scholarly journals DWI of Prostate Cancer: Optimalb-Value in Clinical Practice

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Guglielmo Manenti ◽  
Marco Nezzo ◽  
Fabrizio Chegai ◽  
Erald Vasili ◽  
Elena Bonanno ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Weighted Imaging ◽  
Diagnostic Performance ◽  
Peripheral Zone ◽  
Transitional Zone ◽  
Young Reader ◽  
Mri Scan ◽  
Benign Tissue ◽  
Clinically Significant ◽  
Prostate Cancers

Aim. To compare the diagnostic performance of diffusion weighted imaging (DWI) usingb-values of 1000 s/mm2and 2000 s/mm2at 3 Tesla (T) for the evaluation of clinically significant prostate cancer.Matherials and Methods. Seventy-eight prostate cancer patients underwent a 3T MRI scan followed by radical prostatectomy. DWI was performed usingb-values of 0, 1000, and 2000 s/mm2and qualitatively analysed by two radiologists. ADC maps were obtained atb-values of 1000 and 2000 s/mm2and quantitatively analyzed in consensus.Results. For diagnosis of 78 prostate cancers the accuracy of DWI for the young reader was significantly greater atb= 2000 s/mm2for the peripheral zone (PZ) but not for the transitional zone (TZ). For the experienced reader, DWI did not show significant differences in accuracy betweenb-values of 1000 and 2000 s/mm2. The quantitative analysis in the PZ and TZ was substantially superimposable between the twob-values, albeit with a higher accuracy with ab-value of 2000 s/mm2.Conclusions. With ab-value of 2000 s/mm2at 3T both readers differentiated clinical significant cancer from benign tissue; higherb-values can be helpful for the less experienced readers.

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

2019 ◽  
Vol 70 (4) ◽  
pp. 441-451
Author(s):  
Emetullah Cindil ◽  
Yusuf Oner ◽  
Halit Nahit Sendur ◽  
Hakan Ozdemir ◽  
Eymen Gazel ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Diffusion Weighted Imaging ◽  
Diagnostic Performance ◽  
Peripheral Zone ◽  
Resonance Imaging ◽  
Dce Mri ◽  
Clinically Significant ◽  
Prostate Cancers

Introduction To 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 Methods Twenty-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. Results Ktrans 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). Conclusion The combination of Ktrans and ADC and their normalized versions may help differentiate between ncsPCa from csPCa in the peripheral zone.

The best prostate biopsy sampling system—fusion and systematic biopsy: A single center experience

2021 ◽  
pp. 039156032110371
Author(s):  
Alfonso Califano ◽  
Alessandro Caputo ◽  
Antonio D’Antonio ◽  
Vincenzo Ciccone ◽  
Marco Fabiano ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diagnostic Accuracy ◽  
Diagnostic Performance ◽  
Targeted Biopsy ◽  
Sampling System ◽  
Single Center Experience ◽  
Prostate Biopsies ◽  
Clinically Significant ◽  
Prostate Cancers ◽  
Systematic Biopsy

Background: Prostate cancer is the second most commonly diagnosed cancer in men. The diagnostic accuracy in prostate cancer can be increased by employing a preliminary multiparametric MRI followed by a fusion-targeted biopsy. Methods: To compare the diagnostic accuracy of fusion-targeted biopsy with the standard systematic biopsy in prostate cancer patients, we enrolled 139 patients on which we performed 139 prostate biopsies consisting of three targeted samples followed by 12 regular systematic samples. Based on histology, we analyzed the diagnostic performance of the two methods. Results: Both methods were equally good at detecting clinically significant cancer (83.3%, 50/60), while systematic biopsy detected more clinically insignificant cancers. However, the best diagnostic performance is obtained by combining the two methods. Conclusion: The two methods are best seen as synergistic, and the addition of fusion biopsy can be used to detect more clinically significant prostate cancers than systematic biopsy alone.

scholarly journals Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features

2020 ◽  
Vol 30 (12) ◽  
pp. 6757-6769 ◽  
Author(s):  
Simon Bernatz ◽  
Jörg Ackermann ◽  
Philipp Mandel ◽  
Benjamin Kaltenbach ◽  
Yauheniya Zhdanovich ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Clinical Significance ◽  
Clinical Assessment ◽  
Diagnostic Performance ◽  
Learning Algorithms ◽  
Peripheral Zone ◽  
Multiparametric Mri ◽  
Machine Learning Algorithms ◽  
Clinically Significant

Abstract Objectives To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance. Key Points • Quantitative imaging features differ between normal and malignant tissue of the peripheral zone in prostate cancer. • Radiomic feature analysis of clinical routine multiparametric MRI has the potential to improve the stratification of clinically significant versus insignificant prostate cancer lesions in the peripheral zone. • Certain combinations of standard multiparametric MRI reporting and assessment categories with feature subsets and machine learning algorithms reduced the diagnostic performance over standard clinical assessment categories alone.

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

PLoS ONE ◽  
2018 ◽  
Vol 13 (6) ◽  
pp. e0199636 ◽  
Author(s):  
Hakmin Lee ◽  
Sung Il Hwang ◽  
Hak Jong Lee ◽  
Seok-Soo Byun ◽  
Sang Eun Lee ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Transition Zone ◽  
Diffusion Weighted Imaging ◽  
Diagnostic Performance ◽  
Peripheral Zone ◽  
Diffusion Weighted ◽  
Zone Versus ◽  
Versus Transition

scholarly journals Is PSA density of the peripheral zone as a useful predictor for prostate cancer in patients with gray zone PSA levels?

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jaegeun Lee ◽  
Seung Woo Yang ◽  
Long Jin ◽  
Chung Lyul Lee ◽  
Ji Yong Lee ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Diagnosis ◽  
Prostate Biopsy ◽  
Peripheral Zone ◽  
Transitional Zone ◽  
Gray Zone ◽  
Prostate Cancer Diagnosis ◽  
Psa Density ◽  
Diagnosis Group ◽  
Predictive Rate

Abstract Background Serum prostate-specific antigen (PSA) is widely used in screening tests for prostate cancer. As the low specificity of PSA results in unnecessary and invasive prostate biopsies, we evaluated the clinical significance of various PSAs and PSA density (PSAD) related to peripheral zones in patients with gray zone PSA level (4–10 ng/mL). Methods A total of 1300 patients underwent transrectal ultrasonography-guided prostate biopsy from 2014 to 2019. Among them, 545 patients in the gray zone were divided into the prostate cancer diagnosis group and the non-prostate cancer diagnosis group, and PSA, relative extra transitional zone PSA (RETzPSA), estimated post holmium laser enucleation of the prostate PSA (EPHPSA), PSAD, peripheral zone PSA density (PZPSAD) and extra-transitional zone density (ETzD) were compared and analyzed using receiver-operating characteristics (ROC) analysis after 1:1 matching using propensity score. Results Area under the ROC curve values of PSA, EPHPSA, RETzPSA, PSA density, ETzD, and PZPSAD were 0.553 (95% CI: 0.495–0.610), 0.611 (95% CI: 0.554–0.666), 0.673 (95% CI: 0.617–0.725), 0.745 (95% CI: 0.693–0.793), 0.731 (95% CI: 0.677–0.780) and 0.677 (95% CI: 0.611–0.719), respectively. PSAD had 67.11% sensitivity, 71.71% specificity, and 70.34% positive predictive rate at 0.18 ng/mL/cc. ETzD had 69.08% sensitivity, 64.47% specificity, and 66.04% positive predictive rate at 0.04 ng/mL/cc. When the cut-off value of PSAD was increased to 0.18 ng/mL/cc, the best results were obtained with an odds ratio of 5.171 (95% CI: 3.171–8.432), followed by ETzD with 4.054 (95% CI: 2.513–6.540). Conclusions These results suggested that volume-adjusted parameters (ETzD and PSAD) might be more sensitive and accurate than various PSA in gray zone patients who required prostate biopsy to reduce unnecessary biopsy.

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

2021 ◽  
pp. 20201434
Author(s):  
Yasuyo Urase ◽  
Yoshiko Ueno ◽  
Tsutomu Tamada ◽  
Keitaro Sofue ◽  
Satoru Takahashi ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diagnostic Performance ◽  
Area Under The Curve ◽  
Data System ◽  
Transition Zones ◽  
Prostate Imaging ◽  
Institutional Review ◽  
3.0 T ◽  
Clinically Significant ◽  
Interreader Agreement

Objectives: To evaluate the interreader agreement and diagnostic performance of the Prostate Imaging Reporting and Data System (PI-RADS) v2.1, in comparison with v2. Methods: Institutional review board approval was obtained for this retrospective study. Seventy-seven 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 v2.1 and v2. 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 cutoff ≥3, the agreement among all readers was better with v2.1 than v2 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 cutoff ≥4, the agreement among all readers was also better with v2.1 than v2 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 v2.1 was higher than with v2 (TZ, 0.826–0.907 vs 0.788–0.856; PZ, 0.857–0.919 vs 0.853–0.902). Conclusions: Our study suggests that the PI-RADS v2.1 could improve the interreader agreement and might contribute to improved diagnostic performance compared with v2. Advances in knowledge: PI-RADS v2.1 has a potential to improve interreader variability and diagnostic performance among radiologists with different levels of expertise.

scholarly journals Prostate cancer topography and tumour conspicuity on multiparametric magnetic resonance imaging: A systematic review and meta-analysis

2021 ◽  
Author(s):  
Pranav Satish ◽  
Alex Freeman ◽  
Daniel Kelly ◽  
Alex Kirkham ◽  
Clement Orczyk ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Systematic Review ◽  
Meta Analysis ◽  
Peripheral Zone ◽  
Transitional Zone ◽  
Dynamic Contrast Enhancement ◽  
Significant Difference ◽  
Prisma Statement ◽  
Tumour Location ◽  
The Relationship

Introduction The implications of tumour location on mpMRI conspicuity are not fully understood. Identifying topographical correlates that influence conspicuity may improve outcomes. Here, we present the first systematic review and meta-analysis describing the effect of tumour location on prostate cancer conspicuity on mpMRI. Methods Medline, PubMed, EMBASE and Cochrane databases were systematically searched and results were assessed as per the PRISMA statement. Differential tumour conspicuity on mpMRI was compared between cancers in the peripheral zone (PZ), transitional zone (TZ), base, apex, anterior and posterior. Meta-analysis was conducted to compare diagnostic odds ratios (DOR) of mpMRI detection for tumours in the PZ and TZ. PROSPERO registration: CRD42021228087. Results Thematic synthesis showed apical and basal tumours had reduced conspicuity compared to mid-gland tumours. Cancer in the TZ demonstrated increased conspicuity on T2-weighted imaging, whilst PZ cancers had higher conspicuity on diffusion-weighted and dynamic contrast enhancement imaging. mpMRI had better diagnostic accuracy for PZ lesions, albeit higher specificity for TZ lesions. Meta-analysis showed an increased DOR for PZ tumours (OR: 7.206 [95% CI: 4.991;10.403], compared to TZ (OR: 5.310 [95% CI: 3.082; 9.151]). However, the test for subgroup differences was not significant (p = 0.2743). Conclusions Cancer in the apex or base of the prostate may be less conspicuous than mid-gland tumours. Similarly, TZ cancer appears to have reduced conspicuity compared to PZ cancer, however, meta-analysis did not show a significant difference between DOR. Future larger studies with prospective datasets are required to clarify the relationship between tumour position and conspicuity.

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