The CADMUS trial: A paired cohort, blinded study comparing multiparametric ultrasound targeted biopsies with multiparametric MRI targeted biopsies in the detection of clinically significant prostate cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 5008-5008
Author(s):  
Alistair Grey ◽  
Rebecca Scott ◽  
Bina Shah ◽  
Peter Acher ◽  
Sidath Liyanage ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diagnostic Performance ◽  
Point Of Care ◽  
Low Cost ◽  
Reporting Quality ◽  
Multiparametric Mri ◽  
Lesion Detection ◽  
Gleason Grade ◽  
Clinically Significant ◽  
Targeted Biopsies

5008 Background: Multiparametric MRI (mpMRI) of the prostate followed by targeted biopsy is recommended in men at risk of prostate cancer. Dissemination of this pathway may be limited by cost, variable scan and reporting quality, and contraindicated in the presence of metallic implants and claustrophobia. Multi-parametric ultrasound (mpUSS) is a point of care test with low cost that combines b-mode, colour Doppler, elastography and contrast enhancement. CADMUS compared the diagnostic performance of mpUSS to mpMRI. Methods: CADMUS recruited 370 patients from seven sites to a prospective, multicentre, paired-cohort trial (ISRCTN 38541912). Ethics committee approval was obtained. Patients underwent both mpUSS and mpMRI independently, each with a positive test defined as a Likert score of >3. Those with either a positive mpUSS or mpMRI, or both, were advised to undergo targeted biopsies. Reporting of each scan was carried out blind to the other and prior to biopsy; patients advised for biopsy were blinded to which test was positive. The order of mpUSS and mpMRI targeting was randomised. Primary outcomes were proportion of positive tests and detection of clinically significant cancer (csPCa) defined as Gleason >4+3 of any length and/or maximum cancer core length of >6mm of any grade [PROMIS definition1]. Results: 306 completed both mpUSS and mpMRI. Agreement in lesion detection between mpUSS and mpMRI was 73.2% (kappa 0.06, p = 0.14). 257 with positive results on mpUSS, mpMRI or both had targeted biopsies. Agreement on detection of csPCa was 91.1% (expected 59.8%, kappa 0.78, p < 0.01). Overall, mpUSS detected 4.3% fewer csPCa than mpMRI (95% CI = [-8.3%, -1.5%]; p = 0.042 [Bonferroni correction]). mpUSS detected 7.2% (6/83) csPCa missed by mpMRI; mpMRI detected 20.5% (17/83) csPCa that mpUSS missed. At a less stringent definition of significant cancer, Gleason grade >3+4 of any length (definition 3), agreement was 89.1% (expected 55.6%, kappa 0.75, p < 0.01) mpUSS detected 5.4% fewer definition 3 cancers than mpMRI overall. mpUSS detected 7% (7/99) definition 3 cancers that mpMRI missed; mpMRI detected 21% (21/99) definition 3 cancers that mpUSS missed. Conclusions: The CADMUS trial shows mpUSS has a diagnostic performance approaching that of mpMRI and significant cancer detection is improved by the use of both scans over mpMRI alone. Clinical trial information: 38541912. [Table: see text]

scholarly journals Evaluating the efficacy of a low-cost, minimally-invasive cognitive MRI targeted biopsy protocol in the Prostate Imaging Reporting and Data System (PI-RADS) version 2 era.

2019 ◽  
Author(s):  
Yuta Takeshima ◽  
Yoshinori Tanaka ◽  
Kotaro Takemura ◽  
Shusaku Nakazono ◽  
Eiko Yamashita ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diagnostic Performance ◽  
Low Cost ◽  
Multiparametric Mri ◽  
Data System ◽  
Targeted Biopsy ◽  
Prostate Imaging ◽  
Biopsy Protocol ◽  
Clinically Significant ◽  
Systematic Biopsy

Abstract Background: New MRI-guided targeting biopsy methods have increased cancer yield of prostate biopsies. However, cost and time constraints have made it difficult for many institutions to implement these newer methods. We evaluated the diagnostic performance of a low-cost, minimally-invasive, cognitive MRI-targeted biopsy protocol based on 1.5T multiparametric MRI graded with Prostate Imaging Reporting and Data System version 2 that is easily implemented in any low- to intermediate- volume center. Methods: Retrospective analysis of 255 patients who underwent prostate biopsy between December 2016 and March 2019 at a single facility. Indication for biopsy was based on clinical parameters including 1.5T multiparametric MRI. In addition to 10-core systematic biopsy, targeted cores were obtained with cognitive recognition under ultrasound. A control group of 198 patients biopsied without prior MRI from January to December 2015 was also analyzed. Results: Prostate biopsy preceded by MRI had a significantly higher probability of detecting both prostate cancer (68.1% vs. 43.6%) and clinically significant cancer (56.2% vs. 29.4%) (p values< 0.01). Combination of systematic biopsy and targeted biopsy outperformed either regimen alone for detection of prostate cancer. Multivariate analysis showed PSA density and prostate imaging reporting and data system score were independent risk factors of prostate cancer. A proposed diagnostic model showed sensitivity of 88.6%, specificity of 55%, PPV of 81.2%, NPV of 68.8%, and accuracy of 78%. Prostate imaging reporting and data system score was correlated with a higher presence of prostate cancer, clinically significant prostate cancer, and a higher pathological grade. Conclusions: Incorporation of pre-biopsy MRI imaging, scoring, and targeted biopsy improved cancer yield and achieved diagnostic performance comparable to newer methods of higher cost. Future alterations of possible benefit included increasing the number of target cores per lesion, and combining prostate imaging reporting and data system score and PSA density as indicators for biopsy.

scholarly journals AutoProstate: Towards Automated Reporting of Prostate MRI for Prostate Cancer Assessment Using Deep Learning

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 6138
Author(s):  
Pritesh Mehta ◽  
Michela Antonelli ◽  
Saurabh Singh ◽  
Natalia Grondecka ◽  
Edward W. Johnston ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Specific Antigen ◽  
Reporting Quality ◽  
Lesion Detection ◽  
Observer Agreement ◽  
Detection Sensitivity ◽  
Automatic Assessment ◽  
Experienced Radiologist ◽  
Clinically Significant

Multiparametric magnetic resonance imaging (mpMRI) of the prostate is used by radiologists to identify, score, and stage abnormalities that may correspond to clinically significant prostate cancer (CSPCa). Automatic assessment of prostate mpMRI using artificial intelligence algorithms may facilitate a reduction in missed cancers and unnecessary biopsies, an increase in inter-observer agreement between radiologists, and an improvement in reporting quality. In this work, we introduce AutoProstate, a deep learning-powered framework for automatic MRI-based prostate cancer assessment. AutoProstate comprises of three modules: Zone-Segmenter, CSPCa-Segmenter, and Report-Generator. Zone-Segmenter segments the prostatic zones on T2-weighted imaging, CSPCa-Segmenter detects and segments CSPCa lesions using biparametric MRI, and Report-Generator generates an automatic web-based report containing four sections: Patient Details, Prostate Size and PSA Density, Clinically Significant Lesion Candidates, and Findings Summary. In our experiment, AutoProstate was trained using the publicly available PROSTATEx dataset, and externally validated using the PICTURE dataset. Moreover, the performance of AutoProstate was compared to the performance of an experienced radiologist who prospectively read PICTURE dataset cases. In comparison to the radiologist, AutoProstate showed statistically significant improvements in prostate volume and prostate-specific antigen density estimation. Furthermore, AutoProstate matched the CSPCa lesion detection sensitivity of the radiologist, which is paramount, but produced more false positive detections.

scholarly journals Multiparametric MRI-Targeted TRUS Prostate Biopsies Using Visual Registration

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Philippe Puech ◽  
Adil Ouzzane ◽  
Vianney Gaillard ◽  
Nacim Betrouni ◽  
Benoit Renard ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diagnostic Yield ◽  
Transrectal Ultrasound ◽  
Multiparametric Mri ◽  
Ultrasound Guided ◽  
Prostate Biopsies ◽  
Multiparametric Prostate Mri ◽  
Clinically Significant ◽  
Prostate Cancers ◽  
Targeted Biopsies

Prebiopsy multiparametric prostate MRI (mp-MRI), followed by transrectal ultrasound-guided (TRUS-G) target biopsies (TB) of the prostate is a key combination for the diagnosis of clinically significant prostate cancers (CSPCa), to avoid prostate cancer (PCa) overtreatment. Several techniques are available for guiding TB to the suspicious mp-MRI targets, but the simplest, cheapest, and easiest to learn is “cognitive,” with visual registration of MRI and TRUS data. This review details the successive steps of the method (target detection, mp-MRI reporting, intermodality fusion, TRUS guidance to target, sampling simulation, sampling, TRUS session reporting, and quality insurance), how to optimize each, and the global indications of mp-MRI-targeted biopsies. We discuss the diagnostic yield of visually-registered TB in comparison with conventional biopsy, and TB performed using other registration methods.

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 Study protocol for a single-centre non-inferior randomised controlled trial on a novel three-dimensional matrix positioning-based cognitive fusion-targeted biopsy and software-based fusion-targeted biopsy for the detection rate of clinically significant prostate cancer in men without a prior biopsy

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e041427
Author(s):  
Biming He ◽  
Rongbing Li ◽  
Dongyang Li ◽  
Liqun Huang ◽  
Xiaofei Wen ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Detection Rate ◽  
Three Dimensional ◽  
Multiparametric Mri ◽  
Fusion Method ◽  
Targeted Biopsy ◽  
Cognitive Fusion ◽  
Single Centre ◽  
Clinically Significant ◽  
Randomised Controlled

IntroductionThe classical pathway for diagnosing prostate cancer is systematic 12-core biopsy under the guidance of transrectal ultrasound, which tends to underdiagnose the clinically significant tumour and overdiagnose the insignificant disease. Another pathway named targeted biopsy is using multiparametric MRI to localise the tumour precisely and then obtain the samples from the suspicious lesions. Targeted biopsy, which is mainly divided into cognitive fusion method and software-based fusion method, is getting prevalent for its good performance in detecting significant cancer. However, the preferred targeted biopsy technique in detecting clinically significant prostate cancer between cognitive fusion and software-based fusion is still beyond consensus.Methods and analysisThis trial is a prospective, single-centre, randomised controlled and non-inferiority study in which all men suspicious to have clinically significant prostate cancer are included. This study aims to determine whether a novel three-dimensional matrix positioning cognitive fusion-targeted biopsy is non-inferior to software-based fusion-targeted biopsy in the detection rate of clinically significant cancer in men without a prior biopsy. The main inclusion criteria are men with elevated serum prostate-specific antigen above 4–20 ng/mL or with an abnormal digital rectal examination and have never had a biopsy before. A sample size of 602 participants allowing for a 10% loss will be recruited. All patients will undergo a multiparametric MRI examination, and those who fail to be found with a suspicious lesion, with the anticipation of half of the total number, will be dropped. The remaining participants will be randomly allocated to cognitive fusion-targeted biopsy (n=137) and software-based fusion-targeted biopsy (n=137). The primary outcome is the detection rate of clinically significant prostate cancer for cognitive fusion-targeted biopsy and software-based fusion-targeted biopsy in men without a prior biopsy. The clinically significant prostate cancer will be defined as the International Society of Urological Pathology grade group 2 or higher.Ethics and disseminationEthical approval was obtained from the ethics committee of Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China. The results of the study will be disseminated and published in international peer-reviewed journals.Trial registration numberClinicalTrials.gov Registry (NCT04271527).

scholarly journals Diagnosis of transition zone prostate cancer by multiparametric MRI: added value of MR spectroscopic imaging with sLASER volume selection

2021 ◽  
Vol 28 (1) ◽  
Author(s):  
Neda Gholizadeh ◽  
Peter B. Greer ◽  
John Simpson ◽  
Jonathan Goodwin ◽  
Caixia Fu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Transition Zone ◽  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Diagnostic Performance ◽  
Spectroscopic Imaging ◽  
Multiparametric Mri ◽  
Low Risk ◽  
Intermediate Risk

Abstract Background Current multiparametric MRI (mp-MRI) in routine clinical practice has poor-to-moderate diagnostic performance for transition zone prostate cancer. The aim of this study was to evaluate the potential diagnostic performance of novel 1H magnetic resonance spectroscopic imaging (MRSI) using a semi-localized adiabatic selective refocusing (sLASER) sequence with gradient offset independent adiabaticity (GOIA) pulses in addition to the routine mp-MRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and quantitative dynamic contrast enhancement (DCE) for transition zone prostate cancer detection, localization and grading. Methods Forty-one transition zone prostate cancer patients underwent mp-MRI with an external phased-array coil. Normal and cancer regions were delineated by two radiologists and divided into low-risk, intermediate-risk, and high-risk categories based on TRUS guided biopsy results. Support vector machine models were built using different clinically applicable combinations of T2WI, DWI, DCE, and MRSI. The diagnostic performance of each model in cancer detection was evaluated using the area under curve (AUC) of the receiver operating characteristic diagram. Then accuracy, sensitivity and specificity of each model were calculated. Furthermore, the correlation of mp-MRI parameters with low-risk, intermediate-risk and high-risk cancers were calculated using the Spearman correlation coefficient. Results The addition of MRSI to T2WI + DWI and T2WI + DWI + DCE improved the accuracy, sensitivity and specificity for cancer detection. The best performance was achieved with T2WI + DWI + MRSI where the addition of MRSI improved the AUC, accuracy, sensitivity and specificity from 0.86 to 0.99, 0.83 to 0.96, 0.80 to 0.95, and 0.85 to 0.97 respectively. The (choline + spermine + creatine)/citrate ratio of MRSI showed the highest correlation with cancer risk groups (r = 0.64, p < 0.01). Conclusion The inclusion of GOIA-sLASER MRSI into conventional mp-MRI significantly improves the diagnostic accuracy of the detection and aggressiveness assessment of transition zone prostate cancer.

scholarly journals Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 973
Author(s):  
Valentina Giannini ◽  
Simone Mazzetti ◽  
Giovanni Cappello ◽  
Valeria Maria Doronzio ◽  
Lorenzo Vassallo ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Computer Aided Diagnosis ◽  
Multiparametric Mri ◽  
Performance Study ◽  
Predictive Values ◽  
Cad System ◽  
Computer Aided ◽  
Clinically Significant ◽  
Sensitivity Specificity ◽  
Aided Diagnosis

Recently, Computer Aided Diagnosis (CAD) systems have been proposed to help radiologists in detecting and characterizing Prostate Cancer (PCa). However, few studies evaluated the performances of these systems in a clinical setting, especially when used by non-experienced readers. The main aim of this study is to assess the diagnostic performance of non-experienced readers when reporting assisted by the likelihood map generated by a CAD system, and to compare the results with the unassisted interpretation. Three resident radiologists were asked to review multiparametric-MRI of patients with and without PCa, both unassisted and assisted by a CAD system. In both reading sessions, residents recorded all positive cases, and sensitivity, specificity, negative and positive predictive values were computed and compared. The dataset comprised 90 patients (45 with at least one clinically significant biopsy-confirmed PCa). Sensitivity significantly increased in the CAD assisted mode for patients with at least one clinically significant lesion (GS > 6) (68.7% vs. 78.1%, p = 0.018). Overall specificity was not statistically different between unassisted and assisted sessions (94.8% vs. 89.6, p = 0.072). The use of the CAD system significantly increases the per-patient sensitivity of inexperienced readers in the detection of clinically significant PCa, without negatively affecting specificity, while significantly reducing overall reporting time.

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.

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