Use of the Prostate Imaging Reporting and Data System (PI-RADS) for Prostate Cancer Detection with Multiparametric Magnetic Resonance Imaging: A Diagnostic Meta-analysis

Refining clinically relevant cut-offs of prostate specific antigen density for risk stratification in patients with PI-RADS 3 lesions

BACKGROUND

Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions, identified through multiparametric magnetic resonance imaging (mpMRI), present a clinical challenge due to their equivocal nature in predicting clinically significant prostate cancer (csPCa). Aim of the study is to improve risk stratification of patients with PI-RADS 3 lesions and candidates for prostate biopsy.

METHODS

A cohort of 4841 consecutive patients who underwent MRI and subsequent MRI-targeted and systematic biopsies between January 2016 and April 2023 were retrospectively identified from independent prospectively maintained database. Only patients who have PI-RADS 3 lesions were included in the final analysis. A multivariable logistic regression analysis was performed to identify covariables associated with csPCa defined as International Society of Urological Pathology (ISUP) grade group ≥2. Performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and net benefit. Significant predictors were then selected for further exploration using a Chi-squared Automatic Interaction Detection (CHAID) analysis.

RESULTS

Overall, 790 patients had PI-RADS 3 lesions and 151 (19%) had csPCa. Significant associations were observed for age (OR: 1.1 [1.0-1.1]; p = 0.01) and PSA density (OR: 1643 [2717-41,997]; p < 0.01). The CHAID analysis identified PSAd as the sole significant factor influencing the decision tree. Cut-offs for PSAd were 0.13 ng/ml/cc (csPCa detection rate of 1% vs. 18%) for the two-nodes model and 0.09 ng/ml/cc and 0.16 ng/ml/cc for the three-nodes model (csPCa detection rate of 0.5% vs. 2% vs. 17%).

CONCLUSIONS

For individuals with PI-RADS 3 lesions on prostate mpMRI and a PSAd below 0.13, especially below 0.09, prostate biopsy can be omitted, in order to avoid unnecessary biopsy and overdiagnosis of non-csPCa.

Enhancing prostate cancer diagnosis and reducing unnecessary biopsies with [18F]DCFPyL PET/CT imaging in PI-RADS 3/4 patients

For patients presenting with prostate imaging reporting and data system (PI-RADS) 3/4 findings on magnetic resonance imaging (MRI) examinations, the standard recommendation typically involves undergoing a biopsy for pathological assessment to ascertain the nature of the lesion. This course of action, though essential for accurate diagnosis, invariably amplifies the psychological distress experienced by patients and introduces a host of potential complications associated with the biopsy procedure. However, [18F]DCFPyL PET/CT imaging emerges as a promising alternative, demonstrating considerable diagnostic efficacy in discerning benign prostate lesions from malignant ones. This study aims to explore the diagnostic value of [18F]DCFPyL PET/CT imaging for prostate cancer in patients with PI-RADS 3/4 lesions, assisting in clinical decision-making to avoid unnecessary biopsies. 30 patients diagnosed with PI-RADS 3/4 lesions through mpMRI underwent [18F]DCFPyL PET/CT imaging, with final biopsy pathology results as the "reference standard". Diagnostic performance was assessed through receiver operating characteristic (ROC) analysis, evaluating the diagnostic efficacy of molecular imaging PSMA (miPSMA) visual analysis and semi-quantitative analysis in [18F]DCFPyL PET/CT imaging. Lesions were assigned miPSMA scores according to the prostate cancer molecular imaging standardized evaluation criteria. Among the 30 patients, 13 were pathologically confirmed to have prostate cancer. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of visual analysis in [18F]DCFPyL PET/CT imaging for diagnosing PI-RADS 3/4 lesions were 61.5%, 88.2%, 80.0%, 75.0%, and 76.5%, respectively. Using SUVmax 4.17 as the optimal threshold, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for diagnosis were 92.3%, 88.2%, 85.7%, 93.8%, and 90.0%, respectively. The area under the ROC curve (AUC) for semi-quantitative analysis was 0.94, significantly higher than visual analysis at 0.80. [18F]DCFPyL PET/CT imaging accurately diagnosed benign lesions in 15 (50%) of the PI-RADS 3/4 patients. For patients with PI-RADS 4 lesions, the positive predictive value of [18F]DCFPyL PET/CT imaging reached 100%. [18F]DCFPyL PET/CT imaging provides potential preoperative prediction of lesion nature in mpMRI PI-RADS 3/4 patients, which may aid in treatment decision-making and reducing unnecessary biopsies.

Prostate cancer epidemiology and prognostic factors in the United States

Objective

Using the latest cohort study of prostate cancer patients, explore the epidemiological trend and prognostic factors, and develop a new nomogram to predict the specific survival rate of prostate cancer patients.

Methods

Patients with prostate cancer diagnosed from January 1, 1975 to December 31, 2019 in the Surveillance, Epidemiology, and End Results Program (SEER) database were extracted by SEER stat software for epidemiological trend analysis. General clinical information and follow-up data were also collected from 105 135 patients with pathologically diagnosed prostate cancer from January 1, 2010 to December 1, 2019. The factors affecting patient-specific survival were analyzed by Cox regression, and the factors with the greatest influence on specific survival were selected by stepwise regression method, and nomogram was constructed. The model was evaluated by calibration plots, ROC curves, Decision Curve Analysis and C-index.

Results

There was no significant change in the age-adjusted incidence of prostate cancer from 1975 to 2019, with an average annual percentage change (AAPC) of 0.45 (95% CI:-0.87~1.80). Among the tumor grade, the most significant increase in the incidence of G2 prostate cancer was observed, with an AAPC of 2.99 (95% CI:1.47~4.54); the most significant decrease in the incidence of G4 prostate cancer was observed, with an AAPC of -10.39 (95% CI:-13.86~-6.77). Among the different tumor stages, the most significant reduction in the incidence of localized prostate cancer was observed with an AAPC of -1.83 (95% CI:-2.76~-0.90). Among different races, the incidence of prostate cancer was significantly reduced in American Indian or Alaska Native and Asian or Pacific Islander, with an AAPC of -3.40 (95% CI:-3.97~-2.82) and -2.74 (95% CI:-4.14~-1.32), respectively. Among the different age groups, the incidence rate was significantly increased in 15-54 and 55-64 age groups with AAPC of 4.03 (95% CI:2.73~5.34) and 2.50 (95% CI:0.96~4.05), respectively, and significantly decreased in ≥85 age group with AAPC of -2.50 (95% CI:-3.43~-1.57). In addition, age, tumor stage, race, PSA and gleason score were found to be independent risk factors affecting prostate cancer patient-specific survival. Age, tumor stage, PSA and gleason score were most strongly associated with prostate cancer patient-specific survival by stepwise regression screening, and nomogram prediction model was constructed using these factors. The Concordance indexes are 0.845 (95% CI:0.818~0.872) and 0.835 (95% CI:0.798~0.872) for the training and validation sets, respectively, and the area under the ROC curves (AUC) at 3, 6, and 9 years was 0.7 or more for both the training and validation set samples. The calibration plots indicated a good agreement between the predicted and actual values of the model.

Conclusions

Although there was no significant change in the overall incidence of prostate cancer in this study, significant changes occurred in the incidence of prostate cancer with different characteristics. In addition, the nomogram prediction model of prostate cancer-specific survival rate constructed based on four factors has a high reference value, which helps physicians to correctly assess the patient-specific survival rate and provides a reference basis for patient diagnosis and prognosis evaluation.

Application of targeted diagnosis of PSMA in the modality shift of prostate cancer diagnosis: a review

Prostate cancer (PCa) is a serious threat to the health of men all over the world. The progression of PCa varies greatly among different individuals. In clinical practice, some patients often progress to advanced PCa. Therefore, accurate imaging for diagnosis and staging of PCa is particularly important for clinical management of patients. Conventional imaging examinations such as MRI and CT cannot accurately diagnose the pathological stages of advanced PCa, especially metastatic lymph node (LN) stages. As a result, developing an accurate molecular targeted diagnosis is crucial for advanced PCa. Prostate specific membrane antigen (PSMA) is of great value in the diagnosis of PCa because of its specific expression in PCa. At present, researchers have developed positron emission tomography (PET) targeting PSMA. A large number of studies have confirmed that it not only has a higher tumor detection rate, but also has a higher diagnostic efficacy in the pathological stage of advanced PCa compared with traditional imaging methods. This review summarizes recent studies on PSMA targeted PET in PCa diagnosis, analyzes its value in PCa diagnosis in detail, and provides new ideas for urological clinicians in PCa diagnosis and clinical management.

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.

Direct comparison between Grade Group assessed on systematic and MRI/ultrasound fusion targeted biopsies correlated to the radical prostatectomy specimens in patients with prostate cancer

OBJECTIVES

To compare the correlation of Gleason score (GS) and ISUP grade determined by prostate biopsies (PBx) and radical prostatectomy (RP) specimens according to the biopsy technique: ultrasound randomised (RBx) vs. MRI/ultrasound fusion targeted (TBx).

MATERIALS AND METHODS

Between March 2013 and June 2018, we retrospectively included patients who underwent RP for prostate cancer (PCa) histopathologically proven by RBx and/or TBx. All patients had a prebiopsy MRI by a single radiologist (using PI-RADS score), then transrectal RBx (12cores, blinded to MRI lesions) and TBx (2-4 cores/target) with elastic MRI/ultrasound fusion (UroStation™, Koelis, Grenoble, France). Histological findings were compared: PBx vs. RP.

RESULTS

One hundred and four patients underwent RP after RBx and/or TBx. ISUP concordance rate was better with the association RBx+TBx 49% (51/104) vs. 43.3% with TBx (P=0.07) and 43.3% with RBx (P=0.13). With RBx, 50% of the patients were downgraded (52/104) against 42.3% (44/104) with TBx (P=0.088). The association RBx+TBx significantly decreased the rate of downgrading of the ISUP score compared to the ISUP score of RP 35.6% (37/104) vs. RBx (50%, P=0.0001) and vs. TBx (42.3%, P=0.016).

CONCLUSION

In half of cases, the ISUP score was underestimated in RBx compared to RP specimens. Adding TBx to RBx significantly reduced downgrading. The combination of both biopsy techniques appeared to be the best protocol to get closer to ISUP score and GS of the RP specimens.

LEVEL OF EVIDENCE

C.

A critical analysis of deficiencies in the quality of information contained in prostate multiparametric MRI requests and reports

BACKGROUND

Multiparametric magnetic resonance imaging (mpMRI) has been increasingly recognised as an important tool in the diagnosis of prostate cancer. PI-RADSv2 guidelines recommend that important clinical information including prostate-specific antigen (PSA) levels, examination findings, and biopsy information should be included in mpMRI requests. PIRADS score and PSA density (PSAD) are both independent predictors for the presence of a clinically significant prostate cancer.

AIMS

This study aims to evaluate the quality of mpMRI requests and reports at our institution in accordance with these parameters.

METHODS

All prostate mpMRIs performed by radiology services in Galway University Hospital between 1st September 2019 and 1st March 2020 were reviewed. Exclusion criteria were applied. Requests and reports were analysed for the presence of the following parameters: PSA-results, examination findings, biopsy information, PI-RADS score, prostate volume, and PSAD.

RESULTS

A total of 586 mpMRIs were performed, and of these, 546 were included. PSA value was provided in 497 (91%) of requests, exam findings in 355 (65%), and biopsy information in 452 (82%). PIRADS score was included in 224 (41%) of reports, prostate volume in 178 (32.6%), and PSAD in 106 (19%).

CONCLUSIONS

Great variation in the quality of information contained in both requests and reports for prostate mpMRIs exists within our service. We aim to improve this by collaborating with our radiology colleagues to develop a proforma for requesting and reporting of mpMRIs for our radiology systems to ensure important clinical and radiological information is provided in future.

Computer-aided diagnosis in prostate cancer: a retrospective evaluation of the Watson Elementary® system for preoperative tumor characterization in patients treated with robot-assisted radical prostatectomy

PURPOSE

Computer-aided diagnosis (CAD) may improve prostate cancer (PCa) detection and support multiparametric magnetic resonance imaging (mpMRI) readers for better characterization. We evaluated Watson Elementary^® (WE^®) CAD system results referring to definitive pathological examination in patients treated with robot-assisted radical prostatectomy (RARP) in a tertiary referral center.

METHODS

Patients treated with RARP between 2020 and 2021 were selected. WE^® calculates the Malignancy Attention Index (MAI), starting from the information contained in the mpMRI images. Outcome measures were the capability to predict the presence of PCa, to correctly locate the dominant lesion, to delimit the largest diameter of the dominant lesion, and to predict the extraprostatic extension (EPE).

RESULTS

Overall, tumor presence was confirmed in 46 (92%) WE^® highly suspicious areas, while it was confirmed in 43 (86%) mpMRI PI-RADS ≥ 4 lesions. The WE^® showed a positive agreement with mpMRI of 92%. In 98% of cases, visible tumor at WE^® showed that the highly suspicious areas were within the same prostate sector of the dominant tumor nodule at pathology. WE^® showed a 2.5 mm median difference of diameter with pathology, compared with a 3.8 mm of mpMRI versus pathology (p = 0.019). In prediction of EPE, WE^® and mpMRI showed sensitivity, specificity, positive and negative predictive value of 0.81 vs 0.71, 0.56 vs 0.60, 0.88 vs 0.85 and 0.42 vs 0.40, respectively.

CONCLUSION

The WE^® system resulted accurate in the PCa dominant lesion detection, localization and delimitation providing additional information concerning EPE prediction.

Acute toxicity and health-related quality of life outcomes of localized prostate cancer patients treated with magnetic resonance imaging-guided high-dose-rate brachytherapy: A prospective phase II trial

PURPOSE

To report acute toxicity and health-related quality of life (HRQoL) outcomes of a phase II clinical trial of magnetic resonance imaging (MRI)-guided prostate high-dose-rate brachytherapy (HDR-BT) combined with external beam radiotherapy.

METHODS AND MATERIALS

Patients with intermediate- and high-risk prostate cancer (PCa) were eligible. Treatment consisted of a single 15 Gy MRI-guided HDR-BT followed by external beam radiotherapy (37.5-46 Gy depending on their risk category). Dosimetry, toxicity and HRQoL outcomes were collected prospectively at baseline, 1 and 3 months using Common Terminology Criteria for Adverse Events Version 4.0 and the expanded PCa index composite, respectively. General linear mixed modeling was conducted to assess the changes in expanded PCa index composite domain scores over time. A minimally important difference was defined as a deterioration of HRQoL scores at 3 months compared to baseline ≥ 0.5 standard deviation. A p value ≤ 0.05 was considered statistically significant.

RESULTS

Sixty-one patients were included. Acute grade (G)2 urinary toxicity was observed in 18 (30%) patients while 1 (2%) patient had G3 toxicity, and none had G4 toxicity. Two patients had an acute urinary retention. G2 gastrointestinal toxicity was reported by 5 (8%) patients with no G3-4. Compared to baseline, urinary HRQoL scores significantly declined at 1 month (p < 0.001) but recovered at 3 months (p > 0.05). Bowel (p < 0.001) and sexual (p < 0.001) domain scores showed a significant decline over the 3-month follow-up period. At 3 months, 44%, 49% and 57% of patients reported a minimally important difference respectively in the urinary bowel and sexual domains.

CONCLUSION

MRI-guided HDR-BT boost is a safe and well tolerated treatment of intermediate- and high-risk PCa in the acute setting. A longer follow-up and a comparison to ultrasound-based HDR-BT are needed to assess the potential benefit of MRI-guided prostate HDR-BT.

Correlation of multiparametric MRI with histopathological grade of peripheral zone prostate carcinoma

BACKGROUND

Prostatic cancer is the second most common malignant tumor in men. Preoperative grading of prostate cancer is important for its management. Our objective is to compare individual and combined detection rates of T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI), and magnetic resonance spectroscopy (MRS) for prostate cancer with histopathological diagnosis as its golden standard.

METHODS

Forty-four patients with positive digital rectal examination (DRE) findings and elevated prostate specific antigen (PSA), underwent multiparametric MRI (Mp-MRI). T2WI, DWI, DCE-MRI and MRS were done in all the patients. Cognitive magnetic resonance-transrectal ultrasound (MR-TRUS) fusion biopsy was done in all the patients. Sensitivity and specificity of T2WI, DWI, DCE-MRI, and Prostate Imaging - Reporting and Data System PIRADS version 2 was obtained. Apparent diffusion coefficient (ADC) value and choline/citrate ratio were obtained for each lesion and correlated with histopathological grade.

RESULTS

The mean age of the patients was 68.7 ± 10.1 years, and the mean serum PSA level was 58.1 ± 22.4 ng/dL. Of the 38 lesions in peripheral zone, 33 (87%) had histopathologically proven prostate cancer. T2WI had a sensitivity and specificity of 75.8% and 80% and DWI had a sensitivity and specificity of 90.9% and 80%, respectively, for detection of malignant prostatic lesion. The mean ADC values for prostate cancer, prostatitis, and normal prostatic parenchyma were 0.702 ± 0.094 × 10-3 mm2/sec, 0.959 ± 0.171 × 10-3 mm2/sec, and 1.31 ± 0.223 × 10-3 mm2/sec, respectively. Type 3 curve has lower sensitivity (45.5%) but high specificity (80%) for diagnosing prostate cancer.

CONCLUSION

DWI can be useful to differentiate benign from malignant prostatic lesions, and low-grade from high-grade prostate carcinoma. ADC value has a positive correlation with histopathological grade of prostate cancer.

Inter-reader agreement of the prostate imaging reporting and data system version v2.1 for detection of prostate cancer: A systematic review and meta-analysis

Objectives

We aimed to systematically assess the inter-reader agreement of the Prostate Imaging Reporting and Data System Version (PI-RADS) v2.1 for the detection of prostate cancer (PCa).

Methods

We included studies reporting inter-reader agreement of different radiologists that applied PI-RADS v2.1 for the detection of PCa. Quality assessment of the included studies was performed with the Guidelines for Reporting Reliability and Agreement Studies. The summary estimates of the inter-reader agreement were pooled with the random-effect model and categorized (from slight to almost perfect) according to the kappa (κ) value. Multiple subgroup analyses and meta-regression were performed to explore various clinical settings.

Results

A total of 12 studies comprising 2475 patients were included. The pooled inter-reader agreement for whole gland was κ=0.65 (95% CI 0.56-0.73), and for transitional zone (TZ) lesions was κ=0.62 (95% CI 0.51-0.72). There was substantial heterogeneity presented throughout the studies (I 2= 95.6%), and meta-regression analyses revealed that only readers' experience (<5 years vs. ≥5 years) was the significant factor associated with heterogeneity (P<0.01). In studies providing head-to-head comparison, there was no significant difference in inter-reader agreement between PI-RADS v2.1 and v2.0 for both the whole gland (0.64 vs. 0.57, p=0.37), and TZ (0.61 vs. 0.59, p=0.81).

Conclusions

PI-RADS v2.1 demonstrated substantial inter-reader agreement among radiologists for whole gland and TZ lesions. However, the difference in agreement between PI-RADS v2.0 and v2.1 was not significant for the whole gland or the TZ.

Prospective evaluation of the role of imaging techniques and TMPRSS2:ERG mutation for the diagnosis of clinically significant prostate cancer

Objectives

To test the hypothesis of a relationship between a specific genetic lesion (T2:ERG) and imaging scores, such as PI-RADS and PRI-MUS, and to test the effectiveness of these parameters for the diagnosis of prostate cancer (PCa) and clinically significant PCa (csPCa).

Materials and methods

This is a prospective study of men with suspected PCa enrolled between 2016 and 2019 at a high-volume tertiary hospital. Patients underwent systematic US-guided biopsy, plus targeted biopsy if they were presenting with >=1 suspicious lesion (PI-RADS>2) at mpMRI or PR-IMUS >2 at micro-ultrasound assessment. For each patient, one core from the highest PI-RADS or PRI-MUS lesion was collected for T2:ERG analysis. Multivariable logistic regression models (LRMs) were fitted for csPCa with a clinical model (age, total PSA, previous biopsy, family history for PCa), a clinical plus PI-RADS, clinical plus T2:ERG, clinical plus PI-RADS plus T2:ERG, and T2:ERG plus PI-RADS alone.

Results

The cohort consists of 158 patients: 83.5% and 66.2% had respectively a diagnosis of PCa and csPCa after biopsy. A T2:ERG fusion was found in 37 men and 97.3% of these patients harbored PCa, while 81.1% were diagnosed with csPCa. SE of T2:ERG assay for csPCa was 28.8%, SP 87.0%, NPV 38.8%, and PPV 81.1%. Of 105 patients who performed mpMRI 93.% had PIRADS ≥3. SE of mpMRI for csPCa was 98.5%, SP was 12.8%, NPV was 83.3%, and PPV was 65.7%. Among 67 patients who were subjected to micro-US, 90% had a PRI-MUS ≥3. SE of micro-US for csPCa was 89.1%, SP was 9.52%, NPV was 28.6%, and PPV was 68.3%. At univariable LRM T2:ERG was confirmed as independent of mpMRI and micro-US result (OR 1.49, p=0.133 and OR 1.82, p=0.592, respectively). At multivariable LRM the clinical model alone had an AUC for csPCa of 0.74 while the clinical model including PI-RADS and T2:ERG achieved an AUC of 0.83.

Conclusions

T2:ERG translocation and imaging results are independent of each other, but both are related csPCa. To evaluate the best diagnostic work-up for PCa and csPCa detection, all available tools (T2:ERG detection and imaging techniques) should be employed together as they appear to have a complementary role.

The 17-gene Genomic Prostate Score assay as a predictor of biochemical recurrence in men with intermediate and high-risk prostate cancer

The validated 17-gene Oncotype DX Genomic Prostate Score® (GPS™) assay risk-stratifies prostate-cancer patients with localized disease. The assay has primarily been utilized in lower risk patients deciding between active surveillance versus definitive therapy. In this retrospective cohort study, we analyze the association of the GPS result with time to biochemical recurrence post-prostatectomy in patients with National Comprehensive Cancer Network® (NCCN) intermediate and higher risk prostate cancer. The 141 patients included in the study were from the NorthShore University HealthSystem diagnosed 2014–2019 with NCCN intermediate (n = 109) or higher risk (n = 32) prostate cancer, treated with radical prostatectomy 2015–2019. The association of GPS result with time to biochemical recurrence was evaluated using univariable and multivariable Cox proportional hazards models in 120 patients with unfavorable intermediate or higher risk. Median (interquartile range) follow-up time was 28 (20 to 38) months. The GPS result was significantly associated with time to biochemical recurrence as both a continuous and dichotomous variable in univariable (hazard ratio [HR] per 20 GPS units 2.36, 95% CI 1.45–3.80, p < 0.001; HR for GPS result 41–100 vs 0–40 3.28, 95% CI 1.61–7.19, p < 0.001) and in multivariable models accounting for NCCN risk group (HR per 20 GPS units 2.14, 95% CI 1.31–3.46, p = 0.003; HR for GPS result 41–100 vs 0–40 3.00, 95% CI 1.43–6.72, p = 0.003) or biopsy Gleason Score and diagnostic PSA or PSA density. These results indicate that the GPS assay was a strong predictor of biochemical recurrence after radical prostatectomy in this unfavorable intermediate and higher risk prostate cancer patient population.

Improving liver tumor image contrast and synthesizing novel tissue contrasts by adaptive multiparametric MRI fusion

Purpose

Multiparametric MRI contains rich and complementary anatomical and functional information, which is often utilized separately. This study aims to propose an adaptive multiparametric MRI (mpMRI) fusion method and examine its capability in improving tumor contrast and synthesizing novel tissue contrasts among liver cancer patients.

Methods

An adaptive mpMRI fusion method was developed with five components: image pre-processing, fusion algorithm, database, adaptation rules, and fused MRI. Linear-weighted summation algorithm was used for fusion. Weight-driven and feature-driven adaptations were designed for different applications. A clinical-friendly graphic-user-interface (GUI) was developed in Matlab and used for mpMRI fusion. Twelve liver cancer patients and a digital human phantom were included in the study. Synthesis of novel image contrast and enhancement of image signal and contrast were examined in patient cases. Tumor contrast-to-noise ratio (CNR) and liver signal-to-noise ratio (SNR) were evaluated and compared before and after mpMRI fusion.

Results

The fusion platform was applicable in both XCAT phantom and patient cases. Novel image contrasts, including enhancement of soft-tissue boundary, vertebral body, tumor, and composition of multiple image features in a single image were achieved. Tumor CNR improved from -1.70 ± 2.57 to 4.88 ± 2.28 (p < 0.0001) for T1-w, from 3.39 ± 1.89 to 7.87 ± 3.47 (p < 0.01) for T2-w, and from 1.42 ± 1.66 to 7.69 ± 3.54 (p < 0.001) for T2/T1-w MRI. Liver SNR improved from 2.92 ± 2.39 to 9.96 ± 8.60 (p < 0.05) for DWI. The coefficient of variation (CV) of tumor CNR lowered from 1.57, 0.56, and 1.17 to 0.47, 0.44, and 0.46 for T1-w, T2-w and T2/T1-w MRI, respectively.

Conclusion

A multiparametric MRI fusion method was proposed and a prototype was developed. The method showed potential in improving clinically relevant features such as tumor contrast and liver signal. Synthesis of novel image contrasts including the composition of multiple image features into single image set was achieved.

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.

Utility of serum biomarkers for predicting cancer in patients with previous negative prostate biopsy

PURPOSE

To review the role of serum biomarkers: prostate-specific antigen (PSA), PSA density (PSAD), free:total PSA ratio, prostate health index (PHI) and PHI density (PHID), along with magnetic resonance imaging (MRI) for identification of clinically significant prostate cancer (PCa), comparing their utility in patients with persistently raised PSA levels after a prior negative prostate biopsy (PNB).

METHODS

In this single-centre prospective observational study conducted from September 2015 to October 2020, patients underwent a saturation biopsy via the transperineal route. If a Prostate Imaging Reporting and Data System version 2 (PIRADS) 3 and above lesion was seen on MRI, targeted biopsies were also obtained. Information on clinical history, lesion characteristics, PIRADS classification and follow-up was collected. The sensitivity, specificity and area under curve (AUC) for each of the biomarkers were calculated.

RESULTS

351 men underwent saturation biopsy with or without targeted biopsies. 103 patients had a PNB. Among this PNB cohort, 43 (41.7%) men had a benign outcome, while 60 (58.3%) men had histopathologically diagnosed PCa, of which 41 (39%) were clinically significant. All patients underwent multiparametric MRI scans prior to biopsy. Within this cohort, PHI and PHID had the best abilities to predict for clinically significant PCa with an AUC of 0.73 and 0.70 respectively, compared to 0.65 for PSAD, 0.34 for free:total PSA and 0.56 for PSA.

CONCLUSION

A significant proportion of patients are diagnosed with PCa after a PNB. This study shows that PHI and PHI densities may be suitable adjuncts predicting for clinically significant PCa in patients with PNB.

Artificial intelligence trained with integration of multiparametric MR-US imaging data and fusion biopsy trajectory-proven pathology data for 3D prediction of prostate cancer: A proof-of-concept study

BACKGROUND

We aimed to develop an artificial intelligence (AI) algorithm that predicts the volume and location of clinically significant cancer (CSCa) using convolutional neural network (CNN) trained with integration of multiparametric MR-US image data and MRI-US fusion prostate biopsy (MRI-US PBx) trajectory-proven pathology data.

METHODS

Twenty consecutive patients prospectively underwent MRI-US PBx, followed by robot-assisted radical prostatectomy (RARP). The AI algorithm was trained with the integration of MR-US image data with a MRI-US PBx trajectory-proven pathology. The relationship with the 3D-cancer-mapping of RARP specimens was compared between AI system-suggested 3D-CSCa mapping and an experienced radiologist's suggested 3D-CSCa mapping on MRI alone according to the Prostate Imaging Reporting and Data System (PI-RADS) version 2. The characteristics of detected and undetected tumors at AI were compared in 22,968 image data. The relationships between CSCa volumes and volumes predicted by AI as well as the radiologist's reading based on PI-RADS were analyzed.

RESULTS

The concordance of the CSCa center with that in RARP specimens was significantly higher in the AI prediction than the radiologist' reading (83% vs. 54%, p = 0.036). CSCa volumes predicted with AI were more accurate (r = 0.90, p < 0.001) than the radiologist's reading. The limitations include that the elastic fusion technology has its own registration error.

CONCLUSIONS

We presented a novel pilot AI algorithm for 3D prediction of PCa. AI was trained by integration of multiparametric MR-US image data and fusion biopsy trajectory-proven pathology data. This deep learning AI model may more precisely predict the 3D mapping of CSCa in its volume and center location than a radiologist's reading based on PI-RADS version 2, and has potential in the planning of focal therapy.

PI-RADS v2.1 Combined With Prostate-Specific Antigen Density for Detection of Prostate Cancer in Peripheral Zone

Purpose

To evaluate the diagnostic performance of combining the Prostate Imaging Reporting and Data System (PI-RADS) scoring system v2.1 with prostate-specific antigen density (PSAD) to detect prostate cancer (PCa).

Methods

A total of 266 participants with suspicion of PCa underwent multiparametric magnetic resonance imaging (mpMRI) in our hospital, after at least 4 weeks all patients underwent subsequent systematic transrectal ultrasound (TRUS)-guided biopsy or MRI-TRUS fusion targeted biopsy. All mpMRI images were scored in accordance with the PI-RADS v2.1, and univariate and multivariate logistic regression analyses were performed to determine significant predictors of PCa.

Results

A total of 119 patients were diagnosed with PCa in the biopsy, of them 101 patients were diagnosed with clinically significant PCa. The multivariate analysis revealed that PI-RADS v2.1 and PSAD were independent predictors for PCa. For peripheral zone (PZ), the area under the ROC curve (AUC) for the combination of PI-RADS score and PSAD was 0.90 (95% CI 0.83-0.96), which is significantly superior to using PI-RADS score (0.85, 95% CI 0.78-0.93, P=0.031) and PSAD alone (0.83, 95% CI 0.75-0.90, P=0.037). For transition zone (TZ), however, the combination model was not significantly superior to PI-RADS alone, with AUC of 0.94 (95% CI 0.89-0.99) vs. 0.93 (95% CI 0.88-0.97, P=0.186).

Conclusion

The combination of PI-RADS v2.1 with PSAD could significantly improve the diagnostic performance of PCa in PZ. Nevertheless, no significant improvement was observed regarding PCa in TZ.

Alternatives for MRI in Prostate Cancer Diagnostics-Review of Current Ultrasound-Based Techniques

The purpose of this review is to present the current role of ultrasound-based techniques in the diagnostic pathway of prostate cancer (PCa). With overdiagnosis and overtreatment of a clinically insignificant PCa over the past years, multiparametric magnetic resonance imaging (mpMRI) started to be recommended for every patient suspected of PCa before performing a biopsy. It enabled targeted sampling of the suspicious prostate regions, improving the accuracy of the traditional systematic biopsy. However, mpMRI is associated with high costs, relatively low availability, long and separate procedure, or exposure to the contrast agent. The novel ultrasound modalities, such as shear wave elastography (SWE), contrast-enhanced ultrasound (CEUS), or high frequency micro-ultrasound (MicroUS), may be capable of maintaining the performance of mpMRI without its limitations. Moreover, the real-time lesion visualization during biopsy would significantly simplify the diagnostic process. Another value of these new techniques is the ability to enhance the performance of mpMRI by creating the image fusion of multiple modalities. Such models might be further analyzed by artificial intelligence to mark the regions of interest for investigators and help to decide about the biopsy indications. The dynamic development and promising results of new ultrasound-based techniques should encourage researchers to thoroughly study their utilization in prostate imaging.

Comparison of prostate imaging reporting and data system v2.1 and 2 in transition and peripheral zones: evaluation of interreader agreement and diagnostic performance in detecting clinically significant prostate cancer

OBJECTIVE

To evaluate the interreader agreement and diagnostic performance of the Prostate Imaging Reporting and Data System (PI-RADS) v. 2.1, in comparison with v. 2.

METHODS

Institutional review board approval was obtained for this retrospective study. 77 consecutive patients who underwent a prostate multiparametric magnetic resonance imaging at 3.0 T before radical prostatectomy were included. Four radiologists (two experienced uroradiologists and two inexperienced radiologists) independently scored eight regions [six peripheral zones (PZ) and two transition zones (TZ)] using v. 2.1 and v. 2. Interreader agreement was assessed using κ statistics. To evaluate diagnostic performance for clinically significant prostate cancer (csPC), area under the curve (AUC) was estimated.

RESULTS

228 regions were pathologically diagnosed as positive for csPC. With a cut-off ≥3, the agreement among all readers was better with v. 2.1 than v. 2 in TZ, PZ, or both zones combined (κ-value: TZ, 0.509 vs 0.414; PZ, 0.686 vs 0.568; both zones combined, 0.644 vs 0.531). With a cut-off ≥4, the agreement among all readers was also better with v. 2.1 than v. 2 in the PZ or both zones combined (κ-value: PZ, 0.761 vs 0.701; both zones combined, 0.756 vs 0.709). For all readers, AUC with v. 2.1 was higher than with v. 2 (TZ, 0.826-0.907 vs 0.788-0.856; PZ, 0.857-0.919 vs 0.853-0.902).

CONCLUSION

Our study suggests that the PI-RADS v. 2.1 could improve the interreader agreement and might contribute to improved diagnostic performance compared with v. 2.

ADVANCES IN KNOWLEDGE

PI-RADS v. 2.1 has a potential to improve interreader variability and diagnostic performance among radiologists with different levels of expertise.

MR Imaging in Real Time Guiding of Therapies in Prostate Cancer

Magnetic resonance imaging (MRI)-guided therapy for prostate cancer (PCa) aims to reduce the treatment-associated comorbidity of existing radical treatment, including radical prostatectomy and radiotherapy. Although active surveillance has been used as a conservative method to reduce overtreatment, there is a growing demand for less morbidity and personalized (focal) treatment. The development of multiparametric MRI was of real importance in improving the detection, localization and staging of PCa. Moreover, MRI has been useful for lesion targeting within the prostate, as it is used in the guidance of prostate biopsies, by means of cognitive registration, MRI-ultrasound fusion guidance or direct in-bore MRI-guidance. With regard to PCa therapies, MRI is used for precise probe placement into the lesion and to accurately monitor the treatment in real-time. Moreover, advances in MR-compatible thermal ablation allow for noninvasive real-time temperature mapping during treatment. In this review, we present an overview of the current status of MRI-guided therapies in PCa, focusing on cryoablation, focal laser ablation, high intensity focused ultrasound and transurethral ultrasound ablation. We explain the important role of MRI in the evaluation of the completeness of the ablation and during follow-up. Finally, we will discuss the challenges and future development inherent to these new technologies.

Absolute choline tissue concentration mapping for prostate cancer localization and characterization using 3D 1 H MRSI without water-signal suppression

PURPOSE

Until now, 1 H MRSI of the prostate has been performed with suppression of the large water signal to avoid distortions of metabolite signals. However, this signal can be used for absolute quantification and spectral corrections. We investigated the feasibility of water-unsuppressed MRSI in patients with prostate cancer for water signal-mediated spectral quality improvement and determination of absolute tissue levels of choline.

METHODS

Eight prostate cancer patients scheduled for radical prostatectomy underwent multi-parametric MRI at 3 T, including 3D water-unsuppressed semi-LASER MRSI. A postprocessing algorithm was developed to remove the water signal and its artifacts and use the extracted water signal as intravoxel reference for phase and frequency correction of metabolite signals and for absolute metabolite quantification.

RESULTS

Water-unsuppressed MRSI with dedicated postprocessing produced water signal and artifact-free MR spectra throughout the prostate. In all patients, the absolute choline tissue concentration was significantly higher in tumorous than in benign tissue areas (mean ± SD: 7.2 ± 1.4 vs 3.8 ± 0.7 mM), facilitating tumor localization by choline mapping. Tumor tissue levels of choline correlated better with the commonly used (choline + spermine + creatine)/citrate ratio (r = 0.78 ± 0.1) than that of citrate (r = 0.21 ± 0.06). The highest maximum choline concentrations occurred in high-risk cancer foci.

CONCLUSION

This report presents the first successful water-unsuppressed MRSI of the whole prostate. The water signal enabled amelioration of spectral quality and absolute metabolite quantification. In this way, choline tissue levels were identified as tumor biomarker. Choline mapping may serve as a tool in prostate cancer localization and risk scoring in multi-parametric MRI for diagnosis and biopsy procedures.

Diagnostic Performance of Extraprostatic Extension Grading System for Detection of Extraprostatic Extension in Prostate Cancer: A Diagnostic Systematic Review and Meta-Analysis

PURPOSE

To evaluate the diagnostic performance of the extraprostatic extension (EPE) grading system for detection of EPE in patients with prostate cancer (PCa).

MATERIALS AND METHODS

We performed a literature search of Web of Science, MEDLINE (Ovid and PubMed), Cochrane Library, EMBASE, and Google Scholar to identify eligible articles published before August 31, 2021, with no language restrictions applied. We included studies using the EPE grading system for the prediction of EPE, with histopathological results as the reference standard. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), and diagnostic odds ratio (DOR) were calculated with the bivariate model. Quality assessment of included studies was performed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool.

RESULTS

A total of 4 studies with 1,294 patients were included in the current systematic review. The pooled sensitivity and specificity were 0.82 (95% CI 0.76-0.87) and 0.63 (95% CI 0.51-0.73), with the area under the hierarchical summary receiver operating characteristic (HSROC) curve of 0.82 (95% CI 0.79-0.85). The pooled LR+, LR-, and DOR were 2.20 (95% CI 1.70-2.86), 0.28 (95% CI 0.22-0.36), and 7.77 (95% CI 5.27-11.44), respectively. Quality assessment for included studies was high, and Deeks's funnel plot indicated that the possibility of publication bias was low (p = 0.64).

CONCLUSION

The EPE grading system demonstrated high sensitivity and moderate specificity, with a good inter-reader agreement. However, this scoring system needs more studies to be validated in clinical practice.

Evaluating the performance of clinical and radiological data in predicting prostate cancer in prostate imaging reporting and data system version 2.1 category 3 lesions of the peripheral and the transition zones

PURPOSE

To define the value of clinical and radiological data, using multiparametric magnetic resonance imaging (mpMRI), to predict prostate cancer (PCa) in prostate imaging reporting and data system version 2.1 (PIRADSv2.1) 3 lesions of the peripheral and the transition zones (PZ and TZ).

METHODS

The mpMRI of patients with PIRADSv2.1 3 lesions who had undergone fusion targeted biopsy was reviewed. Morphological pattern, diffusion parameters and vascularisation were evaluated. The radiological/histopathological data of benign and malignant lesions, between the PZ and TZ were compared. Univariate and multivariate analyses were carried out to identify the clinical and radiological data capable of predicting PCa.

RESULTS

One hundred and twenty-three lesions were assessed, 93 (76%) in the PZ and 30 (24%) in the TZ. Of these, 56 (46%) were PCa and 67 (54%) were benign. The majority of the PCas were Grade Group System (GGS) 1 (38%) and GGS 2 (39%); tumours having a GGS ≥ 3 were more frequently in the TZ (p = 0.02). Univariate analysis showed a significant correlation between PCa and prostate volume, prostate-specific antigen (PSA) density, lesion zone and the apparent diffusion coefficient. At multivariate logistic regression PSA density > 0.15 ng/ml/ml {Odds ratio [OR] 2.38; p = 0.001} and lesion zone (i.e. TZ OR 7.55) were independent predictors of PCa (all p ≤ 0.04).

CONCLUSION

In solitary PIRADSv2.1 3 lesions, the most important predictive factor was the location zone, with a much greater risk for TZ lesions.

Automatic PI-RADS assignment by means of formal methods

INTRODUCTION AND OBJECTIVES

The Prostate Imaging Reporting and Data System (PI-RADS) version 2 emerged as standard in prostate magnetic resonance imaging examination. The Pi-RADS scores are assigned by radiologists and indicate the likelihood of a clinically significant cancer. The aim of this paper is to propose a methodology to automatically mark a magnetic resonance imaging with its related PI-RADS.

MATERIALS AND METHODS

We collected a dataset from two different institutions composed by DWI ADC MRI for 91 patients marked by expert radiologists with different PI-RADS score. A formal model is generated starting from a prostate magnetic resonance imaging, and a set of properties related to the different PI-RADS scores are formulated with the help of expert radiologists and pathologists.

RESULTS

Our methodology relies on the adoption of formal methods and radiomic features, and in the experimental analysis, we obtain a specificity and sensitivity equal to 1.

Q CONCLUSIONS

The proposed methodology is able to assign the PI-RADS score by analyzing prostate magnetic resonance imaging with a very high accuracy.

The Quantitative Assessment of Using Multiparametric MRI for Prediction of Extraprostatic Extension in Patients Undergoing Radical Prostatectomy: A Systematic Review and Meta-Analysis

PURPOSE

To investigate the diagnostic performance of using quantitative assessment with multiparametric MRI (mpMRI) for prediction of extraprostatic extension (EPE) in patients with prostate cancer (PCa).

METHODS

We performed a computerized search of MEDLINE, Embase, Cochrane Library, Web of Science, and Google Scholar from inception until July 31, 2021. Summary estimates of sensitivity and specificity were pooled with the bivariate model, and quality assessment of included studies was performed with the Quality Assessment of Diagnostic Accuracy Studies-2. We plotted forest plots to graphically present the results. Multiple subgroup analyses and meta-regression were performed to explore the variate clinical settings and heterogeneity.

RESULTS

A total of 23 studies with 3,931 participants were included. The pooled sensitivity and specificity for length of capsular contact (LCC) were 0.79 (95% CI 0.75-0.83) and 0.77 (95% CI 0.73-0.80), for apparent diffusion coefficient (ADC) were 0.71 (95% CI 0.50-0.86) and 0.71 (95% CI 059-0.81), for tumor size were 0.62 (95% CI 0.57-0.67) and 0.75 (95% CI 0.67-0.82), and for tumor volume were 0.77 (95% CI 0.68-0.84) and 0.72 (95% CI 0.56-0.83), respectively. Substantial heterogeneity was presented among included studies, and meta-regression showed that publication year (≤2017 vs. >2017) was the significant factor in studies using LCC as the quantitative assessment (P=0.02).

CONCLUSION

Four quantitative assessments of LCC, ADC, tumor size, and tumor volume showed moderate to high diagnostic performance of predicting EPE. However, the optimal cutoff threshold varied widely among studies and needs further investigation to establish.

Multiparametric Prostate MRI in Biopsy-Naïve Men: A Prospective Evaluation of Performance and Biopsy Strategies

Objectives

This study aims to prospectively estimate the diagnostic performance of multiparametric prostate MRI (mpMRI) and compare the detection rates of prostate cancer using cognitive targeted transrectal ultrasound (TRUS) guided biopsies, targeted MR-guided in-bore biopsies (MRGB), or both methods combined in biopsy-naïve men.

Methods

The biopsy-naïve men referred for mpMRI (including T2-weighted, diffusion-weighted and dynamic contrast enhanced MRI) due to prostate cancer suspicion (elevated prostate-specific antigen or abnormal digital rectal examination) were eligible for inclusion. The images were scored according to Prostate Imaging Reporting and Data System (PI-RADS) v2, and men with PI-RADS 1-2 lesions were referred for routine systematic TRUS, while those with PI-RADS 3-5 lesions were randomized to MRGB or cognitive targeted TRUS. Men randomized to MRGB were referred to a secondary TRUS 2 weeks after MRGB. Gleason grade group ≥2 was defined as clinically significant prostate cancer. The performance of mpMRI was estimated using prostate cancer detected by any biopsy method as the reference test.

Results

A total of 210 men were included. There was no suspicion of prostate cancer after mpMRI (PI-RADS 1-2) in 48% of the men. Among these, significant and insignificant prostate cancer was diagnosed in five and 11 men, respectively. Thirty-five men who scored as PI-RADS 1-2 did not undergo biopsy and were therefore excluded from the calculation of diagnostic accuracy. The overall sensitivity, specificity, negative predictive value, and positive predictive value of mpMRI for the detection of significant prostate cancer were 0.94, 0.63, 0.92, and 0.67, respectively. In patients with PI-RADS 3-5 lesions, the detection rates for significant prostate cancer were not significantly different between cognitive targeted TRUS (68.4%), MRGB (57.7%), and the combination of the two biopsy methods (64.4%). The median numbers of biopsy cores taken per patient undergoing systematic TRUS, cognitive targeted TRUS, and MRGB were 14 [8-16], 12 [6-17], and 2 [1-4] respectively.

Conclusions

mpMRI, in a cohort of biopsy-naïve men, has high negative predictive value, and our results support that it is safe to avoid biopsy after negative mpMRI. Furthermore, MRGB provides a similar diagnosis to the cognitive targeted TRUS but with fewer biopsies.

Evaluation of multiparametric prostate magnetic resonance imaging findings in patients with a Gleason score of 6 in transrectal ultrasonography-guided biopsy

PURPOSE

We aimed to evaluate prostate multiparametric magnetic resonance imaging (mpMRI) findings of patients with a Gleason score (GS) of 6 and effectiveness of MRI based on the final pathology result in patients undergoing radical prostatectomy (RP).

MATERIAL AND METHODS

mpMRI findings of 80 patients who had a GS of 3 + 3 and who underwent mpMRI were evaluated retrospectively. The mpMRI were scored according to the PIRADS v2.1 guidelines. The patients were divided into those with a high probability of clinically significant cancer (CSC) (PI-RADS 4-5) and those with a low probability of CSC (PI-RADS 2-3).

RESULTS

Of the 80 patients, 33.8% had PI-RADS 2-3, and 66.2% had PI-RADS 4-5 lesions. There was a significant difference between the groups in prostate specific antigen (PSA) value, PSA density, patient age, and tumour percentage on biopsy. When the pathology results were taken as the gold standard in the group that underwent RP, sensitivity, specificity, and accuracy of mpMRI were calculated as 94.74%, 100%, and 96.3%, respectively, an increase in the final GS was found in 9 (33.3%) of the 27 patients, and 70.35% of patients were identified as having CSC.

CONCLUSIONS

PI-RADS 4-5 scores have high sensitivity and negative predictive value in the diagnosis of CSC. mpMRI is a reliable and non-invasive diagnostic method that can complement biopsy results in decision-making in patients who are initially evaluated as low risk.

A comparison of prostate cancer prediction models in men undergoing both magnetic resonance imaging and transperineal biopsy: Are the models still relevant?

OBJECTIVE

To externally validate and compare the performance of the European Randomized Study of Screening for Prostate Cancer risk calculator 3/4 (ERSPC-RC3/4), the Prostate Biopsy Collaborative Group risk calculator (PBCG-RC) and the van Leeuwen model to determine which prediction model would perform the best in a contemporary Australian cohort undergoing transperineal (TP) biopsy.

MATERIALS AND METHODS

A retrospective review identified all patients undergoing TP biopsy across two centres. Of the 797 patients identified, 373 had the data required to test all three risk calculators. The probability of high-grade prostate cancer, defined as International Society of Urological Pathology Grade Group >1, was calculated for each patient. For each prediction model discrimination was assessed using area under the receiver-operating characteristic curve (AUC), calibration using numerical and graphical summaries, and net benefit using decision curve analysis.

RESULTS

Assessment of model discrimination for detecting high-grade prostate cancer showed AUCs of 0.79 (95% confidence interval [CI] 0.74-0.84) for the ERSPC-RC3/4, 0.81 (95% CI 0.77-0.86) for the van Leeuwen model, and 0.68 (95% CI 0.63-0.74) for the PBCG-RC, compared to 0.58 (95% CI 0.52-0.65) for prostate-specific antigen alone. The ERSPC-RC3/4 was the best calibrated in the moderate-risk range of 10-40%, whilst the van Leeuwen model was the best calibrated in the low-risk range of 0-10%. The van Leeuwen model demonstrated the greatest net benefit from 10% risk onwards, followed closely by the ERSPC-RC3/4 and then the PBCG-RC.

CONCLUSION

The ERPSC-RC3/4 demonstrated good performance and was comparable to the van Leeuwen model with regard to discrimination, calibration and net benefit for an Australian population undergoing TP prostate biopsy. It is one of the most accessible risk calculators with an easy-to-use online platform, therefore, we recommend that Australian urologists use the ERSPC-RC3/4 to predict risk in the clinical setting.

Tumor contact length of prostate cancer determined by a three-dimensional method on multiparametric magnetic resonance imaging predicts extraprostatic extension and biochemical recurrence

OBJECTIVE

To evaluate the clinical benefit of tumor contact length as a predictor of pathological extraprostatic extension and biochemical recurrence in patients undergoing prostatectomy.

METHODS

A total of 91 patients who underwent 3T multiparametric magnetic resonance imaging before prostatectomy from April 2014 to July 2019 were included. A total of 94 prostate cancer foci were analyzed retrospectively. We evaluated maximum tumor contact length, which was determined to be the maximum value in the three-dimensional directions, as a predictor of pathological extraprostatic extension and biochemical recurrence.

RESULTS

A total of 19 lesions (20.2%) had positive pathological extraprostatic extension. Areas under the curves showed maximum tumor contact length to be a significantly better parameter to predict pathological extraprostatic extension than the Prostate Imaging Reporting and Data System (P = 0.002), tumor maximal diameter (P = 0.001), prostate-specific antigen (P = 0.020), Gleason score (P < 0.001), and clinical T stage (P < 0.001). Multivariate analysis showed maximum tumor contact length (P = 0.003) to be an independent risk factor for predicting biochemical recurrence. We classified the patients using preoperative factors (prostate-specific antigen >10, Gleason score >3 + 4 and maximum tumor contact length >10 mm) into three groups: (i) high-risk group (patients having all factors); (ii) intermediate-risk group (patients having two of three factors); and (iii) low-risk group (patients having only one or none of the factors). Kaplan-Meier curves showed that the high-risk group had significantly worse biochemical recurrence than the intermediate-risk group (P = 0.042) and low-risk group (P < 0.001).

CONCLUSIONS

Our findings suggest that maximum tumor contact length is an independent predictor of pathological extraprostatic extension and biochemical recurrence. A risk stratification system using prostate-specific antigen, Gleason score and maximum tumor contact length might be useful for preoperative assessment of prostate cancer patients.

Development and validation of a nomogram based on multiparametric magnetic resonance imaging and elastography-derived data for the stratification of patients with prostate cancer

Background

This study sought to develop and validate a nomogram combining the elastographic Q-analysis score (EQS), the Prostate Imaging Reporting and Data System (PI-RADS) score, and clinical parameters for the stratification of patients with prostate cancer (PCa).

Methods

A retrospective study was conducted of 375 patients with 375 lesions who underwent volume-navigation transrectal ultrasound (TRUS) and multiparametric magnetic resonance imaging (MP-MRI)-fusion targeted biopsies between April 2017 and January 2020. Based on a multivariate logistic regression model, a nomogram was created to assess any PCa and high-risk PCa [Gleason score (GS) ≥4+3] using data from patients diagnosed between April 2017 and June 2019 (n=271), and was validated in patients diagnosed after July 2019 (n=104). The nomogram's performance was evaluated based on its discrimination, calibration, and clinical usefulness.

Results

The areas under the curve (AUCs) of the nomogram for predicting any PCa and high-risk PCa were 0.949 [95% confidence interval (CI), 0.921 to 0.978] and 0.936 (95% CI, 0.906 to 0.965), respectively, in the training cohort, and 0.946 (95% CI, 0.894 to 0.997) and 0.971 (95% CI, 0.9331 to 1), respectively, in the validation cohort. The nomogram was well calibrated, and no significant difference was found between the predicted and observed probabilities. A decision curve analysis (DCA) for the nomogram with and without the EQS showed that the threshold probability of for any PCa was <67%.

Conclusions

The nomogram that combined elastography-derived and MP-MRI data was more clinically useful than the model based on PI-RADS and clinical parameters alone. Our nomogram could aid urologists to make decisions and avoid unnecessary biopsies.

Diagnostic accuracy of the Novel 29 MHz micro-ultrasound "ExactVuTM" for the detection of clinically significant prostate cancer: A prospective single institutional study. A step forward in the diagnosis of prostate cancer

INTRODUCTION AND OBJECTIVE

ExactVuTM is a real-time micro-ultrasound system which provides, according to the Prostate Risk Identification Using Micro-Ultrasound protocol (PRI-MUS), a 300% higher resolution compared to conventional transrectal ultrasound. To evaluate the performance of ExactVuTM in the detection of Clinically significant Prostate Cancer (CsPCa).

MATERIALS AND METHODS

Patients with Prostate Cancer diagnosed at fusion biopsy were imaged with ExactVuTM. CsPCa was defined as any Gleason Score ≥ 3+4. ExactVuTM examination was considered as positive when PRI-MUS score was ≥ 3. PRI-MUS scoring system was considered as correct when the fusion biopsy was positive for CsPCa. A transrectal fusion biopsy- proven CsPCa was considered as a gold standard. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under the receiver operator characteristic (ROC) curve (AUC) were calculated.

RESULTS

57 patients out of 68 (84%) had a csPCa. PRI-MUS score was correctly assessed in 68% of cases. Regarding the detection of CsPCa, ExactVuTM 's sensitivity, specificity, PPV, and NPV was 68%, 73%, 93%, and 31%, respectively and the AUC was 0.7 (95% CI 0.5-0-8). For detecting CsPCa in the transition/ anterior zone the sensitivity, specificity, PPV, and NPV was 45%, 66%, 83% and 25% respectively ant the AUC was 0.5 (95% CI 0.2-0.9). Accounting only the CsPCa located in the peripheral zone, sensitivity, specificity, PPV, and NPV raised up to 74%, 75%, 94%, 33%, respectively with AUC 0.75 (95% CI 0.5-0-9).

CONCLUSIONS

ExactVuTM provides high resolution of the prostatic peripheral zone and could represent a step forward in the detection of CsPCa as a triage tool. Further studies are needed to confirm these promising results.

Improvement of PI-RADS-dependent prostate cancer classification by quantitative image assessment using radiomics or mean ADC

Background Currently, interpretation of prostate MRI is performed qualitatively. Quantitative assessment of the mean apparent diffusion coefficient (mADC) is promising to improve diagnostic accuracy while radiomic machine learning (RML) allows to probe complex parameter spaces to identify the most promising multi-parametric models. We have previously developed quantitative RML and ADC classifiers for prediction of clinically significant prostate cancer (sPC) from prostate MRI, however these have not been combined with radiologist PI-RADS assessment. Purpose To propose and evaluate diagnostic algorithms combining quantitative ADC or RML and qualitative PI-RADS assessment for prediction of sPC. Methods and population The previously published quantitative models (RML and mADC) were utilized to construct four algorithms: 1) Down(ADC) and 2) Down(RML): clinically detected PI-RADS positive prostate lesions (defined as either PI-RADS≥3 or ≥4) were downgraded to MRI negative upon negative quantitative assessment; and 3) Up(ADC) and 4) Up(RML): MRI-negative lesions were upgraded to MRI-positive upon positive assessment of quantitative parameters. Analyses were performed at the individual lesion level and the patient level in 133 consecutive patients with suspicion for clinically significant prostate cancer (sPC, International Society of Urological Pathology (ISUP) grade group≥2), the test set subcohort of a previously published patient population. McNemar test was used to compare differences in sensitivity, specificity and accuracy. Differences between lesions of different prostate zones were assessed using ANOVA. Reduction in false positive assessments was assessed as ratios. Results Compared to clinical assessment at the PI-RADS≥4 cut-off alone, algorithms Down(ADC/RML) improved specificity from 43% to 65% (p = 0.001)/62% (p = 0.003), while sensitivity did not change significantly at 89% compared to 87% (p = 1.0)/89% (unchanged) on the patient level. Reduction of false positive lesions was 50% [26/52] in the PZ and 53% [15/28] in the TZ. Algorithms Up(ADC/RML) led, on a patient basis, to an unfavorable loss of specificity from 43% to 30% (p = 0.039)/32% (p = 0.106), with insignificant increase of sensitivity from 89% to 96%/96% (both p = 1.0). Compared to clinical assessment at the PI-RADS≥3 cut-off alone, similar results were observed for Down(ADC) with significantly increased specificity from 2% to 23% (p < 0.001) and unchanged sensitivity on the lesion level; patient level specificity increased only non-significantly. Conclusion Downgrading PI-RADS≥3 and ≥ 4 lesions based on quantitative mADC measurements or RML classifiers can increase diagnostic accuracy by enhancing specificity and preserving sensitivity for detection of sPC and reduce false positives.

Neoadjuvant PROSTVAC prior to radical prostatectomy enhances T-cell infiltration into the tumor immune microenvironment in men with prostate cancer

Background

Clinical trials have shown the ability of therapeutic vaccines to generate immune responses to tumor-associated antigens (TAAs). What is relatively less known is if this translates into immune-cell (IC) infiltration into the tumor microenvironment. This study examined whether neoadjuvant prostate-specific antigen (PSA)-targeted vaccination with PROSTVAC could induce T-cell immunity, particularly at the tumor site.

Methods

An open-label, phase II study of neoadjuvant PROSTVAC vaccine enrolled 27 patients with localized prostate cancer awaiting radical prostatectomy (RP). We evaluated increases in CD4 and CD8 T-cell infiltrates (RP tissue vs baseline biopsies) using a six-color multiplex immunofluorescence Opal method. Antigen-specific responses were assessed by intracellular cytokine staining after in vitro stimulation of peripheral blood mononuclear cells with overlapping 15-mer peptide pools encoding the TAAs PSA, brachyury and MUC-1.

Results

Of 27 vaccinated patients, 26 had matched prevaccination (biopsy) and postvaccination (RP) prostate samples available for non-compartmentalized analysis (NCA) and compartmentalized analysis (CA). Tumor CD4 T-cell infiltrates were significantly increased in postvaccination RP specimens compared with baseline biopsies by NCA (median 176/mm² vs 152/mm²; IQR 136–317/mm² vs 69–284/mm²; p=0.0249; median ratio 1.20; IQR 0.64–2.25). By CA, an increase in both CD4 T-cell infiltrates at the tumor infiltrative margin (median 198/mm² vs 151/mm²; IQR 123–500/mm² vs 85–256/mm²; p=0.042; median ratio 1.44; IQR 0.59–4.17) and in CD8 T-cell infiltrates at the tumor core (median 140/mm² vs 105/mm²; IQR 91–175/mm² vs 83–163/mm²; p=0.036; median ratio 1.25; IQR 0.88–2.09) were noted in postvaccination RP specimens compared with baseline biopsies. A total of 13/25 patients (52%) developed peripheral T-cell responses to any of the three tested TAAs (non-neoantigens); five of these had responses to more than one antigen of the three evaluated.

Conclusion

Neoadjuvant PROSTVAC can induce both tumor immune response and peripheral immune response.

Trial registration number

NCT02153918.

Multiparametric Magnetic Resonance Imaging in Evaluation of Clinically Significant Prostate Cancer

Aim  In this prospective study, we evaluate the role of multiparametric magnetic resonance imaging (mp-MRI) in the assessment of clinically significant prostate cancer at 1.5 T without endorectal coil (ERC).

Materials and Methods  Forty-five men with clinical suspicion of prostate cancer (prostate-specific antigen [PSA] level > 4 ng/mL, hard prostate on digital rectal examination, and suspicious area at transrectal ultrasound [TRUS]) were evaluated using the mp-MRI protocol over a period of 24 months. All cases were interpreted using the Prostate Imaging Reporting and Data System (PI-RADS) version 2 guidelines and correlated with histopathology.

Statistical Analysis Used  A chi-squared test was used for analysis of nominal/categorical variables and receiver operating characteristic (ROC) curve and one-way analysis of variance (ANOVA) test for continuous variables.

Results  The mean age was 67 years and the mean PSA was 38.2 ng/mL. Eighty percent had prostate cancer and 20% were benign (11% benign prostatic hyperplasia [BPH] and 9% chronic prostatitis). Eighty-six percent of all malignancies were in the peripheral zone. The PI-RADS score for T2-weighted (T2W) imaging showed good sensitivity (81%) but low specificity (67%). The PI-RADS score for diffusion weighted imaging (DWI) with sensitivity of 92% and specificity of 78% had a better accuracy overall than T2W imaging alone. The mean apparent diffusion coefficient (ADC) value (×10 ^–6 mm ² /s) was 732 ± 160 in prostate cancer, 1,009 ± 161 in chronic prostatitis, 1,142 ± 82 in BPH, and 663 in a single case of granulomatous prostatitis. Low ADC values (<936) have shown good correlation (area under curve [AUC]: 0.87) with the presence of cancer foci. Inverse correlation was observed between Gleason scores and ADC values. Dynamic contrast-enhanced (DCE) imaging has shown 100% sensitivity/negative predictive value (NPV), but moderate specificity (67%) in predicting malignancy. The final PI-RADS score had 100% sensitivity and NPV with good overall positive predictive value (PPV) of 95%.

Conclusions  T2W imaging and DWI remain the mainstays in diagnosis of prostate cancer with mp-MRI. DCE-MRI can be a problem-solving tool in case of equivocal findings. Because assessment with mp-MRI can be subjective, use of the newly developed PI-RADS version 2 scoring system is helpful in accurate interpretation.

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.

[68Ga-PSMA-11 PET/mpMRI for local detection of primary prostate cancer in men with a negative prior biopsy]

INTRODUCTION AND OBJECTIVE

 Multiparametric MRI (mpMRI) represents the current gold standard for the detection of primary prostate cancer (PC) after a negative biopsy. PSMA PET imaging has been introduced in the diagnostic work-up of PC with high accuracy, but is currently mainly utilised in the setting of biochemical recurrence. This study aimed to determine the efficacy of combined ⁶⁸Ga-PSMA-11 PET/mpMRI imaging to detect PC in patients with previously negative prostate biopsies.

METHODS

 A total of 57 patients who had undergone at least one prior negative prostate biopsy were included in this retrospective analysis. All patients underwent ⁶⁸Ga-PSMA-11 PET/mpMRI imaging of the prostate. mpMRI was evaluated according to the PIRADS classification system and ⁶⁸Ga-PSMA-11 PET was rated on a 5-point Likert scale (1: PC highly unlikely; 2: PC unlikely; 3: presence of PC is equivocal; 4: PC likely; 5: PC highly likely). All patients received a systematic random biopsy as well as a targeted transrectal biopsy of lesions suspicious on imaging. Imaging and histological biopsy outcomes were compared on a per-patient basis.

RESULTS

 In the histological analysis, 35/57 (61.4 %) patients harboured PC lesions. In patients with biopsy-proven PC, 21/35 (60.0 %) had a PI-RADS 4 or 5 lesion on mpMRI and 28 /35 (80.0 %) had a PET rating of 4 or 5. Combined ⁶⁸Ga-PSMA-11 PET/mpMRI missed only one patient with a Gleason score (GS) 7a tumour (rating of 1 or 2 in both PET and mpMRI). Limitations include the retrospective analysis as well as possible false negative biopsy results even in a fusion biopsy setting.

CONCLUSION

 In this initial analysis, the combined ⁶⁸Ga-PSMA-11 PET/mpMRI proved to be a valuable imaging tool to guide prostate biopsies for the detection of PC in patients with a negative prior biopsy. In this approach, ⁶⁸Ga-PSMA-11 PET and mpMRI show partially complementary findings that enhance the detection of PC lesions.

For men enrolled in active surveillance, pre-biopsy biparametric magnetic resonance imaging significantly reduces the risk of reclassification and disease progression after 1 year

AIMS

To assess the level of disease progression at confirmatory staging biopsies after 1 year of active surveillance (AS) and compare the detection rate of significant prostate cancers (PCas) in patients who underwent pre-biopsy biparametric magnetic resonance imaging (bpMRI) before the first set of diagnostic transrectal ultrasonography-guided biopsies (TRUS-bx) with the detection rate in patients who did not undergo pre-biopsy bpMRI.

MATERIALS AND METHODS

Comparison of two patient groups enrolled in AS. Patients in Group A (n = 127) underwent pre-biopsy bpMRI followed by TRUS-bx ± targeted biopsies. Patients in Group B (n = 127) were enrolled in AS based on biopsy results from TRUS-bx only.

RESULTS

Overall, 6% of the patients in Group A and 20% of the patients in Group B had an upgrade in Gleason grade from insignificant to significant PCa at confirmatory staging biopsies (odds ratio [OR], 3.5; p = .002; 95% confidence interval [CI], 1.6-7.9).

CONCLUSIONS

Patients who underwent pre-biopsy bpMRI before the first set of diagnostic biopsies had a reduced risk of reclassification and disease progression after 1 year of AS. Thus, pre-biopsy bpMRI improves the selection of men who should be enrolled in AS.

Diffusion Is Directional: Innovative Diffusion Tensor Imaging to Improve Prostate Cancer Detection

In the prostate, water diffusion is faster when moving parallel to duct and gland walls than when moving perpendicular to them, but these data are not currently utilized in multiparametric magnetic resonance imaging (mpMRI) for prostate cancer (PCa) detection. Diffusion tensor imaging (DTI) can quantify the directional diffusion of water in tissue and is applied in brain and breast imaging. Our aim was to determine whether DTI may improve PCa detection. We scanned patients undergoing mpMRI for suspected PCa with a DTI sequence. We calculated diffusion metrics from DTI and diffusion weighted imaging (DWI) for suspected lesions and normal-appearing prostate tissue, using specialized software for DTI analysis, and compared predictive values for PCa in targeted biopsies, performed when clinically indicated. DTI scans were performed on 78 patients, 42 underwent biopsy and 16 were diagnosed with PCa. The median age was 62 (IQR 54.4–68.4), and PSA 4.8 (IQR 1.3–10.7) ng/mL. DTI metrics distinguished PCa lesions from normal tissue. The prime diffusion coefficient (λ₁) was lower in both peripheral-zone (p < 0.0001) and central-gland (p < 0.0001) cancers, compared to normal tissue. DTI had higher negative and positive predictive values than mpMRI to predict PCa (positive predictive value (PPV) 77.8% (58.6–97.0%), negative predictive value (NPV) 91.7% (80.6–100%) vs. PPV 46.7% (28.8–64.5%), NPV 83.3% (62.3–100%)). We conclude from this pilot study that DTI combined with T2-weighted imaging may have the potential to improve PCa detection without requiring contrast injection.

Multiparametric MRI with in-bore targeted biopsy in the diagnostic pathway of prostate cancer: Data from a single institution experience

BACKGROUND

Accuracy of multiparametric MRI (mpMRI) for the detection of significant prostate cancer (CaP) varies in the literature as only few studies use radical prostatectomy specimens as their gold standard. On another hand, MRI-targeted prostate biopsy is emerging as an alternative to the traditional randomized biopsy, with a higher detection rate of high-grade cancers. However, data on MRI guided in bore biopsy is lacking.

MATERIAL AND METHODS

We reviewed every patient that had his mpMRI, MRI guided in bore biopsy and radical prostatectomy performed in our hospital between November 2015 and December 2020. The diagnostic performances of both mpMRI and MRI targeted biopsy in sampling PIRADS index lesions were studied, using radical prostatectomy specimens as the gold standard. Sensitivity, specificity, positive predictive value and negative predictive value of mpMRI for detecting T3 stage, extra-capsular extension, seminal vesicles involvement and lymph node disease were also evaluated.

RESULTS

Sixty-two met our inclusion criteria. For PIRADS≥3 lesions, sensitivity and positive predictive value for detecting clinically significant CaP were of 83.5% and 94.7%. A total of 32.2% prostate cancers on targeted biopsy were upgraded on final pathology, with an upgrading to ISUP≥2 in 3.2% and to ISUP≥3 in 14.5%. A total of 20.9% of cancers were downgraded but without any downgrading to ISUP 1. When final pathology is taken as a gold standard, sensitivity of mpMRI was 31.8% for T3 staging prediction, 30.0% for extra-capsular extension, 28.7% for seminal vesicles involvement and 66.7% for lymph node disease prediction. Specificity was 89.3%, 93.1%, 95.3%, and 92.7%, respectively.

CONCLUSION

mpMRI has an acceptable accuracy for the prediction of significant CaP and index lesion detection but is unreliable for CaP staging. Comparison between pathology and biopsy results revealed that the in-bore biopsy technique has an upgrading and downgrading rate comparable in the literature to fusion biopsy, but higher than the combined biopsy approach.

Role of MRI for the detection of prostate cancer

The use of multiparametric MRI has been hastened under expanding, novel indications for its use in the diagnostic and management pathway of men with prostate cancer. This has helped drive a large body of the literature describing its evolving role over the last decade. Despite this, prostate cancer remains the only solid organ malignancy routinely diagnosed with random sampling. Herein, we summarize the components of multiparametric MRI and interpretation, and present a critical review of the current literature supporting is use in prostate cancer detection, risk stratification, and management.

Value of clinical parameters and MRI with PI-RADSV2 in predicting seminal vesicle invasion of prostate cancer

OBJECTIVE

To investigate the usefulness of magnetic resonance imaging (MRI) with Prostate Imaging Reporting and Data System version 2 (PI-RADS_V2) and clinical parameters in predicting seminal vesicle invasion (SVI).

MATERIAL AND METHODS

In this retrospective study, we identified 569 prostate cancer patients who underwent radical prostatectomy with MRI before surgery. SVI was interpreted with PI-RADS_V2. Clinical parameters such as the prostate-specific antigen (PSA) and Gleason score (GS) were analyzed for the prediction of SVI. Logistic regression models and receiver operating characteristic (ROC) curves were used to evaluate SVI based on clinical parameters and MRI with PI-RADS_V2.

RESULTS

The median age at presentation was 67 years (43-85 years). The median PSA level was 6.1 ng/mL (2.2-72.8 ng/mL). There were 113 patients with a biopsy GS of ≥ 8. A total of 34 patients (6.0%) were interpreted to have SVI by MRI of which 20 were true positive, and 52 patients (9.1%) had true SVI in the final pathologic analysis. In multivariable analysis, PSA (HR: 1.03, 95% CI: 1.00-1.07), biopsy GS ≥ 8 (HR: 4.14, 95% CI: 2.12-8.09), and MRI with PI-RADS_V2 (HR: 14.67, 95% CI: 6.34-33.93) were significantly associated with pathologic SVI. The area under the curve of the model based on the clinical parameters PSA and GS plus MRI (0.862) was significantly larger than that of the model based on clinical parameters alone (0.777, p < 0.001).

CONCLUSIONS

MRI with PI-RADS_V2 using the clinical parameters PSA and GS was effective in predicting SVI.

Multicenter Multireader Evaluation of an Artificial Intelligence-Based Attention Mapping System for the Detection of Prostate Cancer With Multiparametric MRI

OBJECTIVE. The purpose of this study was to evaluate in a multicenter dataset the performance of an artificial intelligence (AI) detection system with attention mapping compared with multiparametric MRI (mpMRI) interpretation in the detection of prostate cancer. MATERIALS AND METHODS. MRI examinations from five institutions were included in this study and were evaluated by nine readers. In the first round, readers evaluated mpMRI studies using the Prostate Imaging Reporting and Data System version 2. After 4 weeks, images were again presented to readers along with the AI-based detection system output. Readers accepted or rejected lesions within four AI-generated attention map boxes. Additional lesions outside of boxes were excluded from detection and categorization. The performances of readers using the mpMRI-only and AI-assisted approaches were compared. RESULTS. The study population included 152 case patients and 84 control patients with 274 pathologically proven cancer lesions. The lesion-based AUC was 74.9% for MRI and 77.5% for AI with no significant difference (p = 0.095). The sensitivity for overall detection of cancer lesions was higher for AI than for mpMRI but did not reach statistical significance (57.4% vs 53.6%, p = 0.073). However, for transition zone lesions, sensitivity was higher for AI than for MRI (61.8% vs 50.8%, p = 0.001). Reading time was longer for AI than for MRI (4.66 vs 4.03 minutes, p < 0.001). There was moderate interreader agreement for AI and MRI with no significant difference (58.7% vs 58.5%, p = 0.966). CONCLUSION. Overall sensitivity was only minimally improved by use of the AI system. Significant improvement was achieved, however, in the detection of transition zone lesions with use of the AI system at the cost of a mean of 40 seconds of additional reading time.

Efficacy of 3T Multiparametric MR Imaging followed by 3T in-Bore MR-Guided Biopsy for Detection of Clinically Significant Prostate Cancer Based on PIRADSv2.1 Score

PURPOSE

To evaluate the diagnostic yield of 3T in-Bore magnetic resonance-guided biopsy (3T IB-MRGB) for detection of clinically significant prostate cancer (csPCa), based on assessment using the Prostate Imaging Reporting and Data System version 2.1 (PIRADSv2.1).

MATERIALS AND METHODS

This single-center study examined individuals who underwent 3T multiparametric prostate magnetic resonance (MR) imaging and subsequent 3T IB-MRGB. The final study cohort included 379 men (with 475 targets) divided into 3 subcohorts: biopsy-naïve men (n = 123), individuals with a history of negative trans-rectal-ultrasonography (TRUS) biopsy results (n = 106), and men with low-grade PCa under active surveillance (n = 150). csPCa was defined as having a Gleason score (GS) ≥3+4.

RESULTS

3T IB-MRGB detected PCa and csPCa in 69.1% (262 of 379) and 50.3% (193 of 379) of patients, respectively. The PCa and csPCa detection rates per target were 64.2% (305 of 475) and 43.8% (208 of 475), respectively. The rate of urosepsis, treated with intravenous antibiotics, was 1% (4 patients). In TRUS biopsy negative results and biopsy-naïve subcohorts, csPCa was found in 36.8% (39 of 106) and 52.8% (65 of 123), respectively. In 50.7% (76 of 150) of the active surveillance subcohort, 3T IB-MRGB upgraded the GS assigned in prior TRUS biopsies. Positive predictive values of PIRADSv2.1 categories 3, 4, and 5 for csPCa detection were 24.8%, 44.4%, and 67.1%, respectively. Higher PIRADSv2.1 categories were significantly associated with PCa (odds ratio [OR], 3.97; 95% confidence interval [CI], 2.98-5.28) and csPCa (OR, 1.41; 95% CI, 1.03-1.94) detection. Of 137 PIRADSv2 category 3 lesions, 28 were downgraded to PIRADSv2.1 category 2, in which there were no occurrences of csPCa in histology.

CONCLUSIONS

Use of 3T IB-MRGB resulted in detection of csPCa in 50.9% of individuals. 3T IB-MRGB has a high diagnostic yield in individuals with negative TRUS biopsy results and those under active surveillance. The PIRADSv2.1 category is a strong predictor of PCa and csPCa detection.

Performance of Ga-68 PSMA PET/CT for diagnosis and grading of local prostate cancer

Background

We aimed to evaluate the utility of prostate-specific membrane antigen (PSMA) PET/CT for the detection of local disease within the prostate.

Methods

This is a retrospective review of a single-center experience evaluating intraprostatic detection rates compared with final histopathology in a radical prostatectomy (RP) population. Seventy-two patients had PSMA PET/CT scan performed as part of their primary staging. Intraprostatic PSMA PET/CT avidity was assessed. PSMA PET/CT uptake was retrospectively correlated with patient characteristics including final histopathology, MRI Prostate Imaging Reporting and Data System (PI-RADS) score, clinical tumor stage, prostate-specific antigen (PSA) level, and patient age.

Results

The sensitivity of PSMA PET/CT for the detection of RP-confirmed prostate cancer was 81.2%. Much higher sensitivity was found within certain subpopulations. The patient characteristics that most strongly correlated with focal intraprostatic PSMA PET/CT uptake were patient age (Kendall's tau coefficient τ_b = 0.24, p < 0.05) and clinical T stage (τ_b = 0.21, p < 0.05).The International Society of Urological Pathology (ISUP) grade group from final RP was predicted by standardized uptake value (SUV_max) and to a lesser extent PSA and the maximal dimension of PET-avid lesions. SUV_max monotonically increased with ISUP grade group. If SUV_max was above 10 g/mL, the final RP histopathology had a relative risk of 2.3 (95% CI 1.3-4.1) of being ISUP grade group 5.

Conclusion

This trial provides early evidence that PSMA PET/CT assists in the grading of prostate cancer and suggests that the imaging modality is particularly accurate in subpopulations including the elderly and those with palpable disease.

Voxel-based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI

Purpose

The aim of this study was to develop and assess the performance of supervised machine learning technique to classify magnetic resonance imaging (MRI) voxels as cancerous or noncancerous using noncontrast multiparametric MRI (mp‐MRI), comprised of T2‐weighted imaging (T2WI), diffusion‐weighted imaging (DWI), and advanced diffusion tensor imaging (DTI) parameters.

Materials and methods

In this work, 191 radiomic features were extracted from mp‐MRI from prostate cancer patients. A comprehensive set of support vector machine (SVM) models for T2WI and mp‐MRI (T2WI + DWI, T2WI + DTI, and T2WI + DWI + DTI) were developed based on novel Bayesian parameters optimization method and validated using leave‐one‐patient‐out approach to eliminate any possible overfitting. The diagnostic performance of each model was evaluated using the area under the receiver operating characteristic curve (AUROC). The average sensitivity, specificity, and accuracy of the models were evaluated using the test data set and the corresponding binary maps generated. Finally, the SVM plus sigmoid function of the models with the highest performance were used to produce cancer probability maps.

Results

The T2WI + DWI + DTI models using the optimal feature subset achieved the best performance in prostate cancer detection, with the average AUROC , sensitivity, specificity, and accuracy of 0.93 ± 0.03, 0.85 ± 0.05, 0.82 ± 0.07, and 0.83 ± 0.04, respectively. The average diagnostic performance of T2WI + DTI models was slightly higher than T2WI + DWI models (+3.52%) using the optimal radiomic features.

Conclusions

Combination of noncontrast mp‐MRI (T2WI, DWI, and DTI) features with the framework of a supervised classification technique and Bayesian optimization method are able to differentiate cancer from noncancer voxels with high accuracy and without administration of contrast agent. The addition of cancer probability maps provides additional functionality for image interpretation, lesion heterogeneity evaluation, and treatment management.

Can fewer transperineal systematic biopsy cores have the same prostate cancer detection rate as of magnetic resonance imaging/ultrasound fusion biopsy?

BACKGROUND

Due to the discomfort and incidence of complications increases with the increasing number of biopsy cores, the protocol of prostate biopsy has been promoted from systematic biopsy (SB) to targeted biopsy (TB) in many studies. However, the optimal prostate sampling scheme to balance the incidence of biopsy complications and accuracy of biopsy remains controversial. Our objective is to explore an optimal prostate cancer (PCa) sampling scheme with fewer SB cores.

METHODS

Patients with at least one lesion of Prostate Imaging Reporting and Data System ≥3 were prospectively enrolled. TB and SB were performed for each patient as reference. The hypothetical biopsy sampling schemes were TB only, SB only, and TB followed by SB of the nontargeted sector (TB+nSB). The PCa and clinically significant PCa (csPCa) detection rates and cores of the three hypothetical biopsy schemes were compared with TB+SB.

RESULTS

Among 165 patients, 107 (64.8%) were diagnosed with PCa and 91 (55.2%) with csPCa via TB+SB. There were 54 (50.5%) and 42 (46.2%) magnetic resonance imaging (MRI) true negative cases and 53 (49.5%) and 49 (53.8%) false negative cases of nontargeted sectors among PCa and csPCa patients, respectively. The maximal cancer proportion in positive biopsy cases differed significantly between the true and false groups of these cohorts. There was no difference between TB+nSB and TB+SB for PCa or csPCa detection.

CONCLUSIONS

The optimal sampling scheme TB+nSB with fewer SB cores showed same PCa and csPCa detection rates as that of standard TB+SB with MRI/ultrasound fusion biopsy.

Evaluating the efficacy of a low-cost cognitive MRI-targeted prostate biopsy protocol: is there still a role for lower volume centers in the Prostate Imaging Reporting and Data System (PI-RADS) version 2 era?

PURPOSE

MRI-targeted biopsy has improved prostate biopsy yield. However, cost constraints have made it difficult for many institutions to implement the newer methods. We evaluated the performance of a low-cost cognitive-targeting biopsy protocol based on 1.5 T multiparametric MRI graded with Prostate Imaging Reporting and Data System (PI-RADS) version 2 to examine the role for these institutions moving forward.

METHODS

Retrospective analysis of 251 consecutive patients with prostate-specific antigen (PSA) under 50 who underwent MRI and subsequent prostate biopsy at a single facility. In addition to systematic biopsy, targeted cores were obtained with cognitive recognition under ultrasound. A control group of 267 consecutive patients with PSA under 50 biopsied without prior MRI was analyzed.

RESULTS

Prostate biopsy preceded by MRI had a significantly higher probability of detecting both prostate cancer (68.1% vs. 51.3%) and clinically significant prostate cancer (57.4% vs. 39.7%) (p values < 0.01). Combination of systematic and targeted biopsy outperformed either regimen alone. PSA density and PI-RADS score were identified as independent risk factors, and a proposed diagnostic model (PSA density ≥ 0.25 or PI-RADS score ≥ 4) showed sensitivity of 88.6%, specificity of 55%, PPV of 81.2%, NPV of 68.8%, and accuracy of 78.0%.

CONCLUSIONS

Both pre-biopsy MRI and cognitive-targeted biopsy contributed to improvement of cancer yield. Future alterations of possible benefit included increasing target cores per lesion, and combining PI-RADS score and PSA density as indicators for biopsy. Similar protocols may represent an on-going role for lower volume centers in the diagnosis of prostate cancer.