Eran Ben-Levi

In patients with a previous negative prostate biopsy and a suspicious lesion on magnetic resonance imaging, is a 12-core biopsy still necessary in addition to a targeted biopsy?

To evaluate the performance of multiparametric magnetic resonance imaging (mpMRI) in predicting prostate cancer on repeat biopsy; and to compare the cancer detection rates (CDRs) of MRI/transrectal ultrasonography (TRUS) fusion‐guided biopsy with standard 12‐core biopsy in men with at least one previous negative biopsy.

In patients with a previous negative prostate biopsy and a suspicious lesion on MRI, is a 12-core biopsy still necessary in addition to a targeted biopsy?

To evaluate the performance of multiparametric magnetic resonance imaging (mpMRI) in predicting prostate cancer on repeat biopsy; and to compare the cancer detection rates (CDRs) of MRI/transrectal ultrasonography (TRUS) fusion‐guided biopsy with standard 12‐core biopsy in men with at least one previous negative biopsy.

Multiparametric magnetic resonance imaging outperforms the Prostate Cancer Prevention Trial risk calculator in predicting clinically significant prostate cancer

The Prostate Cancer Prevention Trial risk calculator for high‐grade (PCPTHG) prostate cancer (CaP) was developed to improve the detection of clinically significant CaP. In this study, the authors compared the performance of the PCPTHG against multiparametric magnetic resonance imaging (MP‐MRI) in predicting men at risk of CaP.

Comparison of Multiparametric MRI Scoring Systems and the Impact on Cancer Detection in Patients Undergoing MR US Fusion Guided Prostate Biopsies

Introduction Multiple scoring systems have been proposed for prostate MRI reporting. We sought to review the clinical impact of the new Prostate Imaging Reporting and Data System v2 (PI-RADS) and compare those results to our proposed Simplified Qualitative System (SQS) score with respect to detection of prostate cancers and clinically significant prostate cancers. Methods All patients who underwent multiparametric prostate MRI (mpMRI) had their images interpreted using PI-RADS v1 and SQS score. PI-RADS v2 was calculated from prospectively collected data points. Patients with positive mpMRIs were then referred by their urologists for enrollment in an IRB-approved prospective phase III trial of mpMRI-Ultrasound (MR/TRUS) fusion biopsy of suspicious lesions. Standard 12-core biopsy was performed at the same setting. Clinical data were collected prospectively. Results 1060 patients were imaged using mpMRI at our institution during the study period. 341 participants were then referred to the trial. 312 participants underwent MR/TRUS fusion biopsy of 452 lesions and were included in the analysis. 202 participants had biopsy-proven cancer (64.7%) and 206 (45.6%) lesions were positive for cancer. Distribution of cancer detected at each score produced a Gaussian distribution for SQS while PI-RADS demonstrates a negatively skewed curve with 82.1% of cases being scored as a 4 or 5. Patient-level data demonstrated AUC of 0.702 (95% CI 0.65 to 0.73) for PI-RADS and 0.762 (95% CI 0.72 to 0.81) for SQS (p< 0.0001) with respect to the detection of prostate cancer. The analysis for clinically significant prostate cancer at a per lesion level resulted in an AUC of 0.725 (95% CI 0.69 to 0.76) and 0.829 (95% CI 0.79 to 0.87) for the PI-RADS and SQS score, respectively (p< 0.0001). Conclusions mpMRI is a useful tool in the workup of patients at risk for prostate cancer, and serves as a platform to guide further evaluation with MR/TRUS fusion biopsy. SQS score provided a more normal distribution of scores and yielded a higher AUC than PI-RADS v2. However until our findings are validated, we recommend reporting of detailed sequence-specific findings. This will allow for prospectively collected data to be utilized in determining the impact of ongoing changes to these scoring systems as our understanding of mpMRI interpretation evolves.

Reproducibility of Multiparametric Magnetic Resonance Imaging and Fusion Guided Prostate Biopsy: Multi-Institutional External Validation by a Propensity Score Matched Cohort

PURPOSE As the adoption of magnetic resonance imaging/ultrasound fusion guided biopsy expands, the reproducibility of outcomes at expert centers becomes essential. We sought to validate the comprehensive NCI (National Cancer Institute) experience with multiparametric magnetic resonance imaging and fusion guided biopsy in an external, independent, matched cohort of patients. MATERIALS AND METHODS We compared 620 patients enrolled in a prospective trial comparing systematic biopsy to fusion guided biopsy at NCI to 310 who underwent a similar procedure at Long Island Jewish Medical Center. The propensity score, defined as the probability of being treated outside NCI, was calculated using the estimated logistic regression model. Patients from the hospital were matched 1:1 for age, prostate specific antigen, magnetic resonance imaging suspicion score and prior negative biopsies. Clinically significant disease was defined as Gleason 3 + 4 or greater. RESULTS Before matching we found differences between the cohorts in age, magnetic resonance imaging suspicion score (each p <0.001), the number of patients with prior negative biopsies (p = 0.01), and the overall cancer detection rate and the cancer detection rate by fusion guided biopsy (each p <0.001). No difference was found in the rates of upgrading by fusion guided biopsy (p = 0.28) or upgrading to clinically significant disease (p = 0.95). A statistically significant difference remained in the overall cancer detection rate and the rate by fusion guided biopsy after matching. On subgroup analysis we found a difference in the overall cancer detection rate and the rate by fusion guided biopsy (p <0.001 and 0.003) in patients with prior negative systematic biopsy but no difference in the 2 rates (p = 0.39 and 0.51, respectively) in biopsy naïve patients. CONCLUSIONS Improved detection of clinically significant cancer by magnetic resonance imaging and fusion guided biopsy is reproducible by an experienced multidisciplinary team consisting of dedicated radiologists and urologists.

Risk stratification of prostate cancer utilizing apparent diffusion coefficient value and lesion volume on multiparametric MRI.

To evaluate the performance of apparent diffusion coefficient (ADC) and lesion volume in potentially risk‐stratifying patients with prostate cancer (PCa).

Multi-parametric MRI of the prostate: Factors predicting extracapsular extension at the time of radical prostatectomy

Objective Extracapsular extension (ECE) of prostate cancer is a poor prognostic factor associated with progression, recurrence after treatment, and increased prostate cancer-related mortality. Accurate staging prior to radical prostatectomy is crucial in avoidance of positive margins and when planning nerve-sparing procedures. Multi-parametric magnetic resonance imaging (mpMRI) of the prostate has shown promise in this regard, but is hampered by poor sensitivity. We sought to identify additional clinical variables associated with pathologic ECE and determine our institutional accuracy in the detection of ECE amongst patients who went on to radical prostatectomy. Methods mpMRI studies performed between the years 2012 and 2014 were cross-referenced with radical prostatectomy specimens. Predictive properties of ECE as well as additional clinical and biochemical variables to identify pathology-proven prostate cancer ECE were analyzed. Results The prevalence of ECE was 32.4%, and the overall accuracy of mpMRI for ECE was 84.1%. Overall mpMRI sensitivity, specificity, positive predictive value, and negative predictive value for detection of ECE were 58.3%, 97.8%, 93.3%, and 81.5%, respectively. Specific mpMRI characteristics predictive of pathologic ECE included primary lesion size ((20.73 ± 9.09) mm, mean ± SD, p < 0.001), T2 PIRADS score (p = 0.009), overall primary lesion score (p < 0.001), overall study suspicion score (p = 0.003), and MRI evidence of seminal vesicle invasion (SVI) (p = 0.001). Conclusion While mpMRI is an accurate preoperative assessment tool for the detection of ECE, its overall sensitivity is poor, likely related to the low detection rate of standard protocol MRI for microscopic extraprostatic disease. The additional mpMRI findings described may also be considered in surgical margin planning prior to radical prostatectomy.

Intraductal Carcinoma of the Prostate Diagnosed by Multi-Parametric Prostate Magnetic Resonance Imaging (MRI) and MRI/Ultrasound Fusion-Guided Biopsy

Intraductal carcinoma of the prostate (IDC-P) is an aggressive form of prostate cancer (CaP) with clinical and pathological features distinguishing it from high-grade prostatic intraepithelial neoplasia (HG-PIN). IDC-P is characterized by a high volume and high-grade disease, with an aggressive behavior. We present the case of a 63-year-old male with diagnostic MRI imaging indicative of IDC-P. To our knowledge, this is the first reported case of IDC-P identified with multi-parametric MRI.

Implementation of multiparametric magnetic resonance imaging technology for evaluation of patients with suspicion for prostate cancer in the clinical practice setting

To investigate the impact of implementing magnetic resonance imaging (MRI) and ultrasonography fusion technology on biopsy and prostate cancer (PCa) detection rates in men presenting with clinical suspicion for PCa in the clinical practice setting.

PD65-08 DISTINGUISHING LOW VERSUS HIGH RISK PROSTATE CANCER LESIONS USING RADIOMIC FEATURES DERIVED FROM MULTI-PARAMETRIC MAGNETIC RESONANCE IMAGING (MRI)

INTRODUCTION AND OBJECTIVES: Multi-parametric magnetic resonance imaging (mp-MRI) based prostate imaging reporting and data system (PIRADS) is limited in confidently and robustly distinguishing clinically significant and insignificant prostate cancer (PCa). Radiomic features employ image processing methods to characterize specific patterns in images and have been shown to better characterize PCa than mp-MRI signal intensities alone. For example, gradient features quantify the appearance of edges, Haralick features distinguish homogenous low intensity (PCa) from normal regions and Gabor features quantify appearance of PCa at multiple orientations and scales. In this study, we aim to identify which of the mp-MRI derived radiomic features can distinguish high and low risk PCa as defined by the D’Amico criteria. METHODS: A retrospective cohort of 452 PCa patients who underwent a 3 Tesla mp-MRI scan was considered for this study. A subset of 72 patients comprising 153 lesions was chosen chronologically based on PIRADS to obtain a statistically balanced cohort. D’Amico criteria were available for 83 lesions and was used to categorize into low (N1⁄4 26), intermediate (N 1⁄4 43) and high (N 1⁄4 14) risk groups. A balanced dataset of N 1⁄4 28 lesions with 14 lesions from each of high and low risk categories was finally assembled for radiomic feature analysis. RESULTS: A set of 101 radiomic features were extracted on a voxel-wise basis within the lesion region of interest (ROI) from each of T2w and ADC MRI sequences. First order statistics (mean, variance, skewness and kurtosis) were computed within each ROI to obtain 808 features per ROI. Of these, 44 features showed statistically significant differences between high and low risk lesions. Specifically, variance and skewness of T2w gradient and Gabor features, skewness and kurtosis of ADC Haralick and Laws features showed p<0.05 using Wilcoxon Rank-Sum test (representative results are shown in Figure). A random forests classifier trained using these radiomic features within a 3-fold cross validation framework resulted in an AUC of 0.96. CONCLUSIONS: Radiomic features derived from mp-MRI distinguish high and low risk prostate cancer lesions as defined by D’Amico criteria. An independent validation of these features is required on a separate test set.