scholarly journals The Role of MRI-US Fusion Techniques in Detection of Clinically Significant Prostate Cancer

2021 ◽  
Vol 17 (1) ◽  
pp. 3-7
Author(s):  
Samir Ali Muter
Keyword(s):  
Prostate Cancer ◽  
Radiation Treatment ◽  
Biopsy Needle ◽  
Prostate Imaging ◽  
Tumor Pathology ◽  
Static Images ◽  
Clinically Significant ◽  
Magnetic Resonance Imaging Mri ◽  
Limited Sensitivity

Prostate cancer is the commonest male cancer and the second leading cause of cancer-related death in men. Over many decades, prostate cancer detection represented a continuous challenge to urologists. Although all urologists and pathologists agree that tissue diagnosis is essential especially before commencing active surgical or radiation treatment, the best way to obtain the biopsy was always the big hurdle. The heterogenicity of the tumor pathology is very well seen in its radiological appearance. Ultrasound has been proven to be of limited sensitivity and specificity in detecting prostate cancer. However, it was the only available targeting technique for years and was used to guide biopsy needle passed transrectally or transperineally. Magnetic Resonance Imaging (MRI) has revolutionized the process with the advent of its multiparametric imaging (mp MRI) where the prostate is evaluated by different MRI techniques and the likelihood of the detected lesion is scored using the new prostate imaging-reporting and data system (PIRADS) scoring. Despite the improved detection of clinically significant prostate cancer by mpMRI, the ideal way to target the area of suspicion detected by mpMRI is the next level of challenge. In this review article, we will discuss the recent methods of targeting and focus on the different platforms used to integrate the mpMRI static images with the real-time US scanning in what is called (US-MRI fusion techniques).

scholarly journals Prostate MRI quality: a critical review of the last 5 years and the role of the PI-QUAL score

2021 ◽  
pp. 20210415
Author(s):  
Francesco Giganti ◽  
Veeru Kasivisvanathan ◽  
Alex Kirkham ◽  
Shonit Punwani ◽  
Mark Emberton ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Image Quality ◽  
Critical Review ◽  
Resonance Imaging ◽  
Technical Standards ◽  
Prostate Imaging ◽  
Technical Requirements ◽  
Patient Preparation ◽  
Clinically Significant

There is increasing interest in the use of multiparametric magnetic resonance imaging (mpMRI) in the prostate cancer pathway. The European Association of Urology (EAU) and the British Association of Urological Surgeons (BAUS) now advise mpMRI prior to biopsy, and the Prostate Imaging Reporting and Data System (PI-RADS) recommendations set out the minimal technical requirements for the acquisition of mpMRI of the prostate. The widespread and swift adoption of this technique has led to variability in image quality. Suboptimal image acquisition reduces the sensitivity and specificity of mpMRI for the detection and staging of clinically significant prostate cancer. This critical review outlines the studies aimed at improving prostate MR quality that have been published over the last 5 years. These span from the use of specific MR sequences, magnets and coils to patient preparation. The rates of adherence of prostate mpMRI to technical standards in different cohorts across the world are also discussed. Finally, we discuss the first standardised scoring system (i.e., Prostate Imaging Quality, PI-QUAL) that has been created to evaluate image quality, although further iterations of this score are expected in the future.

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 Comparison of Likert and PI-RADS version 2 MRI scoring systems for the detection of clinically significant prostate cancer

2020 ◽  
Vol 93 (1112) ◽  
pp. 20200298 ◽  
Author(s):  
Jeries P Zawaideh ◽  
Evis Sala ◽  
Maria Pantelidou ◽  
Nadeem Shaida ◽  
Brendan Koo ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Area Under The Curve ◽  
Scoring Systems ◽  
Detection Rates ◽  
Prostate Imaging ◽  
Prostate Biopsies ◽  
Threshold Score ◽  
Lesion Level ◽  
Clinically Significant ◽  
Histological Results

Objective: To compare the performance of Likert and Prostate Imaging–Reporting and Data System (PI-RADS) multiparametric (mp) MRI scoring systems for detecting clinically significant prostate cancer (csPCa). Methods: 199 biopsy-naïve males undergoing prostate mpMRI were prospectively scored with Likert and PI-RADS systems by four experienced radiologists. A binary cut-off (threshold score ≥3) was used to analyze histological results by three groups: negative, insignificant disease (Gleason 3 + 3; iPCa), and csPCa (Gleason ≥3 +4). Lesion-level results and prostate zonal location were also compared. Results: 129/199 (64.8%) males underwent biopsy, 96 with Likert or PI-RADS score ≥3, and 21 with negative MRI. A further 12 patients were biopsied during follow-up (mean 507 days). Prostate cancer was diagnosed in 87/199 (43.7%) patients, 65 with (33.6%) csPCa. 30/92 (32.6%) patients with negative MRI were biopsied, with an NPV of 83.3% for cancer and 86.7% for csPCa. Likert and PI-RADS score differences were observed in 92 patients (46.2%), but only for 16 patients (8%) at threshold score ≥3. Likert scoring had higher specificity than PI-RADS (0.77 vs 0.66), higher area under the curve (0.92 vs 0.87, p = 0.002) and higher PPV (0.66 vs 0.58); NPV and sensitivity were the same. Likert had more five score results (58%) compared to PI-RADS (36%), but with similar csCPa detection (81.0 and 80.6% respectively). Likert demonstrated lower proportion of false positive in the predominately AFMS-involving lesions. Conclusion: Likert and PI-RADS systems both demonstrate high cancer detection rates. Likert scoring had a higher AUC with moderately higher specificity and lower positive call rate and could potentially help to reduce the number of unnecessary biopsies performed. Advances in knowledge: This paper illustrates that the Likert scoring system has potential to help urologists reduce the number of prostate biopsies performed.

scholarly journals Impact of PI-RADS Category 3 lesions on the diagnostic accuracy of MRI for detecting prostate cancer and the prevalence of prostate cancer within each PI-RADS category: A systematic review and meta-analysis

2020 ◽  
pp. 20191050
Author(s):  
Akshay Wadera ◽  
Mostafa Alabousi ◽  
Alex Pozdnyakov ◽  
Mohammed Kashif Al-Ghita ◽  
Ali Jafri ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Meta Analysis ◽  
Test Group ◽  
Positive Test ◽  
Test Accuracy ◽  
Prostate Imaging ◽  
Lower Specificity ◽  
Meta Regression ◽  
Clinically Significant ◽  
Low Prevalence

Objective: To evaluate Prostate Imaging Reporting and Data System (PI-RADS) category 3 lesions’ impact on the diagnostic test accuracy (DTA) of MRI for prostate cancer (PC) and to derive the prevalence of PC within each PI-RADS category. Methods: MEDLINE and Embase were searched until April 10, 2020 for studies reporting on the DTA of MRI by PI-RADS category. Accuracy metrics were calculated using a bivariate random-effects meta-analysis with PI-RADS three lesions treated as a positive test, negative test, and excluded from the analysis. Differences in DTA were assessed utilizing meta-regression. PC prevalence within each PI-RADS category was estimated with a proportional meta-analysis. Results: In total, 26 studies reporting on 12,913 patients (4,853 with PC) were included. Sensitivities for PC in the positive, negative, and excluded test groups were 96% (95% confidence interval [CI] 92–98), 82% (CI 75-87), and 95% (CI 91-97), respectively. Specificities for the positive, negative, and excluded test groups were 33% (CI 23-44), 71% (CI 62-79), and 52% (CI 37-66), respectively. Meta-regression demonstrated higher sensitivity (p < 0.001) and lower specificity (p < 0.001) in the positive test group compared to the negative group. Clinically significant PC prevalences were 5.9% (CI 0-17.1), 11.4% (CI 6.5–17.3), 24.9% (CI 18.4–32.0), 55.7% (CI 47.8–63.5), and 81.4% (CI 75.9–86.4) for PI-RADS categories 1, 2, 3, 4 and 5, respectively. Conclusion: PI-RADS category 3 lesions can significantly impact the DTA of MRI for PC detection. A low prevalence of clinically significant PC is noted in PI-RADS category 1 and 2 cases. Advances in knowledge: Inclusion or exclusion of PI-RADS category 3 lesions impacts the DTA of MRI for PC detection.

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

Cancers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2200 ◽  
Author(s):  
Ahmad Algohary ◽  
Rakesh Shiradkar ◽  
Shivani Pahwa ◽  
Andrei Purysko ◽  
Sadhna Verma ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Risk Stratification ◽  
Operating Characteristic ◽  
Prostate Cancer Risk ◽  
Magnetic Resonance Imaging Mri ◽  
Validation Set ◽  
And Diffusion ◽  
Risk Categories

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.

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