Peripheral Zone Prostate Cancer Research Articles

Diagnostic Performance of Mass Enhancement on Dynamic Contrast-Enhanced MRI for Predicting Clinically Significant Peripheral Zone Prostate Cancer.

OBJECTIVE. Current criteria for positive findings on dynamic contrast-enhanced MRI (DCE-MRI) are unclear. We compared the diagnostic performance of mass enhancement on DCE-MRI versus conventional DCE-MRI criteria for identifying clinically significant prostate cancer (csPCa) in the peripheral zone (PZ). MATERIALS AND METHODS. A total of 173 consecutive patients with MRI- and surgically proven prostate cancer (PCa) were evaluated. Two readers independently interpreted DCE-MRI examinations of the PZ. Criteria denoting a positive DCE-MRI examination included conventional criteria from the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) and mass enhancement. The diagnostic performance of and interreader agreement for the two types of enhancement criteria in identifying csPCa in the PZ that met Epstein criteria were investigated. RESULTS. The proportion of csPCa in the PZ was 69.3% (120/173). For both readers, the specificity and positive predictive value of mass enhancement were increased compared with conventional enhancement criteria (specificity, 75.5% vs 5.7% [for reader 1] and 84.9% vs 30.2% [for reader 2], respectively; positive predictive value, 87.1% vs 70.6% [for reader 1] and 91.5% vs 75.3% [for reader 2], respectively). The AUC value of mass enhancement was higher than that of conventional criteria (for reader 1, 0.744 [95% CI, 0.672-0.807] vs 0.528 [95% CI, 0.451-0.605] [p < 0.001], respectively; for reader 2, 0.783 [95% CI, 0.714-0.842] vs 0.602 [95% CI, 0.497-0.700] [p < 0.001], respectively). The weighted kappa value for agreement between the two readers was 0.206 for conventional criteria and 0.613 for mass enhancement. CONCLUSION. PZ lesions with mass enhancement on DCE-MRI are more likely to be csPCa. This enhancement pattern may need to be considered as one of the criteria in PI-RADS.

Re: Diagnostic Value of CT in Detecting Peripheral Zone Prostate Cancer.

No AccessJournal of UrologyUrological Survey1 May 2020Re: Diagnostic Value of CT in Detecting Peripheral Zone Prostate Cancer Cary SiegelMD Cary SiegelCary Siegel More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000766AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "Re: Diagnostic Value of CT in Detecting Peripheral Zone Prostate Cancer." The Journal of Urology, 203(5), p. 861 © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue 5May 2020Page: 861-861 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Cary Siegel More articles by this author Expand All Advertisement PDF DownloadLoading ...

The performance of intravoxel-incoherent motion diffusion-weighted imaging derived hypoxia for the risk stratification of prostate cancer in peripheral zone

PurposeTo assess the association between intravoxel-incoherent motion diffusion-weighted imaging (IVIM) derived hypoxia and the aggressiveness of prostate cancer (PCa) and to explore its contribution to the risk stratification of PCa. MethodsSeventy-five peripheral zone (PZ) PCa patients, who underwent multiparametric MRI (mpMRI), were included in this study. Systematic ultrasound guided biopsy was used as reference. IVIM was acquired with 5 b values (b = 0∼750 s/mm2). Apparent diffusion coefficient (ADC), pure tissue diffusion (Ds), volume fraction of pseudo-diffusion (fp), hypoxic fraction (HFDWI), hypoxia score (HSDWI) and relative oxygen saturation(rOSDWI), were calculated and histogram analysis was applied. Groups comparison was performed between low-intermediate-grade group (LG, the International Society of Urological Pathology (ISUP) Gleason Grade (GG) ≤2) and high-grade (HG, ISUP GG ≥ 3) group. The correlation between diffusion parameters and ISUP GG was assessed. Cross-validated Support Vector Machine (SVM) Classification was performed and compared with univariate ROC analysis to explore the risk stratification of PZ PCa. ResultsMean, median, and the 10th percentile of Ds differed significantly between groups (p < 0.05). Several parameters significantly correlated with ISUP grade, and the 10th percentile of Ds showed the strongest correlation (ρ= − 0.284). The prediction model containing IVIM derived hypoxia yielded an area under the receiver operating characteristics curve (AUC) ranging 0.749–0.786 for cross-validation. The AUCs of the SVM modeling were higher than that of any single parameter. ConclusionIVIM derived hypoxia demonstrated significant correlation with the aggressiveness of PCa. It’s supplemental to the MRI assessment of PCa with a promising stratification of risk stratification of PZ PCa.

Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer

ObjectivesTo create a radiomics approach based on multiparametric magnetic resonance imaging (mpMRI) features extracted from an auto-fixed volume of interest (VOI) that quantifies the phenotype of clinically significant (CS) peripheral zone (PZ) prostate cancer (PCa).MethodsThis study included 206 patients with 262 prospectively called mpMRI prostate imaging reporting and data system 3–5 PZ lesions. Gleason scores > 6 were defined as CS PCa. Features were extracted with an auto-fixed 12-mm spherical VOI placed around a pin point in each lesion. The value of dynamic contrast-enhanced imaging(DCE), multivariate feature selection and extreme gradient boosting (XGB) vs. univariate feature selection and random forest (RF), expert-based feature pre-selection, and the addition of image filters was investigated using the training (171 lesions) and test (91 lesions) datasets.ResultsThe best model with features from T2-weighted (T2-w) + diffusion-weighted imaging (DWI) + DCE had an area under the curve (AUC) of 0.870 (95% CI 0.980–0.754). Removal of DCE features decreased AUC to 0.816 (95% CI 0.920–0.710), although not significantly (p = 0.119). Multivariate and XGB outperformed univariate and RF (p = 0.028). Expert-based feature pre-selection and image filters had no significant contribution.ConclusionsThe phenotype of CS PZ PCa lesions can be quantified using a radiomics approach based on features extracted from T2-w + DWI using an auto-fixed VOI. Although DCE features improve diagnostic performance, this is not statistically significant. Multivariate feature selection and XGB should be preferred over univariate feature selection and RF. The developed model may be a valuable addition to traditional visual assessment in diagnosing CS PZ PCa.Key Points• T2-weighted and diffusion-weighted imaging features are essential components of a radiomics model for clinically significant prostate cancer; addition of dynamic contrast-enhanced imaging does not significantly improve diagnostic performance.• Multivariate feature selection and extreme gradient outperform univariate feature selection and random forest.• The developed radiomics model that extracts multiparametric MRI features with an auto-fixed volume of interest may be a valuable addition to visual assessment in diagnosing clinically significant prostate cancer.

The Utility of Diffusion-Weighted Imaging and Perfusion Magnetic Resonance Imaging Parameters for Detecting Clinically Significant Prostate Cancer

IntroductionTo establish the diagnostic performance of the parameters obtained from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging at 3T in discriminating between non-clinically significant prostate cancers (ncsPCa, Gleason score [GS] < 7) and clinically significant prostate cancers (csPCa, GS ≥ 7) in the peripheral zone. Materials and MethodsTwenty-six male patients with peripheral zone prostate cancer (PCa) who had undergone 3T multiparametric magnetic resonance imaging (MRI) scan prior to biopsy were included in the study and evaluated retrospectively. The GS was obtained by both standard 12-core transrectal ultrasound guided biopsy and targeted MRI-US fusion biopsy and then confirmed by prostatectomy, if available. For each confirmed tumour focus, DCE-derived quantitative perfusion metrics (Ktrans, Kep, Ve, initial area under the curve [AUC]), the apparent diffusion coefficient (ADC) value, and normalized versions of quantitative metrics were measured and correlated with the GS. ResultsKtrans had the highest diagnostic accuracy value of 82% among the DCE-MRI parameters (AUC 0.90), and ADC had the strongest diagnostic accuracy value of 87% among the overall parameters (AUC 0.92). The combination of ADC and Ktrans have higher diagnostic performance with the area under the receiver operating characteristic curve being 0.98 (sensitivity 0.94; specificity 0.89; accuracy 0.92) compared to the individual evaluation of each parameter alone.The GS showed strong negative correlations with ADC (r = −0.72) and normalized ADC (r = −0.69) as well as a significant positive correlation with Ktrans (r = 0.69). ConclusionThe combination of Ktrans and ADC and their normalized versions may help differentiate between ncsPCa from csPCa in the peripheral zone.

Dynamic contrast-enhanced (DCE) MR imaging: the role of qualitative and quantitative parameters for evaluating prostate tumors stratified by Gleason score and PI-RADS v2.

To investigate the role of qualitative and quantitative DCE-MRI parameters in prostate cancer (PCa) stratified by whole-mount histopathology (WMHP) Gleason score (GS) and PI-RADSv2. This retrospective study included 323 PCa tumors in 254 men, who underwent 3T MRI prior to prostatectomy, 7/2009-12/2016. Qualitative DCE curve types included type 1 (progressive), type 2 (plateau) and type 3 (washout). Quantitative DCE-MRI pharmacokinetic (PK) parameters included Ktrans (influx volume transfer coefficient), Kep (efflux reflux rate constant) and iAUC (initial area under the curve). DCE-MRI features of true positive lesions were evaluated for overall, index, transition zone (TZ) and peripheral zone (PZ), based on GS grade (low = 6, high > 6) and PI-RADSv2 score using SPSSv24. There were 57 (17.6%) low-grade and 266 (82.4%) high-grade PCa lesions. PI-RADSv2 3, 4 and 5 included 106, 120 and 97 lesions, respectively. 251 (77.7%) and 72 (22.3%) lesions were located in PZ and TZ, respectively. High-grade lesions had significantly higher proportion of Type 3 curves compared to low-grade lesions in overall (70.3% vs. 54.4%) and TZ (73.5% vs. 43.5%). As PI-RADSv2 increased, the proportion of type 3 curve significantly increased for overall (80.4-51.9%), index (80.4-54.7%) and PZ (78.7-52.1%) lesions. Among PK parameters, Ktrans (0.43 vs 0.32) and iAUC (8.99 vs 6.9) for overall PCa, Ktrans (0.43 vs 0.31) and iAUC (9 vs 6.67) for PZ PCa, and iAUC (8.94 vs 7.42) for index PCa were significantly higher for high-grade versus low-grade lesions. Also, Ktrans (0.51-0.34), Kep (1.75-1.29) and iAUC (9.79-7.6) for overall PCa, Ktrans (0.53-0.32), Kep (1.81-1.26) and iAUC (9.83-7.34) for PZ PCa; and Kep (1.79-1.17) and iAUC (11.3-8.45) for index PCa increased significantly with a higher PI-RADSv2 score. The results of study show the possible utility of qualitative and quantitative DCE-MRI parameters for assessment of PCa GS and PI-RADSv2 categorization.

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method.

Accurate detection and localization of prostate cancer (PCa) in men undergoing prostate MRI is a fundamental step for future targeted prostate biopsies and treatment planning. Fully automated localization of peripheral zone (PZ) PCa using the apparent diffusion coefficient (ADC) map might be clinically useful. To describe automated localization of PCa in the PZ on ADC map MR images using an ensemble U-Net-based model. Retrospective, case-control. In all, 226 patients (154 and 72 patients with and without clinically significant PZ PCa, respectively), training, and testing was performed using dataset images of 146 and 80 patients, respectively. 3T, ADC maps. ADC map. The ground truth was established by manual delineation of the prostate and prostate PZ tumors on ADC maps by dedicated radiologists using MRI-radical prostatectomy maps as a reference standard. Statistical Tests: Performance of the ensemble model was evaluated using Dice similarity coefficient (DSC), sensitivity, and specificity metrics on a per-slice basis. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were employed as well. The paired t-test was used to test the differences between the performances of constituent networks of the ensemble model. Our developed algorithm yielded DSC, sensitivity, and specificity of 86.72% ± 9.93%, 85.76% ± 23.33%, and 76.44% ± 23.70%, respectively (mean ± standard deviation) on 80 test cases consisting of 41 and 39 instances from patients with and without clinically significant tumors including 660 extracted 2D slices. AUC was reported as 0.779. An ensemble U-Net-based approach can accurately detect and segment PCa in the PZ from ADC map MR prostate images. 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1223-1234.

Differentiation of prostatitis and clinically significant prostate cancer in peripheral zone using the prostate imaging-reporting and data system

Objective: To determine whether clinically significant prostate cancer (PCa) and prostatitis in the peripheral zone can be distinguished using prostate imaging reporting and data system version 1 (PI-RADS V1) and version 2(PI-RADS V2). Methods: Between September 2010 and August 2016, mpMRI data of 77 patients with PCa and 29 prostatitis obtained at 3.0 T were collected in Zhangjiagang Hospital Affiliated to Soochow University. Every lesion was scored according to PI-RADS (V1 and V2), as well as a sum score and a PI-RADS V2 score. The non-parametric Kruskal-Wallis test was used to assess differences between PCa and prostatitis regarding PS3, PS4 and PI-RADS V2 score. The diagnostic performance of PI-RADS V1 and V2 for detection of prostatitis in peripheral zone was compared by analyzing ROC curve. Results: The PI-RADS V1 score for PS3, PS4 and the PI-RADS V2-score were all significantly higher for PCa (PS3:12.1±2.1; PS4:16.2±2.9; V2:4.6±0.8) than for prostatitis (PS3:8.0±0.7; PS4:10.6±1.0; V2:3.0±0.5) (all P<0.01). Of these parameters, PS4 achieved the highest predictive value for the presence of prostatitis with an AUC of 0.937, sensitivity and specificity were 87.0%, 97.0% with a threshold of 12.5. Conclusion: Prostatitis can be differentiated from clinically significant PCa in peripheral zone on mpMRI using PI-RADS system, PS4 achieved better results compared to PS3 and V2.

Computer-aided diagnosis system for characterizing ISUP grade ≥ 2 prostate cancers at multiparametric MRI: A cross-vendor evaluation

PurposeTo assess the performance of a computer-aided diagnosis (CADx) system trained at characterizing International Society of Urological Pathology (ISUP) grade≥2 peripheral zone (PZ) prostate cancers on multiparametric magnetic resonance imaging (mpMRI) examinations from a different institution and acquired on different scanners than those used for the training database. Patients and methodsPreoperative mpMRIs of 74 men (median age, 65.7 years) treated by prostatectomy between 2014 and 2017 were retrospectively selected. One radiologist outlined suspicious lesions and scored them using Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2); their CADx score was calculated using a classifier trained on an independent database of 106 patients treated by prostatectomy in another institution. The lesions’ nature was assessed by comparison with prostatectomy whole-mounts. Diagnostic accuracy was estimated with areas under receiver operating characteristic curves (AUCs). Sensitivity and specificity were calculated using a CADx threshold (≥0.21) that yielded 95% sensitivity in the training database, and a PI-RADSv2≥3 threshold. ResultsA total of 127 lesions (PZ, n=104; transition zone [TZ], n=23) were described. In PZ, CADx and PI-RADSv2 scores had similar AUCs for characterizing ISUP grade≥2 cancers (0.78 [95% confidence interval (CI): 0.69–0.87] vs. 0.74 [95%CI: 0.62–0.82], respectively) (P=0.59). Sensitivity and specificity were respectively 89% (95%CI: 82–97%) and 42% (95%CI: 26–58%) for the CADx score, and 97% (95%CI: 93–100%) and 37% (95%CI: 22–52%) for the PI-RADSv2 score. In TZ, both scores showed poor specificity. ConclusionIn this external cohort, the CADx and PI-RADSv2 scores showed similar performances in characterizing ISUP grade≥2 cancers.

PI-RADS version 2: optimal time range for determining positivity of dynamic contrast-enhanced MRI in peripheral zone prostate cancer

To analyse the optimal time cut-off for determining positivity of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in peripheral zone (PZ) prostate cancer (PCa). A consecutive series of 89 patients with PZ PCa who had undergone diffusion-weighted imaging (DWI) and subtraction DCE MRI were included. An experienced reader visually analysed the earliest time after contrast medium injection to visualise the best contrast between an index tumour and normal PZ on DCE MRI (i.e., best contrast time). The best contrast time cut-off for clinically significant cancer (csPCa) according to Epstein criteria or International Society of Urological Pathology (ISUP) grade ≥2 was analysed by an experienced reader, and applied to a less-experienced reader. For the index lesion of DWI category 3, the added value of DCE MRI (increased true positive and negative rates of PI-RADSv2 for csPCa) was evaluated using the cut-off time. The best contrast time cut-off for csPCa was ≤72 seconds for Epstein criteria and ≤56 seconds for ISUP grade ≥2 by an experienced reader. The weighted kappa to determine positivity of DCE MRI was 0.622 for ≤72 seconds and 0.527 for ≤56 seconds between the two readers. The added value of DCE MRI was 55-75% by an experienced reader and 39.1-69.6% by a less-experienced reader. For interpreting PI-RADSv2, imaging findings within 60-72 seconds following contrast media injection seem to reliably determine positivity of DCE MRI in PZ, and have added value for detecting csPCa.

Diagnostic Value of CT in Detecting Peripheral Zone Prostate Cancer.

OBJECTIVE. The purpose of this study was to identify the sensitivity of contrast-enhanced CT in detecting high-grade prostate adenocarcinoma. MATERIALS AND METHODS. A retrospective analysis included 100 patients with prostate cancer proven by biopsy between January 2010 and December 2017 who underwent staging CT of the abdomen and pelvis within 3 months of diagnosis. The control subjects were 100 randomly selected aged-matched male outpatients with no known history of malignancy who underwent contrast-enhanced CT of the abdomen and pelvis in the same time period as the patients with cancer. Two readers, blinded to both groups, independently assessed the likelihood of prostate cancer on the basis of the CT finding of focal abnormally increased peripheral enhancement in the prostate. Binary classification of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) was used to assess the diagnostic utility of CT versus the reference standard of transrectal ultrasound-guided biopsy. RESULTS. Eighty-three of 100 patients with biopsy-proven prostate cancer and 92 of 100 control subjects were correctly identified (sensitivity, 0.83; specificity, 0.92; PPV, 0.91; NPV, 0.84). There was no significant difference in diagnostic accuracy among subjects with different Gleason scores. Interrater agreement on both the cancer and control patients was 0.76 as assessed by Cohen kappa statistic. CONCLUSION. Incidental detection of a focal area of increased enhancement in the periphery of the prostate at contrast-enhanced CT may represent a clinically significant cancer and deserves further workup with prostate-specific antigen measurement and correlation with clinical risk factors for prostate cancer.

Targeted Biopsy Validation of Peripheral Zone Prostate Cancer Characterization With Magnetic Resonance Fingerprinting and Diffusion Mapping.

This study aims for targeted biopsy validation of magnetic resonance fingerprinting (MRF) and diffusion mapping for characterizing peripheral zone (PZ) prostate cancer and noncancers. One hundred four PZ lesions in 85 patients who underwent magnetic resonance imaging were retrospectively analyzed with apparent diffusion coefficient (ADC) mapping, MRF, and targeted biopsy (cognitive or in-gantry). A radiologist blinded to pathology drew regions of interest on targeted lesions and visually normal peripheral zone on MRF and ADC maps. Mean T1, T2, and ADC were analyzed using linear mixed models. Generalized estimating equations logistic regression analyses were used to evaluate T1 and T2 relaxometry combined with ADC in differentiating pathologic groups. Targeted biopsy revealed 63 cancers (low-grade cancer/Gleason score 6 = 10, clinically significant cancer/Gleason score ≥7 = 53), 15 prostatitis, and 26 negative biopsies. Prostate cancer T1, T2, and ADC (mean ± SD, 1660 ± 270 milliseconds, 56 ± 20 milliseconds, 0.70 × 10 ± 0.24 × 10 mm/s) were significantly lower than prostatitis (mean ± SD, 1730 ± 350 milliseconds, 77 ± 36 milliseconds, 1.00 × 10 ± 0.30 × 10 mm/s) and negative biopsies (mean ± SD, 1810 ± 250 milliseconds, 71 ± 37 milliseconds, 1.00 × 10 ± 0.33 × 10 mm/s). For cancer versus prostatitis, ADC was sensitive and T2 specific with comparable area under curve (AUC; (AUCT2 = 0.71, AUCADC = 0.79, difference between AUCs not significant P = 0.37). T1 + ADC (AUCT1 + ADC = 0.83) provided the best separation between cancer and negative biopsies. Low-grade cancer T2 and ADC (mean ± SD, 75 ± 29 milliseconds, 0.96 × 10 ± 0.34 × 10 mm/s) were significantly higher than clinically significant cancers (mean ± SD, 52 ± 16 milliseconds, 0.65 ± 0.18 × 10 mm/s), and T2 + ADC (AUCT2 + ADC = 0.91) provided the best separation. T1 and T2 relaxometry combined with ADC mapping may be useful for quantitative characterization of prostate cancer grades and differentiating cancer from noncancers for PZ lesions seen on T2-weighted images.

Combination of prostate imaging reporting and data system with the apparent diffusion coefficient map for the diagnosis of peripheral zone prostate cancer

To explore the value of prostate imaging reporting and data system version 2 (PI-RADS V2) combined with quantitative parameters derived from apparent diffusion coefficient (ADC) map in the diagnosis of peripheral zone prostate cancer. Methods: A total of 50 patients who underwent prostate multiparametric MRI (mpMRI) with suspicious peripheral nodules were retrospectively enrolled, and all patients were biopsy-proven histologically. Two radiologists analyzed the position and category of peripheral zone lesions based on PI-RADS V2. Then 12 ADC quantitative parameters were calculated regarding each lesion on the ADC map by post-processing software. The lesions were divided into malignant group and benign group according to histopathological findings. The ADC quantitative parameters between groups were compared, and stepwise logistic regression analysis was used to build a discriminative model. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were performed to evaluate the diagnostic power and clinical benefit. Results: Twenty-eight peripheral zone prostate malignant lesions and 25 benign lesions were obtained finally. The area under the ROC curve, sensitivity and specificity to differentiate peripheral zone prostate malignant from benign lesions were as follows: 0.803, 60.71%, 92.00% (PI-RADS V2 score), 0.857, 89.29%, 76.00% (ADC model), and 0.891, 71.43%, 92.00% (combined model), respectively. The discriminative power of the combined model was significantly improved compared with PI-RADS V2 score (P=0.012). The combined model had relatively optimal overall net benefit, which outperformed the PI-RADS V2 score when threshold probability varied in the range of 0.05-0.27 and 0.46-0.81. Conclusion: PI-RADS V2 combined with quantitative analysis of ADC map improve the power in discriminating peripheral zone prostate cancer from benign lesions, and the clinical benefit as well.

Compressed Sensing Radial Sampling MRI of Prostate Perfusion: Utility for Detection of Prostate Cancer.

Purpose To investigate the diagnostic performance of a dual-parameter approach by combining either volumetric interpolated breath-hold examination (VIBE)- or golden-angle radial sparse parallel (GRASP)-derived dynamic contrast agent-enhanced (DCE) MRI with established diffusion-weighted imaging (DWI) compared with traditional single-parameter evaluations on the basis of DWI alone. Materials and Methods Ninety-four male participants (66 years ± 7 [standard deviation]) were prospectively evaluated at 3.0-T MRI for clinical suspicion of prostate cancer. Included were 101 peripheral zone prostate cancer lesions. Histopathologic confirmation at MRI transrectal US fusion biopsy was matched with normal contralateral prostate parenchyma. MRI was performed with diffusion weighting and DCE by using GRASP (temporal resolution, 2.5 seconds) or VIBE (temporal resolution, 10 seconds). Perfusion (influx forward volume transfer constant [Ktrans] and rate constant [Kep]) and apparent diffusion coefficient (ADC) parameters were determined by tumor volume analysis. Areas under the receiver operating characteristic curve were compared for both sequences. Results Evaluated were 101 prostate cancer lesions (GRASP, 61 lesions; VIBE, 40 lesions). In a combined analysis, diffusion and perfusion parameters ADC with Ktrans or Kep acquired with GRASP had higher diagnostic performance compared with diffusion characteristics alone (area under the curve, 0.97 ± 0.02 [standard error] vs 0.93 ± 0.03; P < .006 and .021, respectively), whereas ADC with perfusion parameters acquired with VIBE had no additional benefit (area under the curve, 0.94 ± 0.03 vs 0.93 ± 0.04; P = .18and .50, respectively, for combination of ADC with Ktrans and Kep). Conclusion If used in a dual-parameter model, incorporating diffusion and perfusion characteristics, the golden-angle radial sparse parallel acquisition technique improves the diagnostic performance of multiparametric MRI examinations of the prostate. This effect could not be observed combining diffusing with perfusion parameters acquired with volumetric interpolated breath-hold examination. © RSNA, 2018.

Characterization of prostate cancer using diffusion tensor imaging: A new perspective

PurposeThis study is aimed at evaluating the potential role of quantitative magnetic resonance diffusion tensor imaging (DTI) and tractography parameters in the detection and characterization of peripheral zone prostate cancer with a particular attention for fiber tract density. Materials and methodsDTI was acquired from eleven high risk, transrectal ultrasound (TRUS)-guided biopsy proven prostate cancers with perineural invasion (histological Gleason score ≥ 7) on a 3 T magnet. Twenty parameters derived from DTI were quantified in cancer and healthy regions of the prostate. In addition, fiber tract density in normal versus cancer tissues was also calculated using DTI tractography. Support vector machine with a radial basis function kernel and area under receiver operator characteristic (ROC) were used to describe and compare the diagnostic performance of combined fractional anisotropy (FA) and mean diffusivity (MD) and other statistically significant DTI parameters. Spearman correlation analysis between DTI parameters and Gleason scores was conducted. ResultsEighteen DTI parameters yielded statistically significant differences between cancer and healthy regions (p-value < 0.05). The ROC curve of all statistically significant DTI parameters between cancer and healthy regions was higher than the area under ROC curve using FA + MD alone (95% confidence interval = 0.988, range = 0.975–1.00) vs (95% confidence interval = 0.935, range = 0.898-0.999), respectively (p-value < 0.05). Fiber tract density was also found to be higher in cancer than in healthy tissues (+38.22%, p-value = 0.010) and may be related to the increase in nerve and vascular density reported in prostate cancer. The linear and relative anisotropy were highly correlated with Gleason score (Spearman correlation factor r = 0.655, p-value = 0.001 and r = 0.667, p-value < 0.001, respectively). ConclusionsDTI has the potential to provide imaging biomarkers in the detection and characterization of prostate cancer. Novel quantitative parameters derived from DTI and DTI tractography, including fiber tract density, support the use of DTI in the assessment of high grade prostate cancer.

Diagnostic performance of diffusion-weighted imaging for prostate cancer: Peripheral zone versus transition zone.

ObjectivesDiffusion-weighted imaging (DWI) has been shown to be an important component of multiparametric magnetic resonance imaging (mpMRI). We compared performance of DWI for detection of prostate cancer (PCa) in peripheral zone (PZ) and transition zone (TZ) of prostate.Materials and methodsWe reviewed data of 460 subjects who underwent preoperative 3.0-Tesla mpMRI and subsequently radical prostatectomy. Level of suspicion for PCa was graded using 5-grade Likert-scale from DWI. Topographic analyses were performed for location of tumor foci at each surgical specimen. Among those with DWI grade ≥ III, we analyzed concordance rate on the location of radiologic and pathologic index lesions between DWI and surgical specimens.ResultsAmong 460 patients, 351 (76.3%) patients showed suspicious DWI lesions (57.5% in PZ, 42.5% in TZ). Multivariates regression analyses revealed significant associations between high DWI grade and adverse pathologic outcomes including pathologic stage, Gleason score, tumor volume and extracapsular extension (all p < 0.05). Overall concordance rates between DWI and surgical specimen were 75.8%, significantly higher in PZ than TZ (82.2% vs. 67.1% p = 0.002). Such concordance rate showed a positive linear association with increase in DWI grading (p < 0.001). Among 109 patients with DWI grade I-II, 28 (25.7%) harbored high grade disease (pathologic Gleason score ≥ 4 + 3).ConclusionsDWI detects tumors in PZ of prostate more accurately than those in TZ. Such accuracy of DWI was shown to be more evident with higher DWI grade. Meanwhile, a negative DWI did not guarantee absence of high grade PCa.

Utility of quantitative apparent diffusion coefficient measurements and normalized apparent diffusion coefficient ratios in the diagnosis of clinically significant peripheral zone prostate cancer.

The aim of this study is to evaluate the utility of quantitative apparent diffusion coefficient(ADC) measurements and normalized ADC ratios in multiparametric MRI for the diagnosis of clinically significant peripheral zone (PZ) prostate cancer particularly among equivocally suspicious prostate lesions. A retrospective analysis of 95 patients with PZ lesions by PI-RADSv2 criteria, and who underwent subsequent MRI-US fusion biopsy, was approved by an institutional review board. Two radiologists independently measured ADC values in regions of interest(ROIs) of PZ lesions and calculated normalized ADC ratio based on ROIs in the bladder lumen. Diagnostic performance was evaluated using ROC. Inter observer variability was assessed using intraclass correlation coefficient (ICC). Mean ADC and normalized ADC ratios for clinically significant and non-clinically significant lesions were 0.763 × 10-3 mm2s-1, 29.8%; and 1.135 × 10-3 mm2s-1, 47.2% (p < 0.001), respectively. Area under the ROC curve (AUC) was 0.880 [95% CI (0.816-0.944) and 0.885 (95% CI (0.814-0.955)] for ADC and ADC ratio, respectively. Optimal AUC threshold for ADC was 0.843 × 10-3 mm2s-1 (Sn 70.5%, Sp 88.2%) and for normalized ADC was 33.1% (Sn 75.0%, Sp 95.7%). intraclass correlation coefficient was high at 0.889. Quantitative ADC measurement in PZ prostate lesions demonstrates excellent diagnostic performance in differentiating clinically significant from non-clinically significant prostate cancer with high inter observer correlation. Advances In knowledge: Quantitative ADC is presented as an additional method to evaluate lesions in mpMRI of the prostate. This technique may be incorporated in new and existing methods to improve detection and discrimination of clinically significant prostate cancer.

3T multiparametric MR imaging, PIRADSv2-based detection of index prostate cancer lesions in the transition zone and the peripheral zone using whole mount histopathology as reference standard.

To evaluate 3T mpMRI characteristics of transition zone and peripheral zone index prostate cancer lesions stratified by Gleason Score and PI-RADSv2 with whole mount histopathology correlation. An institution review board-approved, HIPAA-compliant single-arm observational study of 425 consecutive men with 3T mpMRI prior to radical prostatectomy from December 2009 to October 2016 was performed. A genitourinary radiologist and a genitourinary pathologist matched all lesions detected on whole mount histopathology with lesions concordant for size and location on 3T mpMRI. Differences in clinical, MRI parameters, and histopathology between transition zone and peripheral zone were determined and analyzed with χ2 and Mann-Whitney U test. AUC was measured. 3T mpMRI detected 248/323 (76.7%) index lesions in peripheral zone and 75/323 (23.2%) in transition zone. Transition zone prostate cancer had higher median prostate-specific antigen (p = 0.001), larger tumor on 3T mpMRI (p = 0.001), lower proportions of PI-RADSv2 category 4 and 5 (p < 0.001), and lower pathological stage (p = 0.055) compared to peripheral zone prostate cancer. No significant differences were detected in prostate-specific antigen density, preoperative biopsy, and pathology Gleason Scores. After adjusting for significant variables from univariate analysis including prostate volume, tumor volume, prostate-specific antigen, PI-RADSv2 category, AUC for predicting clinically significant tumor in transition zone and peripheral zone were 0.80 and 0.72, respectively (p = 0.36). The diagnostic performance of PI-RADSv2 for clinically significant transition and peripheral zone prostate cancer was similar. However, there was a lower portion of PI-RADSv2 4 and 5 lesions in transition zone compared to peripheral zone.

Multiparametric MR imaging of peripheral zone prostate cancer: effect of postbiopsy hemorrhage on cancer detection according to Gleason score and tumour volume.

To evaluate effect of postbiopsy hemorrhage on detection of peripheral zone (PZ) prostate cancer by multiparametric MR imaging according to Gleason score and tumor volume. This retrospective study included 54 biopsy-proven prostate cancer patients (median age, 67.0 years) who underwent multiparametric MR imaging. Two independent readers evaluated each sextant of the PZ using the PI-RADS v2. One reader recorded the presence or absence of hemorrhage per sextant on T1weighted MR images. Areas under the receiver operating characteristic curves (AUCs) were used to evaluate cancer detection accuracy. Postbiopsy hemorrhage was noted in 122 (37.7%) of 324 sextants of all patients. There was no significant difference in the AUC for detection of cancer with Gleason score≥3+4 or volume≥0.5 ml between sextants with and without hemorrhage (with hemorrhage, reader 1, 0.83 for Gleason score≥3+4, 0.84 for tumor volume≥0.5 ml; reader 2, 0.74 for Gleason score≥3+4, 0.77 for tumor volume≥0.5 ml; without hemorrhage, reader 1, 0.86 for Gleason score≥3+4, 0.88 for tumor volume≥0.5 ml; reader 2, 0.79 for Gleason score≥3+4, 0.83 for tumor volume≥0.5 ml; p > 0.2 for all). Postbiopsy hemorrhage did not negatively affect the detection of clinically significant PZ prostate cancer on multiparametric MR imaging. Advances in knowledge: Under influence of postbiopsy hemorrhage, multiparametric MR can be useful for the detection of clinically significant PZ prostate cancer.

PI-RADS v2 in Prostate Cancer and Correlation with T Staging, PSA Levels and ADC Values

Background: Magnetic resonance imaging of the prostate is a major diagnostic tool in prostate cancer detection. In 2012, prostate imaging reporting and data system (PI-RADS) was published by the European society of urogenital radiology. Due to certain limitations in this version, arevised version namedProstate Imaging Reporting and Data System version 2 (PI-RADS v2) was proposed by the American College of Radiology in 2014 Objectives: To evaluate the role of multiparametric magnetic resonance imaging based prostate imaging reporting and data system (PI-RADS) version 2 for the assessment of peripheral zone prostate cancer and its correlation with the T staging, prostatic specific antigen (PSA) levels and apparent diffusion coefficient (ADC) values. Patients and Methods: Eighty seven patients underwent MRI at 1.5 T units. Dynamic contrast enhanced (DCE) imaging was done in 73 patients as 14 patients had deranged renal functions. Patients with a PI-RADS v2 score of ≥ 2 underwent biopsy except in one patient with a score of 1. The histopathological report was used as gold standard. T staging was done based on MRI. The score was correlated with T staging, ADC values and serum prostate-specific antigen (S.PSA) levels by two experienced blinded radiologists. Statistical analysis was done. Results: Highly significant correlation was observed between PI-RADS v2 score, T staging, ADC values and s PSA levels with P value < 0.005. DCE had added advantage in one patient only for up-gradation of score from 3 to 4. Conclusion: The dominant MR imaging sequence for the peripheral zone prostate cancer is diffusion weighted sequence and the corresponding ADC values, with the role of DCE sequences in doubtful cases only. PI-RADS v2 gives a scoring system on MRI for prostate cancer assessment. It should be routinely incorporated in the reporting protocol. Our study concluded that there is a highly significant correlation between lesion score on PI-RADS v2 with the T stage, corresponding ADC values and S.PSA levels, although larger study groups may be required for further evaluation and beyond doubt PI-RADS version 3 is already in its earliest stages.