Lesion volume predicts prostate cancer risk and aggressiveness: validation of its value alone and matched with prostate imaging reporting and data system score

Multimodal AI Combining Clinical and Imaging Inputs Improves Prostate Cancer Detection

OBJECTIVES

Deep learning (DL) studies for the detection of clinically significant prostate cancer (csPCa) on magnetic resonance imaging (MRI) often overlook potentially relevant clinical parameters such as prostate-specific antigen, prostate volume, and age. This study explored the integration of clinical parameters and MRI-based DL to enhance diagnostic accuracy for csPCa on MRI.

MATERIALS AND METHODS

We retrospectively analyzed 932 biparametric prostate MRI examinations performed for suspected csPCa (ISUP ≥2) at 2 institutions. Each MRI scan was automatically analyzed by a previously developed DL model to detect and segment csPCa lesions. Three sets of features were extracted: DL lesion suspicion levels, clinical parameters (prostate-specific antigen, prostate volume, age), and MRI-based lesion volumes for all DL-detected lesions. Six multimodal artificial intelligence (AI) classifiers were trained for each combination of feature sets, employing both early (feature-level) and late (decision-level) information fusion methods. The diagnostic performance of each model was tested internally on 20% of center 1 data and externally on center 2 data (n = 529). Receiver operating characteristic comparisons determined the optimal feature combination and information fusion method and assessed the benefit of multimodal versus unimodal analysis. The optimal model performance was compared with a radiologist using PI-RADS.

RESULTS

Internally, the multimodal AI integrating DL suspicion levels with clinical features via early fusion achieved the highest performance. Externally, it surpassed baselines using clinical parameters (0.77 vs 0.67 area under the curve [AUC], P < 0.001) and DL suspicion levels alone (AUC: 0.77 vs 0.70, P = 0.006). Early fusion outperformed late fusion in external data (0.77 vs 0.73 AUC, P = 0.005). No significant performance gaps were observed between multimodal AI and radiologist assessments (internal: 0.87 vs 0.88 AUC; external: 0.77 vs 0.75 AUC, both P > 0.05).

CONCLUSIONS

Multimodal AI (combining DL suspicion levels and clinical parameters) outperforms clinical and MRI-only AI for csPCa detection. Early information fusion enhanced AI robustness in our multicenter setting. Incorporating lesion volumes did not enhance diagnostic efficacy.

Clinical characteristics and pathological features of undetectable clinically significant prostate cancer on multiparametric magnetic resonance imaging: A single-center and retrospective study

Objectives

The objectives of this study were to clarify the pathological features of clinically significant prostate cancer (csPC) that is undetectable on multiparametric magnetic resonance imaging (mpMRI).

Material and Methods

This single-center and retrospective study enrolled 33 men with prostate cancer (PC), encompassing 109 PC lesions, who underwent mpMRI before radical prostatectomy. Two radiologists independently assessed the mpMR images of all lesions and compared them with the pathological findings of PC. All PC lesions were marked on resected specimens using prostate imaging reporting and data system version 2.1 and classified into magnetic resonance imaging (MRI)-detectable and MRI-undetectable PC lesions. Each lesion was classified into csPC and clinically insignificant PC. Pathological characteristics were compared between MRI-detectable and MRI-undetectable csPC. Statistical analysis was performed to identify factors associated with MRI detectability. A logistic regression model was used to determine the factors associated with MRI-detectable and MRI-undetectable csPC.

Results

Among 109 PC lesions, MRI-detectable and MRI-undetectable PCs accounted for 31% (34/109) and 69% (75/109) of lesions, respectively. All MRI-detectable PCs were csPC. MRI-undetectable PCs included 30 cases of csPC (40%). The detectability of csPC on mpMRI was 53% (34/64). The MRI-undetectable csPC group had a shorter major diameter (10.6 ± 6.6 mm vs. 19.0 ± 6.9 mm, P < 0.001), shorter minor diameter (5.7 ± 2.9 mm vs. 10.7 ± 3.4 mm, P < 0.001), and lower percentage of lesions with Gleason pattern 5 (17% vs. 71%, P < 0.001). Shorter minor diameter (odds ratio [OR], 2.62; P = 0.04) and lower percentage of Gleason pattern 5 (OR, 24; P = 0.01) were independent predictors of MRI-undetectable csPC.

Conclusion

The pathological features of MRI-undetectable csPC included shorter minor diameter and lower percentage of Gleason pattern 5. csPC with shorter minor diameter may not be detected on mpMRI. Some MRI-undetectable csPC lesions exhibited sufficient size and Gleason pattern 5, emphasizing the need for further understanding of pathological factors contributing to MRI detectability.

Does a large prostate size, small lesion volume, or long needle distance from the probe to the lesion reduce magnetic resonance imaging-targeted cancer detection?

Background

We aimed to evaluate whether large prostate size, small lesion volume, or long lesion distance from the ultrasound probe tip would decrease cancer detection in transrectal magnetic resonance imaging (MRI)-targeted biopsies.

Materials and methods

Patients who underwent MRI-targeted biopsy at our institution between May 2017 and August 2019 were enrolled in a prospective database. Three to 5 cores were obtained from ≥2 prostate imaging reporting and data system v2 lesions. A multivariable model was created that included needle distance to the lesion, prostate specific antigen, prostate imaging reporting and data system, lesion volume, and prostate volume.

Results

A total of 377 patients with 533 lesions underwent a biopsy during the study period. A total of 233 (44%) lesions were positive for prostate cancer, and 173 (32%) lesions had clinically significant prostate cancer. The mean needle distance to the lesion was 11.7 mm (interquartile range, 7.6-15.5 mm). The likelihood of obtaining a positive core on biopsy decreased as the distance from the ultrasound probe increased for all prostate cancers and clinically significant prostate cancer (p = 0.018 and p = 0.004, respectively). Every 10 mm from the rectum, there was an 8%-10% decrease in the rate of cancer detection. Similarly, as the prostate volume increased, the odds of obtaining a positive core also decreased (p = 0.039). There was no significant association between the lesion size and amount of cancer obtained on biopsy.

Conclusions

Our data showed that transrectal MRI-targeted biopsy cancer detection modestly decreased the lesion from the ultrasound probe and with a large prostate volume but could not prove that lesion volume was a significant predictor of the amount of cancer detected.

High-grade prostate cancer demonstrates preferential growth in the cranio-caudal axis and provides discrimination of disease grade in an MRI parametric model

OBJECTIVES

To determine if multiparametric MRI prostate cancer (PC) lesion dimensions in different axes could distinguish between PC, grade group (GG) >2, and GG >3 on targeted transperineal biopsy and create and validate a predictive model on a separate cohort.

METHODS

The maximum transverse, anterio-posterior, and cranio-caudal lesion dimensions were assessed against the presence of any cancer, GG >2, and GG >3 on biopsy by binary logistic regression. The optimum multivariate models were evaluated on a separate cohort.

RESULTS

One hundred and ninety-three lesions from 148 patients were evaluated. Increased lesion volume, Prostate Specific Antigen (PSA), Prostate Imaging Reporting and Data System score, and decreased Apparent Diffusion Coefficient (ADC) were associated with increased GG (P < .001). The ratio of cranio-caudal to anterior-posterior lesion dimension increased from 1.20 (95% CI, 1.14-1.25) for GG ≤ 3 to 1.43 (95% CI, 1.28-1.57) for GG > 3 (P = .0022). The cranio-caudal dimension of the lesion was the strongest predictor of GG >3 (P = .000, area under the receiver operator characteristic curve [AUC] = 0.81). The best multivariate models had an AUC of 0.84 for cancer, 0.88 for GG > 2, and 0.89 for GG > 3. These models were evaluated on a separate cohort of 40 patients with 61 lesions. They demonstrated an AUC, sensitivity, and specificity of 0.82, 82.3%, and 55.5%, respectively, for the detection of cancer. For GG > 2, the models achieved an AUC of 0.84, sensitivity of 91.7%, and specificity of 69.4%. Additionally, for GG > 3, the models showed an AUC of 0.92, sensitivity of 88.9%, and specificity of 98.1%.

CONCLUSIONS

Cranio-caudal lesion dimension when used in conjunction with other parameters can create a model superior to the Prostate Imaging Reporting and Data Systems score in predicting cancer.

ADVANCES IN KNOWLEDGE

Higher-grade PC has a propensity to grow in the cranio-caudal direction, and this could be factored into MRI-based predictive models of prostate biopsy grade.

The Added Value of MRI-Based Targeted Biopsy in Biopsy-Naïve Patients: A Propensity-Score Matched Comparison

BACKGROUND

Multiparametric Magnetic Resonance Imaging (mpMRI)-based targeted biopsy has shown to be beneficial in detecting Clinically Significant Prostate Cancer (csPCa) and avoiding diagnosis of Non-csPCa (ncsPCa); however, its role in the treatment of biopsy-naïve patients is still under discussion.

METHODS

After identifying predictors for the diagnosis of csPCa via Multivariate Logistic Regression Analysis (MLRA), a propensity-score (1:1 nearest neighbor) matched comparison was performed between a Systematic-Only Biopsy (SOB) cohort and a mpMRI-based Combined (systematic + targeted) Biopsy (CB) cohort from two tertiary urologic centers (SOB: Department of Urology, University General Hospital of Heraklion, University of Crete, School of Medicine, Heraklion, Crete, Greece; CB: LKH Hall in Tirol, Austria). Only biopsy-naïve patients were included in the study. The study period for the included patients was from February 2018 to July 2023 for the SOB group and from July 2017 to June 2023 for the CB group. The primary outcome was the diagnosis of csPCa (≥ISUP 2); secondary outcomes were overall cancer detection, the added value of targeted biopsy in csPCa detection, and the reduction in ncsPCa diagnosis with CB compared to SOB. To estimate the Average Treatment effect of the Treated groups (ATT), cluster-robust standard errors were used to perform g-computation in the matched sample. p-values < 0.05 with a two-sided 95% confidence interval were considered statistically significant.

RESULTS

Matching achieved well-balanced groups (each n = 140 for CB and SOB). In the CB group, 65/140 (46.4%) patients were diagnosed with csPCa compared to 44/140 (31.4%) in the SOB group (RR 1.48, 95%-CI: 1.09-2.0, p = 0.01). In the CB group, 4.3% (6/140) and 1.4% (2/140) of csPCa cases were detected with targeted-only and systematic-only biopsy cores, respectively. In the CB group, 22/140 (15.7%) patients were diagnosed with ncsPCa compared to 33/140 (23.6%) in the SOB group (RR = 0.67, 95% CI: 0.41-1.08, p = 0.1). When comparing SOB to CB (ATT), the marginal OR was 0.56 (95% CI: 0.38-0.82, p = 0.003) for the diagnosis of csPCa and 0.75 (95% CI: 0.47-1.05, p = 0.085) for the diagnosis of overall cancer (≥ISUP 1).

CONCLUSION

The CB approach was superior to the SOB approach in detecting csPCa, while no additional detection of ncsPCa was seen. Our results support the application of mpMRI for biopsy-naïve patients with suspicions of prostate cancer.

Clinical significance of prostate cancer identified by transperineal standard template biopsy in men with nonsuspicious multiparametric magnetic resonance imaging

OBJECTIVE

Multiparametric magnetic resonance imaging (mpMRI) of the prostate has excellent sensitivity in detecting clinically significant prostate cancer (csCaP). However, whether a negative mpMRI in patients with a clinical suspicion of CaP can omit a confirmatory biopsy remains less understood and without consensus. Transperineal (TP) standard template biopsy (SBx) provides an effective approach to CaP detection. Our aim is to provide a comprehensive understanding of the CaP characteristics detected through TP SBx that are systematically overlooked by mpMRI.

METHODS

We conducted a retrospective analysis of all men who underwent prebiopsy mpMRI and subsequent a 20-core TP SBx at our hospital from September 2019 to February 2021. Patients with suspicious mpMRI received a combined TP SBx and targeted biopsy (TBx) (suspicious group), while those without suspicious (negative) mpMRI and who proceeded to biopsy, received TP SBx only (nonsuspicious group). A negative mpMRI was defined as the absence of suspicious findings and/or the presence of low-risk areas with a PI-RADS score of ≤2. Subsequently, we compared and evaluated the clinical and biopsy characteristics between these 2 groups.

RESULTS

We identified 301 men in suspicious group and 215 men in nonsuspicious group. The overall CaP detection rate and csCaP detection rate by TP SBx were 74.1%, 38.9% for suspicious group and 43.3%, 14.9% for nonsuspicious group, respectively. csCaP NPV of mpMRI was 85.1% with a csCaP prevalence 28.9%. The greatest percentage of cancer involvement (GPC) in biopsy core from nonsuspicious group was significantly lower than those of suspicious group (40% vs. 50%, p = 0.005), In multivariate logistic analysis, only PSAD > 0.15 ng/ml/cc was identified as an independent and significant predictor of csCaP in nonsuspicious group.

CONCLUSION

Within our cohort, false-negative rates of mpMRI for csCaP are substantial, reaching 15%. Nonsuspicious cases may contain a large volume tumor since the high GPC of SBx. For cases with nonsuspicious imaging and higher PSAD, a confirmatory biopsy may be necessary due to the increased risk of missed csCaP by mpMRI.

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.

Clinical and Radiological Factors for Predicting Clinically Significant Prostate Cancer in Biopsy-Naive Patients With PI-RADS 3 Lesions

OBJECTIVE

This study intends to examine the anticipatory power of clinical and radiological parameters in detecting clinically significant prostate cancer in patients demonstrating Prostate Imaging Reporting and Data System 3 lesions.

METHODS

This was a retrospective study. The study included participation from 453 patients at the First Affiliated Hospital of Soochow University, sampled between September 2017 through August 2022. Each patient underwent a routine 12-core prostate biopsy followed by a 2 to 5 core fusion-targeted biopsy. We utilized both univariate and multivariate logistic regression analyses to identify the parameters that have a correlation with clinically significant prostate cancer. The predictive ability of these parameters was assessed using the receiver operating characteristic curve, leading to the creation of a nomogram.

RESULTS

Clinically significant prostate cancer was detected in 68 out of 453 patients with Prostate Imaging Reporting and Data System 3 lesions (15.01%). Among Prostate Imaging Reporting and Data System 3a and 3b patients, 4.78% (3.09% of the total) and 33.75% (11.92% of the total), respectively, had clinically significant prostate cancer. Systematic biopsy improved prostate cancer and clinically significant prostate cancer detection rates by 7.72% and 3.09%, respectively, compared to targeted biopsy. Without systematic biopsy, there would be an undetected rate of 15% for prostate cancer and 8.13% for clinically significant prostate cancer in Prostate Imaging Reporting and Data System 3b patients. Several clinical parameters, including age, prostate-specific antigen density, lesion volume, apparent diffusion coefficient, and digital rectal examination, were statistically significant in the logistic regression analysis for clinically significant prostate cancer. The individual diagnostic accuracies of these parameters for clinically significant prostate cancer were 0.648, 0.645, 0.75, 0.763, and 0.7, respectively, but their combined accuracy improved to 0.866. A well-fit nomogram based on the identified risk factors was constructed (χ2 = 10.254, P = .248).

CONCLUSION

The combination of age, prostate-specific antigen density, lesion volume, apparent diffusion coefficient, and digital rectal examination presented a higher diagnostic value for clinically significant prostate cancer than any single parameter in patients with Prostate Imaging Reporting and Data System 3 lesions. Systematic biopsy proved crucial for biopsy-naive patients with Prostate Imaging Reporting and Data System 3 lesions and should not be omitted.

Results of fusion prostate biopsy comparing with cognitive and systematic biopsy

PURPOSE

Our study aims to determine whether there are differences in the degree of detection of prostate cancer (PCa) and CsPCa between fusion prostate biopsy (FPB), cognitive biopsy (PCB), and randomized, systematic biopsy (SB).

METHODS

A retrospective analysis was carried out of 195 patients with suspected PCa at the San Cecilio University Clinical Hospital in Granada who underwent a prostate biopsy between January and December 2021. Patients were divided into three groups: group 1, patients undergoing FPB transperineally with ultrasound BK 3000 (N = 87); group 2, PCB (N = 59) transperineally; and group 3, transrectal SB (N = 49), the latter two, with an ultrasound BK Specto.

RESULTS

We found differences in favor of image-directed biopsies (FPB and PCB) with a percentage of positive biopsies of 52.8% and 50%, respectively, compared to 41.4% with SB, but without these differences being significant. Given the controversy in performing prostate biopsies in PI-RADS 3 lesions reported in the literature, a subanalysis was performed excluding the FPB performed for PI-RADS 3 lesions (PI-RADS 4 and 5 are included), finding significant differences when comparing FPB with PCB and SB (group 1, 64% vs group 2, 45.8%; p = 0.05) (group 1, 64% vs group 3, 42.9%; p = 0.035).

CONCLUSION

With the results obtained in our series, we conclude that the finding of a PI-RADS 3 lesion in mpMRI should not be an absolute criterion to indicate prostate biopsy. On the other hand, for PI-RADS 4 and 5 lesions, FPB is recommended, which in this case turns out to be superior to PCB and SB.

The role of tumor density in predicting significant cancer on targeted biopsy of the prostate

OBJECTIVE

Multiparametric magnetic resonance imaging (mpMRI) is central to diagnosing prostate cancer; however, not all imaged lesions represent clinically significant tumors. We aimed to evaluate the association between the relative tumor volume on mpMRI and clinically significant prostate cancer on biopsy.

MATERIALS AND METHODS

We retrospectively reviewed the medical records of 340 patients who underwent combined transperineal targeted and systematic prostate biopsies between 2017 and 2021. Tumor volume was estimated based on the mpMRI diameter of suspected lesions. Relative tumor volume (tumor density) was calculated by dividing the tumor and prostate volumes. The study outcome was clinically significant cancer on biopsy. Logistic regression analyses were used to evaluate the association between tumor density and the outcome. The cutoff for tumor density was determined with ROC curves.

RESULTS

Median estimated prostate and peripheral zone tumor volumes were 55cm3 and 0.61cm3, respectively. Median PSA density was 0.13 and peripheral zone tumor density was 0.01. Overall, 231 patients (68%) had any cancer and 130 (38%) had clinically significant cancer. On multivariable logistic regression age, PSA, previous biopsy, maximal PI-RADS score, prostate volume, and peripheral zone tumor density were significant predictors of outcome. Using a threshold of 0.006, the sensitivity, specificity, positive and negative predictive values of peripheral zone tumor density were 0.9, 0.51, 0.57, and 0.88, respectively.

CONCLUSION

Peripheral zone tumor density is associated with clinically significant prostate cancer in patients with PI-RADS 4 and 5 mpMRI lesions. Future studies are required to validate our findings and evaluate the role of tumor density in avoiding unnecessary biopsies.

Transperineal US-MRI Fusion-Guided Biopsy for the Detection of Clinical Significant Prostate Cancer: A Systematic Review and Meta-Analysis Comparing Cognitive and Software-Assisted Technique

INTRODUCTION

We aimed to find potential differences in clinically significant prostate cancer (csPCa) detection rates between transperineal software-assisted fusion biopsy (saFB) and cognitive fusion biopsies (cFB).

METHODS

A systematic review of the literature was performed to identify comparative studies using PubMed, EMBASE, and Scopus according to the PICOS criteria. Cancer detection and complication rates were pooled using the Cochran-Mantel-Haenszel method with the random effect model and reported as odds ratios (ORs), 95% confidence intervals (CI), and p-values. A meta-analysis was performed using Review Manager (RevMan) 5.4 software by Cochrane Collaboration. The quality assessment of the included studies was performed using the Cochrane Risk of Bias tool, using RoB 2 for randomized studies and ROBINS-I for retrospective and nonrandomized ones.

RESULTS

Eight studies were included for the meta-analysis, including 1149 cases in software-based and 963 cases in cognitive fusion biopsy. The detection rates of csPCa were similar between the two groups (OR 1.01, 95% CI 0.74-1.37, p = 0.95). Study heterogeneity was low (I2 55%).

CONCLUSION

There is no actual evidence of the superiority of saFB over cFB in terms of the csPCa detection rate. Operator experience and software availability can drive the choice of one fusion technique over the other.

Quantification of prostate tumour diameter and volume from MR images using 3D ellipsoid model and its impact on PI-RADS v2.1 assessment

Maximum diameter and volume of the tumour provide important clinical information and are decision-making parameters for patients suspected with prostate cancer (PCa). The objectives of this study were to develop an automated method for 3D tumour measurement and compare it with the radiologist's manual assessment, as well as to investigate the impact of 3D tumour measurement on Prostate Imaging-Reporting and Data System version-2.1 (PI-RADS v2.1) scoring of prostate cancer. Tumour maximum diameter and volume were calculated using automated ellipsoid-fit method. For all PI-RADS scores, mean ± standard deviation range of tumour maximum diameter and volume measured using ellipsoid-fit method were 1.36 ± 0.28 to 1.97 ± 0.67 cm and 0.49 ± 0.31 to 1.05 ± 0.78 cc and manual assessment were in range of 0.73 ± 0.12 to 1.14 ± 0.25 cm and 0.36 ± 0.21 to 0.93 ± 0.39 cc, respectively. Ellipsoid-fit method showed significantly (p < 0.05) higher values for maximum diameter and volume than manual assessment. 3D measurement of tumour using ellipsoid-fit method was found to have higher maximum diameter and volume values (in 40-61% patients) compared to conventional assessment by radiologist, which may have an impact on PI-RADS v2.1 scoring system.

Machine learning-based analysis of a semi-automated PI-RADS v2.1 scoring for prostate cancer

Background

Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS v2.1) was developed to standardize the interpretation of multiparametric MRI (mpMRI) for prostate cancer (PCa) detection. However, a significant inter-reader variability among radiologists has been found in the PI-RADS assessment. The purpose of this study was to evaluate the diagnostic performance of an in-house developed semi-automated model for PI-RADS v2.1 scoring using machine learning methods.

Methods

The study cohort included an MRI dataset of 59 patients (PI-RADS v2.1 score 2 = 18, score 3 = 10, score 4 = 16, and score 5 = 15). The proposed semi-automated model involved prostate gland and zonal segmentation, 3D co-registration, lesion region of interest marking, and lesion measurement. PI-RADS v2.1 scores were assessed based on lesion measurements and compared with the radiologist PI-RADS assessment. Machine learning methods were used to evaluate the diagnostic accuracy of the proposed model by classification of PI-RADS v2.1 scores.

Results

The semi-automated PI-RADS assessment based on the proposed model correctly classified 50 out of 59 patients and showed a significant correlation (r = 0.94, p < 0.05) with the radiologist assessment. The proposed model achieved an accuracy of 88.00% ± 0.98% and an area under the receiver-operating characteristic curve (AUC) of 0.94 for score 2 vs. score 3 vs. score 4 vs. score 5 classification and accuracy of 93.20 ± 2.10% and AUC of 0.99 for low score vs. high score classification using fivefold cross-validation.

Conclusion

The proposed semi-automated PI-RADS v2.1 assessment system could minimize the inter-reader variability among radiologists and improve the objectivity of scoring.

The Diagnostic Value of PI-RADS v2.1 in Patients with a History of Transurethral Resection of the Prostate (TURP)

To explore the diagnostic value of the Prostate Imaging−Reporting and Data System version 2.1 (PI-RADS v2.1) for clinically significant prostate cancer (CSPCa) in patients with a history of transurethral resection of the prostate (TURP), we conducted a retrospective study of 102 patients who underwent systematic prostate biopsies with TURP history. ROC analyses and logistic regression analyses were performed to demonstrate the diagnostic value of PI-RADS v2.1 and other clinical characteristics, including PSA and free/total PSA (F/T PSA). Of 102 patients, 43 were diagnosed with CSPCa. In ROC analysis, PSA, F/T PSA, and PI-RADS v2.1 demonstrated significant diagnostic value in detecting CSPCa in our cohort (AUC 0.710 (95%CI 0.608−0.812), AUC 0.768 (95%CI 0.676−0.860), AUC 0.777 (95%CI 0.688−0.867), respectively). Further, PI-RADS v2.1 scores of the peripheral and transitional zones were analyzed separately. In ROC analysis, PI-RADS v2.1 remained valuable in identifying peripheral-zone CSPCa (AUC 0.780 (95%CI 0.665−0.854; p < 0.001)) while having limited capability in distinguishing transitional zone lesions (AUC 0.533 (95%CI 0.410−0.557; p = 0.594)). PSA and F/T PSA retain significant diagnostic value for CSPCa in patients with TURP history. PI-RADS v2.1 is reliable for detecting peripheral-zone CSPCa but has limited diagnostic value when assessing transitional zone lesions.

Prostate Cancer Detection with mpMRI According to PI-RADS v2 Compared with Systematic MRI/TRUS-Fusion Biopsy: A Prospective Study

Background: mpMRI assesses prostate lesions through their PI-RADS score. The primary goal of this prospective study was to demonstrate the correlation of PI-RADS v2 score and the volume of a lesion with the presence and clinical significance of prostate cancer (PCa). The secondary goal was to determine the extent of additionally PCa in inconspicuous areas. Methods: All 157 patients underwent a perineal MRI/TRUS-fusion prostate biopsy. Targeted biopsies as well as a systematic biopsy were performed. The presence of PCa in the probes was specified by the ISUP grading system. Results: In total, 258 lesions were biopsied. Of the PI-RADS 3 lesions, 24% were neoplastic. This was also true for 36.9% of the PI-RADS 4 lesions and for 59.5% of the PI-RADS 5 lesions. Correlation between ISUP grades and lesion volume was significant (p < 0.01). In the non-suspicious mpMRI areas carcinoma was revealed in 19.7% of the patients. Conclusions: The study shows that the PI-RADS v2 score and the lesion volume correlate with the presence and clinical significance of PCa. However, there are two major points to consider: First, there is a high number of false positive findings. Second, inconspicuous mpMRI areas revealed PCa.

Development and external validation of a novel nomogram to predict prostate cancer in biopsy-naïve patients with PSA <10 ng/ml and PI-RADS v2.1 = 3 lesions

OBJECTIVE

To develop and externally validate a novel nomogram in biopsy-naïve patients with prostate-specific antigen (PSA) <10 ng/ml and PI-RADS v2.1 = 3 lesions.

METHODS

We retrospectively collected 307 men that underwent initial biopsy from October 2015 to January 2022 in Cohort 1 (The First Affiliated Hospital of Soochow University). External cohort (Cohort 2, Kunshan Hospital) included 109 men that met our criteria from July 2016 to June 2021. By Slicer-3D Software, the volume of all lesions was divided into two subgroups (PI-RADS v2.1 = 3a and 3b). Logistic regression analysis was performed to screen for variables and construct nomogram by analyzing clinical data from Cohort 1. Receiver operating characteristics curve analysis, calibration plot and decision curve analysis (DCA) were plotted to validate the nomogram in external cohort.

RESULTS

A total of 70 (22.8%) patients was diagnosed with prostate cancer in Institution 1. Among them, 34 (11.1%) had clinically significant prostate cancer (csPCa). Age, prostate-specific antigen density, digital rectal examination, PI-RADS v2.1 = 3 subgroups (3a and 3b) and apparent diffusion coefficient (ADC, <750 mm² /s) were predictive factors for prostate cancer (PCa) and csPCa. High area under the curve of the nomogram was found in Cohort 1 and Cohort 2 for PCa (0.857 vs. 0.850) and for csPCa (0.896 vs. 0.893). Calibration curves showed excellent agreement between the predicted probability and actual risk for the models in internal and external validation. The DCA demonstrated net benefit of our nomogram.

CONCLUSION

Until now, this is the first nomogram that predicts PCa and csPCa in biopsy-naïve patients with PSA <10 ng/ml and PI-RADS v2.1 = 3 lesions. Furthermore, PI-RADS v2.1 = 3 subgroups were considered to be an independent risk factor in our model. Our nomogram may assist urologists in biopsy decision making for these so-called "double gray zone" patients.

Can high b-value 3.0 T biparametric MRI with the Simplified Prostate Image Reporting and Data System (S-PI-RADS) be used in biopsy-naïve men?

OBJECTIVE

To analyze the clinical value of high b-value 3.0 T biparametric magnetic resonance with the Simplified Prostate Image Reporting and Data System (S-PI-RADS) in biopsy-naïve men.

METHODS

A retrospective analysis of the data of 224 patients who underwent prostate biopsy (cognitive fusion targeted biopsy combined with systematic biopsy) after a high b-value 3.0 T magnetic resonance examination at Haikou Hospital from July 2018 to July 2020 was performed. Two radiologists performed multi-parameter magnetic resonance imaging (mp-MRI) with the prostate imaging report and data system version 2 (PI-RADS v2) and biparametric magnetic resonance imaging (bp-MRI) with the simplified prostate image reporting and data system (S-PI-RADS). The detection efficacy of the two regimens was evaluated by classifying prostate cancer (PCa) and clinically significant prostate cancer (csPCa) according to pathology, and the statistical significance of the differences between the two regimens was determined by Z-test.

RESULTS

The area under the receiver operating curve (AUC) values of mp-MRI based on PI-RADS v2 and bp-MRI based on S-PI-RADS to detect PCa were 0.905 and 0.892, respectively, while the AUC values for the detection of csPCa were 0.919 and 0.906, respectively. There was no statistically significant difference between the two tests (Z values were 0.909 and 1.145, p > 0.05).

CONCLUSION

There was no significant difference in the detection efficacy of high b-value bp-MRI based on the S-PI-RADS score for prostate cancer and clinically significant prostate cancer compared with the standard PI-RADS v2 score with mp-MRI protocols, which can be applied clinically.

Utility of PSA Density in Predicting Upgraded Gleason Score in Men on Active Surveillance With Negative MRI

OBJECTIVES

To determine whether PSA density (PSAD), can sub-stratify risk of biopsy upgrade among men on active surveillance (AS) with normal baseline MRI.

METHODS

We identified a cohort of patients with low and favorable intermediate-risk prostate cancer on AS at two large academic centers from February 2013 - December 2017. Analysis was restricted to patients with GG1 cancer on initial biopsy and a negative baseline or surveillance mpMRI, defined by the absence of PI-RADS 2 or greater lesions. We assessed ability of PSA, prostate volume and PSAD to predict upgrading on confirmatory biopsy.

RESULTS

We identified 98 patients on AS with negative baseline or surveillance mpMRI. Median PSA at diagnosis was 5.8 ng/mL and median PSAD was 0.08 ng/mL/mL. Fourteen men (14.3%) experienced Gleason upgrade at confirmatory biopsy. Patients who were upgraded had higher PSA (7.9 vs 5.4 ng/mL, P = .04), PSAD (0.20 vs 0.07 ng/mL/mL, P < .001), and lower prostate volumes (42.5 vs 65.8 mL, P = .01). On multivariate analysis, PSAD was associated with pathologic upgrade (OR 2.23 per 0.1-increase, P = .007). A PSAD cutoff at 0.08 generated a NPV of 98% for detection of pathologic upgrade.

CONCLUSION

PSAD reliably discriminated the risk of Gleason upgrade at confirmatory biopsy among men with low-grade prostate cancer with negative MRI. PSAD could be clinically implemented to reduce the intensity of surveillance for a subset of patients.

The impact of age on pathological insignificant prostate cancer rates in contemporary robot-assisted prostatectomy patients despite active surveillance eligibility

BACKGROUND

To assess insignificant prostate cancer(iPCa) rates after robot-assisted radical prostatectomy (RARP) in contemporary patients who were preoperatively eligible for active surveillance(AS). IPCa indicates no risk of PCa progression.

METHODS

We retrospectively analysed 2,837 RARP patients (2010-2019) who fulfilled at least one AS entry criteria set - Prostate Cancer Research International - Active Surveillance(PRIAS), University of California San Francisco(UCSF), National Comprehensive Cancer Network(NCCN) or University of Toronto. We utilized four different iPCa definitions, (1) based on pT2 and Gleason score ≤6 and also cumulative tumor-volume (2) ≤2.5mL, (3) ≤0.7mL or (4) ≤0.5mL. For each AS set we tested the rates of iPCa and compared between age <70 vs. ≥70 yrs. This was complemented by multivariable logistic regression(LRM) predicting iPCa, adjusted for age and clinical AS variables. Finally, within the subgroup, who had iPCa, we tested rate of those, who were deemed preoperatively AS ineligible.

RESULTS

Between most(PRIAS) and least stringent(TORONTO) AS sets, iPCa(1) was correctly predicted in 70-57%. Similarly, for iPCa definitions 2-4, rates were (2)59-42%, (3)34-19% and (4)27-14%. Senior patients harbored decreased proportions of iPCa. LRM confirmed that advanced age is associated with a lower chance of iPCa. More stringent AS sets lead to higher rates of AS ineligibility, e.g. 53% for PRIAS, despite iPCa.

CONCLUSIONS

AS sets show limited accuracy for stricter iPCa definitions, which further declined with advanced age. Greater AS stringency resulted in more AS ineligible patients despite harboring iPCa. In consequence, patients are at risk for overtreatment. Clinicians must consider age and different AS sets that result in highly variable detection rates of iPCa.

Diagnostic Accuracy and Interobserver Agreement of PI-RADS Version 2 and Version 2.1 for the Detection of Transition Zone Prostate Cancers

BACKGROUND. PI-RADS version 2.1 (v2.1) introduced a number of key changes to the assessment of transition zone (TZ) lesions. OBJECTIVE. The purpose of this study was to evaluate interobserver agreement and diagnostic accuracy for detecting TZ prostate cancer (PCa) and clinically significant PCa (csPCa) by use of PI-RADS v2 and PI-RADS v2.1 among radiologists with different levels of experience. METHODS. This retrospective study included 355 biopsy-naïve patients who from January 2017 to March 2020 underwent prostate MRI that showed a TZ lesion and underwent subsequent biopsy. PCa was diagnosed in 93 patients (International Society of Urological Pathology [ISUP] grade group 1, n = 34; ISUP grade group ≥ 2, n = 59) and non-cancerous lesions in 262 patients. Five radiologists with varying experience in prostate MRI scored lesions using PI-RADS v2 and PI-RADS v2.1 in sessions separated by at least 4 weeks. Interobserver agreement was evaluated with kappa and Kendall W statistics. ROC curve analysis was used to evaluate performance in detection of TZ PCa and csPCa. RESULTS. Interobserver agreement among all readers was higher for PI-RADS v2.1 than for PI-RADS v2 (mean weighted κ = 0.700 vs 0.622; Kendall W = 0.805 vs 0.728; p = .03). The pooled AUC values for detecting TZ PCa and csPCa were higher among all readers using PI-RADS v2.1 (0.866 vs 0.827 for TZ PCa; 0.929 vs 0.899 for TZ csPCa; p < .001). For detecting TZ PCa, the pooled sensitivity, specificity, and accuracy were 86.9%, 79.4%, and 75.4% among all readers for PI-RADS v2.1 compared with 79.4%, 71.8%, and 73.8% for PI-RADS v2. For detecting TZ csPCa, the pooled sensitivity, specificity, and accuracy were 84.8%, 90.9%, and 89.9% among all readers for PI-RADS v2.1 compared with 81.4%, 89.9%, and 88.5% for PI-RADS v2. Reader 1, who had the least experience, had the lowest sensitivity, specificity, and accuracy (78.0%, 89.2%, and 87.3%). Reader 5, who had the most experience, had the highest sensitivity, specificity, and accuracy (88.1%, 92.9%, and 92.1%) in detecting csPCa. CONCLUSION. PI-RADS v2.1 had better interobserver agreement and diagnostic accuracy than PI-RADS v2 for evaluating TZ lesions. Reader experience continues to affect the performance of prostate MRI interpretation with PI-RADS v2.1. CLINICAL IMPACT. PI-RADS v2.1 is more accurate and reproducible than PI-RADS v2 for the diagnosis of TZ PCa.

Urinary proteomic profiles of prostate cancer with different risk of progression and correlation with histopathological features

Prostate cancer (PCa) is the most common tumor in men with extremely variable outcome, varying from latent or indolent form to very aggressive behavior. High grade tumors, expansions exceeding the prostatic capsule into the surrounding soft tissues and spreading through lymph vascular channels, represent the most consistent unfavorable prognostic factors. However, accuracy in the prediction of the disease progression is sometimes difficult. Along with new molecular diagnostic techniques and more accurate histopathological approaches, proteomic studies challenge to identify potential biomarkers predictive of PCa progression. In our study we analyzed the urinary proteomes of 42 patients affected by PCa through two-dimensional electrophoresis associated with mass spectrometry. Proteomic profiles were correlated to histopathological features including pTNM stage and tumor differentiation in order to provide new promising markers able to define more accurately the PCa aggressiveness and driving new therapeutic approaches.

Equivocal PI-RADS Three Lesions on Prostate Magnetic Resonance Imaging: Risk Stratification Strategies to Avoid MRI-Targeted Biopsies

We aimed to investigate the relation between largest lesion diameter, prostate-specific antigen density (PSA-D), age, and the detection of clinically significant prostate cancer (csPCa) using first-time targeted biopsy (TBx) in men with Prostate Imaging-Reporting and Data System (PI-RADS) 3 index lesions. A total of 292 men (2013-2019) from two referral centers were included. A multivariable logistic regression analysis was performed. The discrimination and clinical utility of the built model was assessed by the area under the receiver operation curve (AUC) and decision curve analysis, respectively. A higher PSA-D and higher age were significantly related to a higher risk of detecting csPCa, while the largest index lesion diameter was not. The discrimination of the model was 0.80 (95% CI 0.73-0.87). When compared to a biopsy-all strategy, decision curve analysis showed a higher net benefit at threshold probabilities of ≥2%. Accepting a missing ≤5% of csPCa diagnoses, a risk-based approach would result in 34% of TBx sessions and 23% of low-risk PCa diagnoses being avoided. In men with PI-RADS 3 index lesions scheduled for first-time TBx, the balance between the number of TBx sessions, the detection of low-risk PCa, and the detection of csPCa does not warrant a biopsy-all strategy. To minimize the risk of missing the diagnosis of csPCa but acknowledging the need of avoiding unnecessary TBx sessions and overdiagnosis, a risk-based approach is advisable.

Round table: arguments in supporting abbreviated or biparametric MRI of the prostate protocol

Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 update, in the attempt to improve clinical guidelines for multiparametric magnetic resonance imaging (mpMRI) of the prostate, has clear limitations. The role of dynamic contrast-enhanced sequences is not defined, precise guidance on the clinical management (biopsy or clinical surveillance) for score 3 lesions [equivocal for clinical significant prostate cancer (sPCa)] is not offered and criteria for lesions interpretation remain difficult and subjective. We report criteria and arguments in supporting the use of abbreviated or biparametric prostate MRI protocol in clinical practice for detection and management of PCa.

PI-RADS 3 lesions: Does the association of the lesion volume with the prostate-specific antigen density matter in the diagnosis of clinically significant prostate cancer?

INTRODUCTION

Currently, a new subclassification of the Pi-RADS 3 lesions and subgroups is being used: 3a (indolent or low-risk lesions with volume <0.5 ml) and 3b (significant or high-risk lesions with volume ≥0.5 ml). The prostate-specific antigen density (PSAd) has been identified as a diagnostic tool that helps to predict clinically significant prostate cancer (csCaP). The aim of this study is to evaluate the association of the volume of the Pi-RADS 3 lesions and the PSAd in the diagnosis of csCaP.

MATERIAL AND METHODS

We conducted a retrospective study that included prostate biopsies performed using a transperineal approach and guided by ultrasound between 2015 and 2020. csCaP was defined as Gleason score ≥3 + 4. The population was divided into groups according to the Pi-RADS 3 subclassification and the PSAd value. We calculated sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) of 3b lesions for the detection of high-grade prostate cancer, alone and combined with PSAD groups.

RESULTS

In total, 99 patients with Pi-RADS 3 lesions were included. Forty-three patients were in group 3a and 56, in 3b. Mean PSA was 7.28 ± 2.6 ng/ml. Pi-RADS 3a lesion did not present csCaP but 17.8% of Pi-RADS 3b lesion did. In group 3b with PSAd > 0.15, 62.5% presented csCaP. In those Pi-RADS 3b with PSAd ≤ 0.15, all biopsies were insignificant prostate cancer (isCaP) and 40 biopsies could have been avoided. Considering 3b as positive for csCaP detection, sensitivity was 100%, specificity 48.3%, NPV 17.8%, and PPV 100%. When adding PSAd to group 3b, sensitivity was 100%, specificity was 86.9%, NPV was 62.5%, PPV was 100%. In total, only the subgroup 3b with PSAd > 0.15 presented csCaP and 83.8% biopsies could be avoided.

CONCLUSIONS

In this series, the association of the volume of PIRADS 3 lesion and the PSAd improves specificity and PPV contributing to improve the management of csCaP.

Software-assisted US/MRI fusion-targeted biopsy for prostate cancer

BACKGROUND

Prostate cancer is the first cancer diagnosis in men. European Association of Urology  (EAU) Guidelines for Prostate Cancer underline the importance of screening, performed through PSA testing on all men with more than 50 years of age and before on men with risk factors. The diagnosis is still histopathologic, and it is done on the basis of the findings on biopsy samples.

MATERIALS AND METHODS

Fusion biopsy is a relatively new technique that allows the operator to perform the biopsies in office instead of the MRI gantry, without losing the detection capability of MRI. The  T2-wighted images obtained during a previous mpMRI are merged with the real-time ones of the TRUS.

RESULTS

Fusion biopsy in comparison with the systematic standard biopsy has a better detection rate of clinically significant cancers and of any cancers.

CONCLUSION

EAU 2020 guidelines still do offer a list of indications of when the biopsy should be performed, but it still appeared to be overperformed. The aim of our study is to underline how, in accordance with the recent literature result,  fusion biopsy has showed a better detection rate of any cancer and clinically significant disease with a reduced numbers of samplings, and no substantial difference between the multiple software.

Value of prostate-specific antigen density in negative or equivocal lesions on multiparametric magnetic resonance imaging

OBJECTIVE

The prostate-specific antigen density (PSAD) is an accessory tool when suspecting prostate cancer. Multiparametric magnetic resonance imaging (mpMRI) of the prostate has a high rate of false negatives. The aim of this study is to evaluate the sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) when adding the PSAD and negative or equivocal mpMRI.

MATERIAL AND METHODS

A retrospective study that included prostate biopsies performed using a transperineal approach and guided by ultrasound between 2015 and 2019 was conducted. Clinically significant prostate cancer (csPCa) was defined as Gleason score ≥3+4. The population was divided into groups according to the PSAD level-≤0.15 and >0.15. Sensitivity, specificity, NPV, and PPV of mpMRI were calculated.

RESULTS

A total of 292 patients were included; 12.1% (4/33 patients) of the negative mpMRI group presented csPCa, and only 7 in the equivocal mpMRI group presented csPCa. NPV and sensitivity were 91.15% and 90.5%, respectively. In the positive mpMRI group, 53.7% (96/179) had csPCa, with a PPV of 53.6% and specificity of 55.3%. Of the patients with PSAD ≤0.15, 23 (16.54%) presented csPCa. All of them presented a positive mpMRI. All patients with a negative or equivocal mpMRI and a PSAD ≤0.15 presented a clinically non-significant tumor or benign result. The addition of this tool to mpMRI resulted in 100% sensitivity, 69% specificity, and 34.8% PPV.

CONCLUSION

In our series, PSAD ≤0.15 increased the NPV in negative or equivocal mpMRI, and through this unnecessary prostate biopsies could be avoided.

Use of Imaging to Optimise Prostate Cancer Tumour Volume Assessment for Focal Therapy Planning

PURPOSE OF REVIEW

Rapid advances in imaging of the prostate have facilitated the development of focal therapy and provided a non-invasive method of estimating tumour volume. Focal therapy relies on an accurate estimate of tumour volume for patient selection and treatment planning so that the optimal energy dose can be delivered to the target area(s) of the prostate while minimising toxicity to surrounding structures. This review provides an overview of different imaging modalities which may be used to optimise tumour volume assessment and critically evaluates the published evidence for each modality.

RECENT FINDINGS

Multi-parametric MRI (mp-MRI) has become the standard tool for patient selection and guiding focal therapy treatment. The current evidence suggests that mp-MRI may underestimate tumour volume, although there is a large variability in results. There remain significant methodological challenges associated with pathological processing and accurate co-registration of histopathological data with mp-MRI. Advances in different ultrasound modalities are showing promise but there has been limited research into tumour volume estimation. The role of PSMA PET/CT is still evolving and further investigation is needed to establish if this is a viable technique for prostate tumour volumetric assessment. mp-MRI provides the necessary tumour volume information required for selecting patients and guiding focal therapy treatment. The potential for underestimation of tumour volume should be taken into account and an additional margin applied to ensure adequate treatment coverage. At present, there are no other viable image-based alternatives although advances in new technologies may refine volume estimations in the future.

Clinical factors for predicting malignancy in patients with PSA < 10 ng/mL and PI-RADS 3 lesions

AIM

To determine clinical risk factors in patients with PI-RADS 3 lesions and prostate-specific antigen (PSA) < 10 ng/mL.

METHODS

In this prospective study, all patients underwent multiparametric magnetic resonance imaging. Following the 2-5 core fusion-targeted biopsy, standard 12-core prostate biopsy was performed in each patient (combined biopsy). The cutoff values were calculated with receiver-operating characteristic analysis. First, univariate logistic regression analysis was used to evaluate the relationship between total eight parameters and prostate cancer. Subsequently, multiple logistic regression analysis was performed to the parameters associated with prostate cancer.

RESULTS

Two hundred and eighty-eight patients were included in the study. Some clinical parameters are determined to be significant in univariate and multiple logistic regression analyses, including PSA, free/total PSA ratio, PSA density (PSA/total prostate volume), positive family history of PCa, and PI-RADS 3 lesion diameter. Patients were classified between 0 and 5 according to the number of risk factors. While the risk of cancer was 7.1% in patients with one or less risk factors, the PCA rate was 45.2% among patients with all risk factors.

CONCLUSION

In patients with PI-RADS 3 lesion and PSA < 10 ng/mL, histopathological results of biopsy can be estimated with higher accuracy using some clinical parameters.

Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters

Radiomics is an emerging field of image analysis with potential applications in patient risk stratification. This study developed and evaluated machine learning models using quantitative radiomic features extracted from multiparametric magnetic resonance imaging (mpMRI) to detect and classify prostate cancer (PCa). In total, 191 patients that underwent prostatic mpMRI and combined targeted and systematic fusion biopsy were retrospectively included. Segmentations of the whole prostate glands and index lesions were performed manually in apparent diffusion coefficient (ADC) maps and T2-weighted MRI. Radiomic features were extracted from regions corresponding to the whole prostate gland and index lesion. The best performing combination of feature setup and classifier was selected to compare its predictive ability of the radiologist’s evaluation (PI-RADS), mean ADC, prostate specific antigen density (PSAD) and digital rectal examination (DRE) using receiver operating characteristic (ROC) analysis. Models were evaluated using repeated 5-fold cross-validation and a separate independent test cohort. In the test cohort, an ensemble model combining a radiomics model, with models for PI-RADS, PSAD and DRE achieved high predictive AUCs for the differentiation of (i) malignant from benign prostatic lesions (AUC = 0.889) and of (ii) clinically significant (csPCa) from clinically insignificant PCa (cisPCa) (AUC = 0.844). Our combined model was numerically superior to PI-RADS for cancer detection (AUC = 0.779; p = 0.054) as well as for clinical significance prediction (AUC = 0.688; p = 0.209) and showed a significantly better performance compared to mADC for csPCa prediction (AUC = 0.571; p = 0.022). In our study, radiomics accurately characterizes prostatic index lesions and shows performance comparable to radiologists for PCa characterization. Quantitative image data represent a potential biomarker, which, when combined with PI-RADS, PSAD and DRE, predicts csPCa more accurately than mADC. Prognostic machine learning models could assist in csPCa detection and patient selection for MRI-guided biopsy.

How accurately can multiparametric magnetic resonance imaging measure the tumour volume of a prostate cancer? Results of a systematic review

The tumour volume of a cancer within the prostate gland is commonly measured with multiparametric MRI. The measurement has a role in many clinical scenarios including focal therapy, but the accuracy of it has never been systematically reviewed. We included articles if they compared tumour volume measurements obtained by mpMRI with a reference volume measurement obtained after radical prostatectomy. Correlation and concordance statistics were summarised. A simple accuracy score was derived by dividing the given mean or median mpMRI volume by the histopathological reference volume. Factors affecting the accuracy were noted. Scores for potential bias and quality were calculated for each article. A total of 18 articles describing 1438 patients were identified. Nine articles gave Pearson's correlation scores, with a median value of 0.75 but the range was wide (0.42-0.97). A total of 11 articles reported mean values for volume while 9 reported median values. For all 18 articles, the mean or median values for MRI volumes were lower than the corresponding reference values suggesting consistent underestimation. For articles reporting mean and median values for volume, the median accuracy scores were 0.83 and 0.80, respectively. The accuracy was higher for tumours of greater volume, higher grade and when an endorectal coil was used. Accuracy did not seem to improve over time, with a 3 Tesla magnet or by applying a shrinkage factor to the reference measurement. Most studies showed evidence of at least moderate bias, and their quality was highly variable, but neither of these appeared to affect accuracy.

What Type of Prostate Cancer Is Systematically Overlooked by Multiparametric Magnetic Resonance Imaging? An Analysis from the PROMIS Cohort

BACKGROUND

All risk stratification strategies in cancer overlook a spectrum of disease. The Prostate MR Imaging Study (PROMIS) provides a unique opportunity to explore cancers that are overlooked by multiparametric magnetic resonance imaging (mpMRI).

OBJECTIVE

To summarise attributes of cancers that are systematically overlooked by mpMRI.

DESIGN, SETTING, AND PARTICIPANTS

PROMIS tested performance of mpMRI and transrectal ultrasonography (TRUS)-guided biopsy, using 5 mm template mapping (TPM) biopsy as the reference standard.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Outcomes were overall and maximum Gleason scores, maximum cancer core length (MCCL), and prostate-specific antigen density (PSAD). Cancer attributes were compared between cancers that were overlooked and those that were detected.

RESULTS AND LIMITATIONS

Of men with cancer, 7% (17/230; 95% confidence interval [CI] 4.4-12%) had significant disease overlooked by mpMRI according to definition 1 (Gleason ≥ 4 + 3 of any length or MCCL ≥ 6 mm of any grade) and 13% (44/331; 95% CI 9.8-17%) according to definition 2 (Gleason ≥ 3 + 4 of any length or MCCL ≥ 4 mm). In comparison, TRUS-guided biopsy overlooked 52% (119/230; 95% CI 45-58%) of significant disease by definition 1 and 40% (132/331; 95% CI 35-45%) by definition 2. Prostate cancers undetected by mpMRI had significantly lower overall and maximum Gleason scores (p = 0.0007; p < 0.0001) and shorter MCCL (median difference: 3 mm [5 vs 8 mm], p < 0.0001; 95% CI 1-3) than cancers that were detected. No tumours with overall Gleason score > 3 + 4 (Gleason Grade Groups 3-5; 95% CI 0-6.4%) or maximum Gleason score > 4 + 3 (Gleason Grade Groups 4-5; 95% CI 0-8.0%) on TPM biopsy were undetected by mpMRI. Application of a PSAD threshold of 0.15 reduced the proportion of men with undetected cancer to 5% (12/230; 95% CI 2.7-8.9%) for definition 1 and 9% (30/331; 95% CI 6.2-13%) for definition 2. Application of a PSAD threshold of 0.10 reduced the proportion of men with undetected disease to 3% (6/230; 95% CI 1.0-5.6%) for definition 1 cancer and to 3% (11/331; 95% CI 1.7-5.9%) for definition 2 cancer. Limitations were post hoc analysis and uncertain significance of undetected lesions.

CONCLUSIONS

Overall, a small proportion of cancers are overlooked by mpMRI, with estimates ranging from 4.4% (lower boundary of 95% CI for definition 1) to 17% (upper boundary of 95% CI for definition 2). Prostate cancers undetected by mpMRI are of lower grade and shorter length than cancers that are detected.

PATIENT SUMMARY

Prostate cancers that are undetected by magnetic resonance imaging (MRI) are smaller and less aggressive than those that are detected, and none of the most aggressive cancers are overlooked by MRI.

Histological differences in cancer cells, stroma, and luminal spaces strongly correlate with in vivo MRI-detectability of prostate cancer

The current study aimed to investigate the plausible histopathological factors that affect the detectability of prostate cancers on multiparametric magnetic resonance imaging (MP-MRI). This retrospective study included 59 consecutive patients who had undergone MP-MRI and subsequent radical prostatectomy. The cases were standardized according to the tumor size ranging from 10 to 20 mm on the final pathological diagnosis. Histopathological review and semi-automated imaging analysis were performed to evaluate the relative area fractions of the histological components, including cancer cells, stroma, and luminal spaces. Among the 59 prostatectomy specimens, no case showed two or more foci of cancer that matched the size criteria. Of the 59 lesions, 35 were MRI-detectable [Prostate Imaging Reporting and Data System (PIRADS) score of 3 or greater] and 24 were MRI-undetectable (PIRADS score of 2 or less). No significant differences were observed in Gleason Grade Group, percentage of Gleason pattern 4, and predominant subtype of Gleason pattern 4 between MRI-detectable and MRI-undetectable cancers. On the other hand, significantly higher mean area fraction of cancer cells (60.9% vs. 42.7%, P < 0.0001) and lower mean area fractions of stroma (33.8% vs. 45.1%, P = 0.00089) and luminal spaces (5.2% vs. 12.2%, P < 0.0001) were observed in MRI-detectable cancers than in MRI-undetectable cancers. In a multivariable analysis performed upon exclusion of area fraction of stroma due to its multicollinearity with that of cancer cells, area fractions of cancer cells (P = 0.0031) and luminal space (P = 0.0035) demonstrated strong positive and negative correlation with MRI-detectability, respectively. Changes in cancer cells, stroma, and luminal spaces, rather than conventional histological parameters, could be considered one of the best predictors to clinical, in vivo MRI-detectability of prostate cancer.

Using the prostate imaging reporting and data system version 2 (PI-RIDS v2) to detect prostate cancer can prevent unnecessary biopsies and invasive treatment

Prostate cancer (PCa) is one of the most common cancers among men globally. The authors aimed to evaluate the ability of the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) to classify men with PCa, clinically significant PCa (CSPCa), or no PCa, especially among those with serum total prostate-specific antigen (tPSA) levels in the “gray zone” (4–10 ng ml⁻¹). A total of 308 patients (355 lesions) were enrolled in this study. Diagnostic efficiency was determined. Univariate and multivariate analyses, receiver operating characteristic curve analysis, and decision curve analysis were performed to determine and compare the predictors of PCa and CSPCa. The results suggested that PI-RADS v2, tPSA, and prostate-specific antigen density (PSAD) were independent predictors of PCa and CSPCa. A PI-RADS v2 score ≥4 provided high negative predictive values (91.39% for PCa and 95.69% for CSPCa). A model of PI-RADS combined with PSA and PSAD helped to define a high-risk group (PI-RADS score = 5 and PSAD ≥0.15 ng ml^-1 cm^-3, with tPSA in the gray zone, or PI-RADS score ≥4 with high tPSA level) with a detection rate of 96.1% for PCa and 93.0% for CSPCa while a low-risk group with a detection rate of 6.1% for PCa and 2.2% for CSPCa. It was concluded that the PI-RADS v2 could be used as a reliable and independent predictor of PCa and CSPCa. The combination of PI-RADS v2 score with PSA and PSAD could be helpful in the prediction and diagnosis of PCa and CSPCa and, thus, may help in preventing unnecessary invasive procedures.

Prostate MRI and transperineal TRUS/MRI fusion biopsy for prostate cancer detection: clinical practice updates

This narrative review summarizes the current knowledge about multiparametric and biparametric magnetic resonance imaging of the prostate. This is provided from both a radiological and a urological point of view analyzing the technical aspects of fusion-targeted biopsy using the transperineal approach. We report practical considerations concerning pure cognitive and software-assisted settings, discuss the principal transperineal fusion software now available, and debate the pros and cons of choosing one approach over the other.

Analysis of PI-RADS 4 cases: Management recommendations for negatively biopsied patients

PURPOSE

To evaluate if subgroups of patients assigned to MRI category PI-RADS 4 regarding clinical and MRI imaging aspects have distinct risks of prostate cancer (PCa) to facilitate adequate clinical management of this population, especially after negative targeted biopsy.

METHODS

This prospective, IRB approved single center cross-sectional study includes 931 consecutive patients after mp-MRI at 3 T for PCa detection. 193 patients with PI-RADS assessment category 4 received subsequent combined targeted MRI/US fusion-guided and systematic 12-core TRUS-guided biopsy as reference standard and were finally analyzed. The primary endpoint was PCa detection of PI-RADS 4 with MRI subgroup analyses. Secondary endpoints were analyses of clinical data, location of PCa, and detection of targeted biopsy cores.

RESULTS

PCa was detected in 119 of 193 patients (62%) including clinically significant PCa (csPCa; Gleason score ≥3 + 4 = 7) in 92 patients (48%). MRI subgroup analysis revealed 95% PCa (73% csPCa) in unambiguous PI-RADS 4 index lesions without additional, interfering signs of prostatitis in the peripheral zone or overlaying signs of severe stromal hyperplasia in the transition zone according to PI-RADS v2. Transition zone confined PI-RADS-4-lesions with overlaying signs of stromal hyperplasia showed PCa only in 11% (4% csPCa). Targeted biopsy cores missed the csPCa index lesion in 7% of the patients. PSA density (PSAD) was significantly higher in PCa patients.

CONCLUSIONS

Small csPCa can reliably be detected with mp-MRI by experienced readers, but can be missed by targeted MR/US fusion biopsy alone. Targeted re-biopsy of unambiguous (peripheral) PI-RADS-4-lesions is recommended; whereas transition zone confined PI-RADS-4-lesions with overlaying signs of stromal hyperplasia might be followed-up by re-MRI primarily.

Association of training level and outcome of software-based image fusion-guided targeted prostate biopsies

PURPOSE

The aim of this study was to assess the impact of experience on the outcome of image fusion-guided prostate biopsies performed by urologists working at a high-volume medical center.

METHODS

The first 210 consecutive fusion biopsies were analyzed following installation of the software-based biopsy platform Artemis™ (Eigen, USA). The impact of training was measured in terms of changes in prostate cancer detection rates and biopsy duration over time. We sought to identify a threshold of experience for urologists, which predicts higher detection rates of targeted biopsies. The influence of various factors on prostate cancer detection rates was evaluated using bi- and multivariate analysis.

RESULTS

Twenty-two urologists (n = 9 senior urologists, n = 13 urological residents) performed targeted biopsies followed by systematic 12-core biopsies. Overall, targeted biopsies yielded a positive result in 39.6% of 260 suspicious MRI lesions. A subgroup analysis of the six urologists who performed more than ten biopsies was then conducted, and their level of experience (i.e., performance of more than eight biopsies) was found to be associated with higher detection rates than those with less experience (49.0% and 23.0%, respectively; p < 0.001) in the targeted biopsies. Experience was likewise a significant and independent predictor of a cancer-positive targeted biopsy (p = 0.002). Experienced senior physicians did not outperform residents in their targeted biopsy results. Further, biopsy duration correlated negatively (r = - 0.5931, p < 0.001) with the total number of biopsies performed for all subgroups during the period of assessment.

CONCLUSIONS

Experience is an important predictor of the rate of detection in targeted biopsies using software-based biopsy platforms with semi-robotic assistance. Moreover, the performance of just a few procedures appears sufficient to increase biopsy effectiveness significantly. Lastly, supervision by experts is recommended during the training phase.

Changes in prostate apparent diffusion coefficient values during radiotherapy after neoadjuvant hormones

Background

Changes in prostate cancer apparent diffusion coefficient (ADC) derived from diffusion-weighted magnetic resonance imaging (MRI) provide a noninvasive method for assessing radiotherapy response. This may be attenuated by neoadjuvant hormone therapy (NA-HT). We investigate ADC values measured before, during and after external beam radiotherapy (EBRT) following NA-HT.

Methods

Patients with ⩾T2c biopsy-proven prostate cancer receiving 3 months of NA-HT plus definitive radiotherapy were prospectively identified. All underwent ADC-MRI scans in the week before EBRT, in the third week of EBRT and 8 weeks after its completion. Imaging was performed at 1.5 T. The tumour, peripheral zone (PZ) and central zone (CZ) of the prostate gland were identified and median ADC calculated for each region and time point.

Results

Between September and December 2014, 15 patients were enrolled (median age 68.3, range 57-78) with a median Gleason score of 7 (6-9) and prostate-specific antigen (PSA) at diagnosis 14 (3-197) ng/ml. Median period of NA-HT prior to first imaging was 96 days (69-115). All patients completed treatment. Median follow up was 25 months (7-34), with one patient relapsing in this time. Thirteen patients completed all imaging as intended, one withdrew after one scan and another missed the final imaging. PZ and CZ could not be identified in one patient. Median tumour ADC before, during and post radiotherapy was 1.24 × 10^-3 mm²/s (interquartile range 0.16 × 10^-3 mm²/s), 1.31 × 10^-3 mm²/s (0.22 × 10^-3 mm²/s), then 1.32 × 10^-3 mm²/s (0.13 × 10^-3 mm²/s) respectively (p > 0.05). There was no significant difference between median tumour and PZ or CZ ADC at any point. Gleason score did not correlate with ADC values.

Conclusions

Differences in ADC parameters of normal and malignant tissue during EBRT appear attenuated by prior NA-HT. The use of changes in ADC as a predictive tool in this group may have limited utility.

Positive pre-biopsy MRI: are systematic biopsies still useful in addition to targeted biopsies?

PURPOSE

The diagnostic strategy implementing multiparametric magnet resonance tomography (mpMRI) and targeted biopsies (TB) improves the detection and characterization of significant prostate cancer (PCa). We aimed to assess the clinical usefulness of systematic biopsies (SB) in the setting of patients having a pre-biopsy positive MRI.

METHODS

A review of the literature was performed in March 2018. All studies investigating the performance of SB in addition to TB (all techniques) were assessed, both in the biopsy-naïve and repeat biopsy setting.

RESULTS

Evidence demonstrates that TB improves the detection of index-significant PCa compared with SB alone, in both initial and repeat biopsy settings. However, the combination of both TB and SB improved the overall (around 30%) and significant (around 10%) PCa detection rates as compared with TB alone. Significant differences between both biopsy approaches exist regarding cancer location favoring SB for the far lateral sampling, and TB for the anterior zone. Main current pitfalls of pure TB strategy are the learning curve and experience required for mpMRI reading and biopsy targeting, as well as the precision assessment in TB techniques.

CONCLUSION

A pure TB strategy omitting SB leads to the risk of missing up to 15% of significant cancer, due to limitations of mpMRI performance/reading and of precision during lesion targeting. SB remain necessary, in addition to the TB, to obtain the most accurate assessment of the entire prostate gland in this sub-group of patients at risk of significant disease.

Prostate Imaging-Reporting and Data System Version 2: Beyond Prostate Cancer Detection

The main purpose of Prostate Imaging-Reporting and Data System Version 2 (PI-RADSv2) is to effectively detect clinically significant prostate cancers (csPCa) using multiparametric magnetic resonance imaging. Since the first introduction of PI-RADSv2, researchers have validated its diagnostic performance in identifying csPCa, and these promising data have influenced biopsy and treatment schemes. However, in this article, we focused on the potential of PI-RADSv2 in relation to various aspects of PCa such as Gleason score, tumor volume, extraprostatic extension, lymph node metastasis, and postoperative biochemical recurrence, beyond prostate cancer detection.

Biparametric MRI of the prostate

Biparametric Magnetic Resonance Imaging (bpMRI) of the prostate combining both morphologic T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) is emerging as an alternative to multiparametric MRI (mpMRI) to detect, to localize and to guide prostatic targeted biopsy in patients with suspicious prostate cancer (PCa). BpMRI overcomes some limitations of mpMRI such as the costs, the time required to perform the study, the use of gadolinium-based contrast agents and the lack of a guidance for management of score 3 lesions equivocal for significant PCa. In our experience the optimal and similar clinical results of the bpMRI in comparison to mpMRI are essentially related to the DWI that we consider the dominant sequence for detection suspicious PCa both in transition and in peripheral zone. In clinical practice, the adoption of bpMRI standardized scoring system, indicating the likelihood to diagnose a clinically significant PCa and establishing the management of each suspicious category (from 1 to 4), could represent the rationale to simplify and to improve the current interpretation of mpMRI based on Prostate Imaging and Reporting Archiving Data System version 2 (PI-RADS v2). In this review article we report and describe the current knowledge about bpMRI in the detection of suspicious PCa and a simplified PI-RADS based on bpMRI for management of each suspicious PCa categories to facilitate the communication between radiologists and urologists.