increased to 62% by adding to TB 4 PL cores, and to 80% by adding 14 RB cores. Most of the differences were observed in PI-RADS 4 lesions.

CONCLUSIONS

We found that PL biopsy increased the detection rate of GGG ≥2 cancers as compared with TB alone. However, the combination of those cores missed a large percentage of the CS cancers identified with larger RB cores, including a 20% of CS cancers diagnosed only by the combination of TB plus 24-core RB.

Comparison of prostate cancer detection rate at targeted biopsy of hub and spoke centers mpMRI: experience matters

BACKGROUND

Latest changes in European guidelines on prostate cancer determined a widespread of multiparametric magnetic resonance imaging (mpMRI) even in less experienced centers due to an increased demand. This could decrease diagnostic accuracy of targeted biopsy (TB) since image interpretation can be challenging and requires adequate and supervised training. Therefore we aimed to evaluate the prostate cancer (PCa) detection rate on TB according to mpMRI center's volume and experience.

METHODS

We retrospectively analyzed data of 737 patients who underwent mpMRI-TB at our institution. Patients were stratified according to mpMRI center: Hub (high volume >100 exams/year with dedicated radiologists and supervised training) and Spoke center (low volume <100 exams/year without dedicated radiologists and/or supervised training). Detection rate of PCa at TB and possible predictors of clinically significant PCa (csPCa) at TB. Differences in detection rate were explored using Chi-square test. Predictors of csPCa were evaluated through uni and multivariable logistic regression. The adjustment for casemix included: age, PSA, mpMRI center, lesion's location, PSA density, PI-RADS score and index lesion's size.

RESULTS

Four hundred forty-nine (60.9%) and 288 (39.1%) patients underwent mpMRI at a Hub or Spoke center, respectively. Hub group had higher detection rate for both any (60.3% vs. 48.1%) and csPCa (46.9% vs 38.7%; all P≤0.001). After stratifying for PI-RADS score, Hub group had higher detection rate for PI-RADS score 3 (csPCA 25.2% vs. 15.7%; p 0.04) and 4 (csPCa 65.7% vs. 45.7%; P=0.001). At multivariable analyses, receiving an mpMRI scan at a Spoke center was an independent predictor for csPCa on TB (OR 0.65; P=0.04).

CONCLUSIONS

mpMRI performed in Hub centers provided a significantly higher PCa yield on TB. A dedicated team of experienced radiologist, a supervised training for mpMRI and a central revision of mpMRI performed in non-experienced centres are essential to avoid unnecessary and potentially harmful procedures.

Site-specific concordance of targeted and systematic biopsy cores at the index lesion on multiparametric magnetic resonance: can we spare the double-tap?

PURPOSE

To define the impact of systematic biopsy (SB) cores directed in the same area of index lesion in patients undergoing targeted biopsy (TB) and SB for prostate cancer (PCa) suspicion.

METHODS

We retrospectively analyzed data of biopsy-naïve patients with one single suspicious lesion at mpMRI who underwent TB plus SB at our institution between January 2015 and September 2021. A convenient sample of 336 patients was available for our analyses. The primary outcome was to evaluate the impact of overlapping SB cores directed to the index lesion at mpMRI. The secondary outcome was to evaluate the SB cores concordance in terms of highest Gleason Score Detection with TB cores.

RESULTS

56% of patients were found to have site-specific concordance. SB cores determined disease upgrade in 22.1% patients. Thirty-one (16.4%) site-concordant patients experienced upgrade through overlapping SB cores, while 149 (79.3%) had no benefit by SB cores, and 8 (4.3%) patients had the worst ISUP at TB cores. 50% of the patients with negative-TB were upgraded to insignificant PCa, and 17.5% was upgraded from negative to unfavorable-intermediate- or high-risk PCa. Overall, 14 (19.4%) patients were also upgraded from ISUP 1 on TB to csPCa, with 28.5% of these harboring high-risk PCa. In csPCas at TB, 9 (12.5%) patients were upgraded from intermediate- to high-risk disease by SB.

CONCLUSIONS

TB alone consents to identify worst ISUP PCa in vast majority of patients scheduled for biopsy. A non-negligible number of patients are upgraded via-SB cores, including also index lesion overlapping cores. Omitting these cores might lead to a suboptimal patient management.

Adverse upgrading and/or upstaging in contemporary low-risk prostate cancer patients

Background

Upgrading and/or upstaging in low-risk prostate cancer (PCa) patients may represent an indication for active treatment instead of active surveillance (AS). We addressed contemporary upgrading and/or upstaging rates in a large population based-cohort of low-risk PCa patients.

Materials and methods

Whitin the SEER database (2010–2015), NCCN low-risk PCa patients were identified across management modalities: radical prostatectomy (RP), radiotherapy (RT) and non-local treatment (NLT). In RP patients, upgrading and/or upstaging rates were assessed in logistic regression models.

Results

Overall, of 27,901 low-risk PCa patients, 38% underwent RP vs 28% RT vs 34% NLT. RP patients were the youngest and harbored the highest percentage of positive cores and a higher rate of cT2a than NLT. At RP, 46.2% were upgraded to GGG ≥ 2, 6.0% to GGG ≥ 3 and 10.5% harbored nonorgan-confined stage (NOC, pT3-4 or pN1). Of NOC patients, 1.6% harbored GGG ≥ 3, 6.3% harbored GGG2 and 2.6% harbored GGG1. Of pT2 patients, 4.4% harbored GGG ≥ 3, 33.9% harbored GGG2 and 51.3% harbored GGG1. Age, PSA, percentage of positive cores and number of positive cores independently predicted the presence of NOC and/or GGG ≥ 3, but with low accuracy (63.9%).

Conclusions

In low-risk PCa, critical changes between tumor grade and stage at biopsy vs RP may be expected in very few patients: NOC with GGG ≥ 3 in 1.6% and NOC with GGG2 in 6.3%. Other patients with upgrading and/or upstaging combinations will invariably harbor either pT2 or GGG1 that far less critically affect PCa prognosis.

Role of multi-parametric magnetic resonance imaging fusion biopsy in active surveillance of prostate cancer: a systematic review

Background:

Our goal is to review current literature regarding the role of multi-parametric magnetic resonance imaging (mpMRI) in the active surveillance (AS) of prostate cancer (PCa) and identify trends in rate of reclassification of risk category, performance of fusion biopsy (FB) versus systematic biopsy (SB), and progression-free survival.

Methods:

We performed a comprehensive literature search in PubMed and identified 121 articles. A narrative summary was performed.

Results:

Thirty-two articles were chosen to be featured in this review. SB and FB are complementary in detecting higher-grade disease in follow-up. While FB was more likely than SB to detect clinically significant disease, FB missed 6.4–11% of clinically significant disease. Imaging factors that predicted upgrading include number of lesions on magnetic resonance imaging (MRI), lesion density, and MRI suspicion level.

Conclusion:

Incorporating mpMRI FB in conjunction with SB should be part of contemporary AS protocols. mpMRI should additionally be used routinely for follow-up; however, mpMRI is not currently sensitive enough in detecting disease progression to replace biopsy in the surveillance protocol.

The best prostate biopsy sampling system-fusion and systematic biopsy: A single center experience

BACKGROUND

Prostate cancer is the second most commonly diagnosed cancer in men. The diagnostic accuracy in prostate cancer can be increased by employing a preliminary multiparametric MRI followed by a fusion-targeted biopsy.

METHODS

To compare the diagnostic accuracy of fusion-targeted biopsy with the standard systematic biopsy in prostate cancer patients, we enrolled 139 patients on which we performed 139 prostate biopsies consisting of three targeted samples followed by 12 regular systematic samples. Based on histology, we analyzed the diagnostic performance of the two methods.

RESULTS

Both methods were equally good at detecting clinically significant cancer (83.3%, 50/60), while systematic biopsy detected more clinically insignificant cancers. However, the best diagnostic performance is obtained by combining the two methods.

CONCLUSION

The two methods are best seen as synergistic, and the addition of fusion biopsy can be used to detect more clinically significant prostate cancers than systematic biopsy alone.

Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data

Simple Summary

Prostate Cancer is one of the main threats to men’s health. Its accurate diagnosis is crucial to properly treat patients depending on the cancer’s level of aggressiveness. Tumor risk-stratification is still a challenging task due to the difficulties met during the reading of multi-parametric Magnetic Resonance Images. Artificial Intelligence models may help radiologists in staging the aggressiveness of the equivocal lesions, reducing inter-observer variability and evaluation time. However, these algorithms need many high-quality images to work efficiently, bringing up overfitting and lack of standardization and reproducibility as emerging issues to be addressed. This study attempts to illustrate the state of the art of current research of Artificial Intelligence methods to stratify prostate cancer for its clinical significance suggesting how widespread use of public databases could be a possible solution to these issues.

Abstract

Many efforts have been carried out for the standardization of multiparametric Magnetic Resonance (mp-MR) images evaluation to detect Prostate Cancer (PCa), and specifically to differentiate levels of aggressiveness, a crucial aspect for clinical decision-making. Prostate Imaging—Reporting and Data System (PI-RADS) has contributed noteworthily to this aim. Nevertheless, as pointed out by the European Association of Urology (EAU 2020), the PI-RADS still has limitations mainly due to the moderate inter-reader reproducibility of mp-MRI. In recent years, many aspects in the diagnosis of cancer have taken advantage of the use of Artificial Intelligence (AI) such as detection, segmentation of organs and/or lesions, and characterization. Here a focus on AI as a potentially important tool for the aim of standardization and reproducibility in the characterization of PCa by mp-MRI is reported. AI includes methods such as Machine Learning and Deep learning techniques that have shown to be successful in classifying mp-MR images, with similar performances obtained by radiologists. Nevertheless, they perform differently depending on the acquisition system and protocol used. Besides, these methods need a large number of samples that cover most of the variability of the lesion aspect and zone to avoid overfitting. The use of publicly available datasets could improve AI performance to achieve a higher level of generalizability, exploiting large numbers of cases and a big range of variability in the images. Here we explore the promise and the advantages, as well as emphasizing the pitfall and the warnings, outlined in some recent studies that attempted to classify clinically significant PCa and indolent lesions using AI methods. Specifically, we focus on the overfitting issue due to the scarcity of data and the lack of standardization and reproducibility in every step of the mp-MR image acquisition and the classifier implementation. In the end, we point out that a solution can be found in the use of publicly available datasets, whose usage has already been promoted by some important initiatives. Our future perspective is that AI models may become reliable tools for clinicians in PCa diagnosis, reducing inter-observer variability and evaluation time.