Biparametric Magnetic Resonance Imaging Research Articles

Deep Learning Regression for Prostate Cancer Detection and Grading in Bi-Parametric MRI.

One of the most common types of cancer in men is prostate cancer (PCa). Biopsies guided by bi-parametric magnetic resonance imaging (MRI) can aid PCa diagnosis. Previous works have mostly focused on either detection or classification of PCa from MRI. In this work, however, we present a neural network that simultaneously detects and grades cancer tissue in an end-to-end fashion. This is more clinically relevant than the classification goal of the ProstateX-2 challenge. We used the dataset of this challenge for training and testing. We use a 2D U-Net with MRI slices as input and lesion segmentation maps that encode the Gleason Grade Group (GGG), a measure for cancer aggressiveness, as output. We propose a method for encoding the GGG in the model target that takes advantage of the fact that the classes are ordinal. Furthermore, we evaluate methods for incorporating prostate zone segmentations as prior information, and ensembling techniques. The model scored a voxel-wise weighted kappa of 0.446 ±0.082 and a Dice similarity coefficient for segmenting clinically significant cancer of 0.370 ±0.046, obtained using 5-fold cross-validation. The lesion-wise weighted kappa on the ProstateX-2 challenge test set was 0.13 ±0.27. We show that our proposed model target outperforms standard multiclass classification and multi-label ordinal regression. Additionally, we present a comparison of methods for further improvement of the model performance.

A Predictive Model Based on Bi-parametric Magnetic Resonance Imaging and Clinical Parameters for Clinically Significant Prostate Cancer in the Korean Population.

PurposeThis study aimed to develop and validate a predictive model for the assessment of clinically significant prostate cancer (csPCa) in men, prior to prostate biopsies, based on bi-parametric magnetic resonance imaging (bpMRI) and clinical parameters.Materials and MethodsWe retrospectively analyzed 300 men with clinical suspicion of prostate cancer (prostate-specific antigen [PSA] ≥ 4.0 ng/mL and/or abnormal findings in a digital rectal examination), who underwent bpMRI-ultrasound fusion transperineal targeted and systematic biopsies in the same session, at a Korean university hospital. Predictive models, based on Prostate Imaging Reporting and Data Systems scores of bpMRI and clinical parameters, were developed to detect csPCa (intermediate/high grade [Gleason score ≥ 3+4]) and compared by analyzing the areas under the curves and decision curves.ResultsA predictive model defined by the combination of bpMRI and clinical parameters (age, PSA density) showed high discriminatory power (area under the curve, 0.861) and resulted in a significant net benefit on decision curve analysis. Applying a probability threshold of 7.5%, 21.6% of men could avoid unnecessary prostate biopsy, while only 1.0% of significant prostate cancers were missed.ConclusionThis predictive model provided a reliable and measurable means of risk stratification of csPCa, with high discriminatory power and great net benefit. It could be a useful tool for clinical decision-making prior to prostate biopsies.

A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI.

BackgroundWe developed and validated an integrated radiomic-clinicopathologic nomogram (RadClip) for post-surgical biochemical recurrence free survival (bRFS) and adverse pathology (AP) prediction in men with prostate cancer (PCa). RadClip was further compared against extant prognostics tools like CAPRA and Decipher.MethodsA retrospective study of 198 patients with PCa from four institutions who underwent pre-operative 3 Tesla MRI followed by radical prostatectomy, between 2009 and 2017 with a median 35-month follow-up was performed. Radiomic features were extracted from prostate cancer regions on bi-parametric magnetic resonance imaging (bpMRI). Cox Proportional-Hazards (CPH) model warped with minimum redundancy maximum relevance (MRMR) feature selection was employed to select bpMRI radiomic features for bRFS prediction in the training set (D1, N = 71). In addition, a bpMRI radiomic risk score (RadS) and associated nomogram, RadClip, were constructed in D1 and then compared against the Decipher, pre-operative (CAPRA), and post-operative (CAPRA-S) nomograms for bRFS and AP prediction in the testing set (D2, N = 127).Findings“RadClip yielded a higher C-index (0.77, 95% CI 0.65-0.88) compared to CAPRA (0.68, 95% CI 0.57-0.8) and Decipher (0.51, 95% CI 0.33-0.69) and was found to be comparable to CAPRA-S (0.75, 95% CI 0.65-0.85). RadClip resulted in a higher AUC (0.71, 95% CI 0.62-0.81) for predicting AP compared to Decipher (0.66, 95% CI 0.56-0.77) and CAPRA (0.69, 95% CI 0.59-0.79).”InterpretationRadClip was more prognostic of bRFS and AP compared to Decipher and CAPRA. It could help pre-operatively identify PCa patients at low risk of biochemical recurrence and AP and who therefore might defer additional therapy.FundingThe National Institutes of Health, the U.S. Department of Veterans Affairs, and the Department of Defense.

A pilot study of radiomics signature based on biparametric MRI for preoperative prediction of extrathyroidal extension in papillary thyroid carcinoma.

To investigate efficiency of radiomics signature to preoperatively predict histological features of aggressive extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC) with biparametric magnetic resonance imaging findings. Sixty PTC patients with preoperative MR including T2WI and T2WI-fat-suppression (T2WI-FS) were retrospectively analyzed. Among them, 35 had ETE and 25 did not. Pre-contrast T2WI and T2WI-FS images depicting the largest section of tumor were selected. Tumor regions were manually segmented using ITK-SNAP software and 107 radiomics features were computed from the segmented regions using the open Pyradiomics package. Then, a random forest model was built to do classification in which the datasets were partitioned randomly 10 times to do training and testing with ratio of 1:1. Furthermore, forward greedy feature selection based on feature importance was adopted to reduce model overfitting. Classification accuracy was estimated on the test set using area under ROC curve (AUC). The model using T2WI-FS image features yields much higher performance than the model using T2WI features (AUC = 0.906 vs. 0.760 using 107 features). Among the top 10 important features of T2WI and T2WI-FS, there are 5 common features. After feature selection, the models trained using top 2 features of T2WI and the top 6 features of T2WI-FS achieve AUC 0.845 and 0.928, respectively. Combining features computed from T2WI and T2WI-FS, model performance decreases slightly (AUC = 0.882 based on all features and AUC = 0.913 based on top features after feature selection). Adjusting hyper parameters of the random forest model have negligible influence on the model performance with mean AUC = 0.907 for T2WI-FS images. Radiomics features based on pre-contrast T2WI and T2WI-FS is helpful to predict aggressive ETE in PTC. Particularly, the model trained using the optimally selected T2WI-FS image features yields the best classification performance. The most important features relate to lesion size and the texture heterogeneity of the tumor region.

Multicenter transperineal MRI-TRUS fusion guided outpatient clinic prostate biopsies under local anesthesia

IntroductionTransperineal Prostate biopsies (TPBx) are usually performed under general anesthesia without image fusion. This study aimed to evaluate prostate cancer (Pca) detection rates (CDR), pain, and adverse events using a novel, free-hand TPBx technique, based on elastic fusion of magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) under local anesthesia. Materials and methodsThis multicenter retrospective study included all consecutive patients scheduled for a TPBx. All had clinical suspicion of Pca, active surveillance scheduled for a re-biopsy, or suspicion of local recurrence after previous treatment. Bi-parametric or multiparametric MRI was performed in all patients and classified as positive in the case of Prostate Imaging-Reporting and Data System (PIRADS) suspicion ≥3. At least 1 targeted TPBx was realized from each PIRADS ≥3 index lesion. Six to 12 systematic random TPBx were done in patients with negative MRI. All biopsies were performed under local anesthesia in an outpatient clinic with MRI-TRUS fusion and the 3D navigation system Trinity Perine (Koelis, France). Any- and clinically significant Pca (csPca) (ISUP gr. ≥2) was recorded. Biopsy-related pain and adverse events were reported according to a visual analogue score of 0–10. ResultsIn total, 377 patients were included for analyses. The mean age was 67 years (95% Confidence Interval: 66–68) and the median prostate-specific antigen was 7.2 ng/ml (interquartile range [IQR] 4.8–11.0). MRI was negative in 6% and positive in 94%. The median MRI prostate volume was 43 ml (IQR 31–60) and the median MRI index tumor volume was 0.9 ml (IQR 0.5–2.1). The median number of TPBx was 4 (IQR 3–4). The overall detection of any- and csPca was 64% and 52%, respectively. The overall CDR according to PIRADS 3, 4, and 5 was 30%, 70%, and 94%, respectively. In patients with negative MRI, any- and csPca was detected in 23% and 9%, respectively. The median visual analogue score score was 2 (IQR 1–3, range 0–7). Two patients (0.5%) developed postbiopsy infection, of which one developed urosepsis. Treatment requiring haematuria or urinary retention did not occur. ConclusionFree-hand MRI/TRUS fusion-guided and systematic random TPBx in LA is a feasible, safe, and well-tolerated technique for diagnosing Pca.

Autonomous Detection and Classification of PI-RADS Lesions in an MRI Screening Population Incorporating Multicenter-Labeled Deep Learning and Biparametric Imaging: Proof of Concept.

Background: Opportunistic prostate cancer (PCa) screening is a controversial topic. Magnetic resonance imaging (MRI) has proven to detect prostate cancer with a high sensitivity and specificity, leading to the idea to perform an image-guided prostate cancer (PCa) screening; Methods: We evaluated a prospectively enrolled cohort of 49 healthy men participating in a dedicated image-guided PCa screening trial employing a biparametric MRI (bpMRI) protocol consisting of T2-weighted (T2w) and diffusion weighted imaging (DWI) sequences. Datasets were analyzed both by human readers and by a fully automated artificial intelligence (AI) software using deep learning (DL). Agreement between the algorithm and the reports—serving as the ground truth—was compared on a per-case and per-lesion level using metrics of diagnostic accuracy and k statistics; Results: The DL method yielded an 87% sensitivity (33/38) and 50% specificity (5/10) with a k of 0.42. 12/28 (43%) Prostate Imaging Reporting and Data System (PI-RADS) 3, 16/22 (73%) PI-RADS 4, and 5/5 (100%) PI-RADS 5 lesions were detected compared to the ground truth. Targeted biopsy revealed PCa in six participants, all correctly diagnosed by both the human readers and AI. Conclusions: The results of our study show that in our AI-assisted, image-guided prostate cancer screening the software solution was able to identify highly suspicious lesions and has the potential to effectively guide the targeted-biopsy workflow.

Diagnostic accuracy of bi-parametric magnetic resonance imaging in suspected prostate cancer: Correlation of scan results with biopsy findings in a series of 266 patients

Objectives: This study aimed to determine the diagnostic accuracy of bi-parametric magnetic resonance imaging (bpMRI) for clinically significant (CS) prostate cancer (PCa), and to assess the suitability of a new diagnostic pathway using bpMRI and prostate-specific antigen density (PSAd) to determine the need for biopsy. Methods: A total of 386 patients referred to one UK cancer centre with suspected PCa across 12 months from 2017 to 2018 underwent bpMRI, with a Prostate Imaging Reporting and Data System (PIRADS) score assigned. Of these, 266 (69%) were biopsied, with 150 CS-PCa (a Gleason score of 7 or a Gleason score of 3 with core length ⩾5 mm) detected: a 57% diagnostic yield. Imaging, PSAd and biopsy results were collated, and a confusion matrix was calculated. Results: Twenty-three men with PIRADS 1 were biopsied, with two CS-PCa detected: PSAd M=0.19 ( SD=0.07). Twenty-one men with PIRADS 2 lesions were biopsied, with one CS-PCa detected: PSAd was 0.28. Seventy-five men with PIRADS 3 were biopsied, with 25 CS-PCa detected: PSAd M=0.26 ( SD=0.16). Fifty-seven men with PIRADS 4 were biopsied, with 46 CS-PCa detected: PSAd M=0.26 ( SD=0.16). Ninety men with PIRADS 5 were biopsied, with 83 CS-PCa detected: PSAd M=0.55 ( SD=0.63). Among the 266 biopsied patients, a pathway offering biopsy if PIRADS is ⩾3 or PSAd ⩾0.1 spares 11 (4.2%) biopsies compared to baseline practice, with a sensitivity of 100% and a specificity of 10.1%, for biopsy-detected CS-PCa. The diagnostic yield is 61.8%. Conclusion: BpMRI is comparable to multi-parametric MRI for assessing need for biopsy in suspected PCa, albeit with lower specificity. A diagnostic pathway using bpMRI and PSAd can be safely used to avoid biopsy in men at low risk, increasing diagnostic yield of biopsy while reducing overdiagnosis and avoiding the risks and costs associated with gadolinium contrast. Level of evidence: Level 4.

Biparametric magnetic resonance imaging in the surveillance of testicular tumors following radical orchiectomy.

Computed tomography has been considered the preferred imaging modality for the surveillance of patients with testicular tumors (TTs) following radical orchiectomy. However, because of the concerns of frequent radiation exposure and intravenous iodinated contrast, biparametric magnetic resonance imaging (bpMRI) is a valid and safer alternative in the surveillance of patients with TT, instead of multiparametric magnetic resonance imaging. In this review article, we propose a protocol algorithm that utilizes bpMRI in the evaluation of patients after radical orchiectomy for TTs.

68Ga-PSMA-11-PET, 18F-PSMA-1007-PET and MRI for Gross Tumor Volume Delineation in Primary Prostate Cancer: Intermodality and Inter-tracer Variability

Abstract Purpose To assess the intermodality and intertracer variability of gallium-68 (68Ga)- or fluorine-18 (18F)-labeled prostate-specific membrane antigen (PSMA) positron emission tomography (PET) and biparametric magnetic resonance imaging (bpMRI)-based gross tumor volume (GTV) delineation for focal boosting in primary prostate cancer. Methods Nineteen prospectively enrolled patients with prostate cancer underwent a PSMA PET/MRI scan, divided into a 1:1 ratio between 68Ga-PSMA-11 and 18F-PSMA-1007, before radical prostatectomy (IWT140193). Four delineation teams performed manual contouring of the GTV based on bpMRI and PSMA PET imaging, separately. Index lesion coverage (overlap%) and interobserver variability were assessed. Furthermore, the distribution of the voxelwise normalized standardized uptake values (SUV%) was determined for the majority-voted (>50%) GTV (GTVmajority) and whole prostate gland to investigate intertracer variability. The median patientwise SUV% contrast ratio (SUV%-CR, calculated as median GTVmajority SUV% / median prostate gland without GTVmajority SUV%) was calculated according to the tracer used. Results A significant difference in overlap% favoring PSMA PET compared with bpMRI was found in the 18F subgroup (median, 63.0% vs 53.1%; P = .004) but was not present in the 68Ga subgroup (32.5% vs 50.6%; P = .100). Regarding interobserver variability, measured Sorensen-Dice coefficients (0.58 vs 0.72) and calculated mean distances to agreement (2.44 mm vs 1.22 mm) were statistically significantly lower and higher, respectively, for the 18F cohort compared with the 68Ga cohort. For the bpMRI-based delineations, the median Sorensen-Dice coefficient and mean distance to agreement were 0.63 and 1.76 mm, respectively. Median patientwise SUV%-CRs of 1.8 (interquartile range [IQR], 1.6-2.7) for 18F-PSMA and 3.3 (IQR, 2.7-5.9) for 68Ga-PSMA PET images were found. Conclusions Both MRI and PSMA PET provided consistent intraprostatic GTV lesion detection. However, the PSMA tracer seems to have a major influence on the contour characteristics, owing to an apparent difference in SUV% distribution in the prostate gland.

Prostate cancer local staging using biparametric MRI: assessment and comparison with multiparametric MRI

PurposeThe value of adding dynamic contrast-enhanced (DCE) imaging to T2-weighted (T2W) magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) to improve the detection and staging of prostate cancer (PCa) is unclear. The aim of this retrospective study was to compare the diagnostic performance of non-contrast biparametric MRI (bpMRI) with multiparametric MRI (mpMRI), for local staging of PCa. MethodsNinety-two patients who underwent prostate MRI on a 3-Tesla MRI system before radical prostatectomy for PCa were included retrospectively. Four readers independently assigned a Likert score (ranging from 1 to 5) for predicting extra-prostatic extension (EPE) on T2W + DWI (bpMRI) and then on T2W + DWI + DCE imaging (mpMRI). MRI-based staging results were compared with radical prostatectomy histology. A prediction of EPE generalized linear mixed model was used to assess the added-value of DCE and discriminative power of staging accuracy by area under the receiver-operating curve (AUC ROC). ResultsAUC was not significantly improved by DCE (mpMRI, AUC = 0.73 [95%CI: 0.655‒0.827] vs. bpMRI, AUC = 0.76 [95%CI: 0.681‒0.846]). After applying a selection procedure, only MRI criteria were retained in a multivariate model. The following criteria were significantly associated with local extension: localization in the peripheral zone (p < 0.001), maximal diameter of the lesion (<0.0001), curvilinear capsular contact on T2W (p < 0.0001), capsular irregularity on T2W (p < 0.0001), bulging on T2W (p < 0.001) and seminal vesicle hypo-signal (p < 0.001). ConclusionUse of bpMRI did not result in a decrease in local staging accuracy.

Use of Prostate Systematic and Targeted Biopsy on the Basis of Bi-Parametric Magnetic Resonance Imaging in Biopsy-Naïve Patients

Objectives To explore the performance of targeted biopsy (TB) in combination with systematic biopsy (SB) in the detection of prostate cancer (PCa) in biopsy naïve patients. Methods From May 2018 to January 2020, 230 biopsy-naïve men with suspicious bi-parametric MRI [bpMRI; Prostate Imaging Reporting and Data System (PI-RADS) score ≥3] were enrolled. All patients had prostate-specific antigen (PSA) levels of 20 ng/ml or less. For each patient, transrectal ultrasound-guided prostate biopsy was performed. The primary endpoint was the detection rate of CSPC [clinically-significant PCa, International Society of Urological Pathology grade group (ISUP GG) 2 or higher tumors]. The secondary endpoints were the detection rates of CIPC (clinically insignificant PCa, ISUP GG 1 tumors). Results CSPC was detected in 90 patients. Twelve (13.33%) of them were detected by TB only and 18 (20.00%) by SB only. Detection of CSPC by SB and TB did not differ significantly (p = .36). In 4.35% of 230 patients, CSPC would have been missed if we performed SB only, and in 6.09% of patients if we performed TB only. Moreover, combination of TB and SB did not increase the detection of CIPC. Conclusions No significant difference was found in the detection of CSPC between TB and SB; however, both techniques revealed substantial added value and combination of TB and SB could further improve this detection rate without increasing the detection of CIPC.

Combined Use of Prostate-specific Antigen Density and Magnetic Resonance Imaging for Prostate Biopsy Decision Planning: A Retrospective Multi-institutional Study Using the Prostate Magnetic Resonance Imaging Outcome Database (PROMOD)

BackgroundPrevious studies suggested that prostate-specific antigen (PSA) density (PSAd) combined with magnetic resonance imaging (MRI) may help avoid unnecessary prostate biopsy (PB) with a limited risk of missing clinically significant prostate cancer (csPCa; Gleason grade group [GGG] >1). ObjectiveTo define optimal diagnostic strategies based on the combined use of PSAd and MRI in patients at risk of prostate cancer (PCa). Design, setting, and participantsA retrospective analysis of the international multicenter Prostate MRI Outcome Database (PROMOD), including 2512 men having undergone PSAd and prostate MRI before PB between 2013 and 2019, was performed. Outcome measurements and statistical analysisRates of avoided PB, missed GGG 1, and csPCa according to 10 strategies based on PSAd values and MRI reporting scores (Prostate Imaging Reporting and Data System [PI-RADS]/Likert/IMPROD biparametric prostate MRI Likert). Decision curve analysis (DCA) was used to statistically compare the net benefit of each strategy. Combined systematic and targeted biopsies were used for reference. Results and limitationsAccording to DCA, the best strategy in biopsy-naive patients was #7 (PI-RADS/Likert 4–5 or PI-RADS/Likert 3 if PSAd >0.2), which avoided 41.2% PBs while missed 44% of GGG 1 and 10.9% of csPCa cases. From a clinical standpoint, however, strategies with a lower risk of missing csPCa included #10 (PI-RADS/Likert 4–5 or PI-RADS 3 if PSAd >0.10 or PSAd >0.2), which avoided 27% PBs while missing 24.4% GGG 1 and 4% csPCa cases, or #5 (PI-RADS/Likert 3-5 or PSAd>0.15), which avoided 14.7% PBs while missing 9.3% GGG 1 and 1.7% csPCa cases. Similar results were found in patients with a previous negative biopsy. This study is limited by its retrospective nature, and no central review of MRI and histopathological findings. ConclusionsCombined PSAd and MRI findings allows individualization of the decision to perform PB on the basis of the risk of missing PCa that both patients and clinicians are ready to accept to avoid this procedure. Patient summaryWe compared several biopsy strategies based on a combination of prostate magnetic resonance imaging findings and prostate-specific antigen density, providing a readily available tool for each center and practicing urologist to counsel patients about their individual risk of significant prostate cancer.

SC70 - Diagnostic accuracy of biparametric magnetic resonance imaging of the prostate (bp-MRI) in males who never underwent prostate biopsy: data after more than 350 patients enrolled

SC70 - Diagnostic accuracy of biparametric magnetic resonance imaging of the prostate (bp-MRI) in males who never underwent prostate biopsy: data after more than 350 patients enrolled

Added value of systematic biopsy in men with a clinical suspicion of prostate cancer undergoing biparametric MRI-targeted biopsy: multi-institutional external validation study

PurposeWe aimed to develop and externally validate a nomogram based on MRI volumetric parameters and clinical information for deciding when SBx should be performed in addition to TBx in man with suspicious prostate MRI.Materials and methodsRetrospective analyses of single (IMPROD, NCT01864135) and multi-institution (MULTI-IMPROD, NCT02241122) clinical trials. All men underwent a unique rapid biparametric magnetic resonance imaging (IMPROD bpMRI) consisting of T2-weighted imaging and three separate DWI acquisitions. Men with IMPROD bpMRI Likert scores of 3–5 were included. Logistic regression models were developed using IMPROD trial (n = 122) and validated using MULTI-IMPROD trial (n = 262) data. The model’s performance was evaluated in the terms of PCa detection with Gleason Grade Group 1 (clinically insignificant prostate cancer, iPCa) and > 1 (clinically significant prostate cancer, csPCa). Net benefits and decision curve analyses (DCA) were compared. Combined biopsies were used for reference.ResultsThe developed nomogram included age, PSA, prostate volume, MRI suspicion score (IMPROD bpMRI Likert or PIRADsv2.1 score), MRI-suspicion lesion volume percentage, and lesion location. All these variables were significant predictors of csPCa in SBx in multivariable analysis. In the validation cohort (n = 262) using different nomogram cutoffs, 19–43% of men would have avoided SBx while missing 1–4% of csPCa and avoiding detection of 9–20% of iPCa. Similar performance was found for nomograms using IMPROD bpMRI Likert score or v2.1.ConclusionsThe developed nomogram demonstrated potential to select men with a clinical suspicion of PCa who would benefit from performing SBx in addition to TBx. Public access to the nomogram is provided at:https://petiv.utu.fi/multiimprod/.

Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study

Background: Prostate cancer (PCa) influences its surrounding habitat, which tends to manifest as different phenotypic appearances on magnetic resonance imaging (MRI). This region surrounding the PCa lesion, or the peri-tumoral region, may encode useful information that can complement intra-tumoral information to enable better risk stratification. Purpose: To evaluate the role of peri-tumoral radiomic features on bi-parametric MRI (T2-weighted and Diffusion-weighted) to distinguish PCa risk categories as defined by D’Amico Risk Classification System. Materials and Methods: We studied a retrospective, HIPAA-compliant, 4-institution cohort of 231 PCa patients (n = 301 lesions) who underwent 3T multi-parametric MRI prior to biopsy. PCa regions of interest (ROIs) were delineated on MRI by experienced radiologists following which peri-tumoral ROIs were defined. Radiomic features were extracted within the intra- and peri-tumoral ROIs. Radiomic features differentiating low-risk from: (1) high-risk (L-vs.-H), and (2) (intermediate- and high-risk (L-vs.-I + H)) lesions were identified. Using a multi-institutional training cohort of 151 lesions (D1, N = 116 patients), machine learning classifiers were trained using peri- and intra-tumoral features individually and in combination. The remaining 150 lesions (D2, N = 115 patients) were used for independent hold-out validation and were evaluated using Receiver Operating Characteristic (ROC) analysis and compared with PI-RADS v2 scores. Results: Validation on D2 using peri-tumoral radiomics alone resulted in areas under the ROC curve (AUCs) of 0.84 and 0.73 for the L-vs.-H and L-vs.-I + H classifications, respectively. The best combination of intra- and peri-tumoral features resulted in AUCs of 0.87 and 0.75 for the L-vs.-H and L-vs.-I + H classifications, respectively. This combination improved the risk stratification results by 3–6% compared to intra-tumoral features alone. Our radiomics-based model resulted in a 53% accuracy in differentiating L-vs.-H compared to PI-RADS v2 (48%), on the validation set. Conclusion: Our findings suggest that peri-tumoral radiomic features derived from prostate bi-parametric MRI add independent predictive value to intra-tumoral radiomic features for PCa risk assessment.

Added Value of Biparametric MRI and TRUS-Guided Systematic Biopsies to Clinical Parameters in Predicting Adverse Pathology in Prostate Cancer.

ObjectiveTo develop novel models for predicting extracapsular extension (EPE), seminal vesicle invasion (SVI), or upgrading in prostate cancer (PCa) patients using clinical parameters, biparametric magnetic resonance imaging (bp-MRI), and transrectal ultrasonography (TRUS)-guided systematic biopsies.Patients and MethodsWe retrospectively collected data from PCa patients who underwent standard (12-core) systematic biopsy and radical prostatectomy. To develop predictive models, the following variables were included in multivariable logistic regression analyses: total prostate-specific antigen (TPSA), central transition zone volume (CTZV), prostate-specific antigen (PSAD), maximum diameter of the index lesion at bp-MRI, EPE at bp-MRI, SVI at bp-MRI, biopsy Gleason grade group, and number of positive biopsy cores. Three risk calculators were built based on the coefficients of the logit function. The area under the curve (AUC) was applied to determine the models with the highest discrimination. Decision curve analyses (DCAs) were performed to evaluate the net benefit of each risk calculator.ResultsA total of 222 patients were included in this study. Overall, 83 (37.4%), 75 (33.8%), and 107 (48.2%) patients had EPE, SVI, and upgrading at final pathology, respectively. The addition of bp-MRI data improved the discrimination of models for predicting SVI (0.807 vs 0.816) and upgrading (0.548 vs 0.625) but not EPE (0.766 vs 0.763). Similarly, models including clinical parameters, bp-MRI data, and information on systematic biopsies achieved the highest AUC in the prediction of EPE (0.842), SVI (0.913), and upgrading (0.794), and the three corresponding risk calculators yielded the highest net benefit.ConclusionWe developed three easy-to-use risk calculators for the prediction of adverse pathological features based on patient clinical parameters, bp-MRI data, and information on systematic biopsies. This may be greatly beneficial to urologists in the decision-making process for PCa patients.

PT006 - Biparametric magnetic resonance imaging findings combined with prostate specific antigen density is a reliable factor to diagnose clinically significant prostate cancer

PT006 - Biparametric magnetic resonance imaging findings combined with prostate specific antigen density is a reliable factor to diagnose clinically significant prostate cancer

Can the combination of biparametric magnetic resonance imaging and PSA-related indicators predict the prostate biopsy outcome?

To assess the value of biparametric magnetic resonance imaging (bpMRI) for detecting and ruling out prostate cancer in patients with elevated prostate-specific antigen (PSA). The basic information and bpMRI images of enrolled patients who took transperineal template saturate biopsy were retrospectively collected for analysis. Based on our results, we found that free/total PSA, and PI-RADS score were independent risk factors of PCa (p<.05), the PSA density, PI-RADS score were the independent risk factors of csPCa (p<.05). PI-RADS score threshold of 3 could achieve the highest Yonder index for predicting PCa, and PI-RADS score threshold of 4 could achieve the highest Yonder index for predicting csPCa. Therefore, we draw a conclusion that PI-RADSv2 score-based bpMRI could diminish the unnecessary prostate biopsies in patients with elevated PSA when combined with other PSA-related indicators.

Negative Predictive Value of Biparametric Prostate Magnetic Resonance Imaging in Excluding Significant Prostate Cancer: A Pooled Data Analysis Based on Clinical Data from Four Prospective, Registered Studies

BackgroundMultiparametric prostate magnetic resonance imaging (mpMRI) can be considered the gold standard in prostate magnetic resonance imaging (MRI). Biparametric prostate MRI (bpMRI) is faster and could be a feasible alternative to mpMRI. ObjectiveTo determine the negative predictive value (NPV) of Improved Prostate Cancer Diagnosis (IMPROD) bpMRI as a whole and in clinical subgroups in primary diagnostics of clinically significant prostate cancer (CSPCa). Design, setting, and participantsThis is a pooled data analysis of four prospective, registered clinical trials investigating prebiopsy IMPROD bpMRI. Men with a clinical suspicion of prostate cancer (PCa) were included. InterventionPrebiopsy IMPROD bpMRI was performed, and an IMPROD bpMRI Likert scoring system was used. If suspicious lesions (IMPROD bpMRI Likert score 3–5) were visible, targeted biopsies in addition to systematic biopsies were taken. Outcome measurements and statistical analysisPerformance measures of IMPROD bpMRI in CSPCa diagnostics were evaluated. NPV was also evaluated in clinical subgroups. Gleason grade ≥3 + 4 in any biopsy core taken was defined as CSPCa. Results and limitationsA total of 639 men were included in the analysis. The mean age was 64 yr, mean prostate-specific antigen level was 8.9 ng/ml, and CSPCa prevalence was 48%. NPVs of IMPROD bpMRI Likert scores 3–5 and 4–5 for CSPCa were 0.932 and 0.909, respectively, and the corresponding positive predictive values were 0.589 and 0.720. Only nine of 132 (7%) men with IMPROD bpMRI Likert score 1–2 had CSPCa and none with Gleason score >7. Thus, 132 of 639 (21%) study patients could have avoided biopsies without missing a single Gleason >7 cancer in the study biopsies. In the subgroup analysis, no clear outlier was present. The limitation is uncertainty of the true CSPCa prevalence. ConclusionsIMPROD bpMRI demonstrated a high NPV to rule out CSPCa. IMPROD bpMRI Likert score 1–2 excludes Gleason >7 PCa in the study biopsies. Patient summaryWe investigated the feasibility of prostate magnetic resonance imaging (MRI) with the Improved Prostate Cancer Diagnosis (IMPROD) biparametric MRI (bpMRI) protocol in excluding significant prostate cancer. In this study, highly aggressive prostate cancer was excluded using the publicly available IMPROD bpMRI protocol (http://petiv.utu.fi/multiimprod/).

Biparametric prostate MRI and clinical indicators predict clinically significant prostate cancer in men with “gray zone” PSA levels

PurposeTo predict clinically significant prostate cancer (cs-PCa) by combining the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) score based on biparametric magnetic resonance imaging (bp-MRI) and clinical indicators in men with prostate-specific antigen (PSA) levels in the gray zone of 4−10 ng/mL. MethodWe retrospectively analyzed 364 patients with elevated PSA levels in the gray zone who had pathologically confirmed disease and had undergone MRI examinations from January 2015 to October 2019; a training group (n = 255) and validation group (n = 109) were randomly established. Multivariate logistic regression analysis of the training group was performed to identify the independent predictors for cs-PCa, thereby establishing a predictive model that was evaluated in the training and validation groups by analyzing the receiver operating characteristic (ROC) curve. ResultsIn the training group, the PI-RADS v2 score and prostate volume (PV) were independent predictors of cs-PCa (P < 0.05). The prediction model comprising the PI-RADS v2 score and PV had a larger AUC than the other predictors alone in the training group. The diagnostic sensitivity and specificity of the prediction model were 84.1 % and 83.4 %, respectively. The prediction model was indicated to have better predictive performance in the validation group. ConclusionsThe prediction model exhibits a satisfactory predictive value for cs-PCa in men with PSA levels in the gray zone. PI-RADS v2 is the strongest univariate predictor for the detection of cs-PCa in men with PSA in the gray zone, but combining this with the PV can provide superior predictive ability.