Role of mpMRI of the prostate in screening for prostate cancer.
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ABSTRACT:
SUBMITTER: Wallis CJD
PROVIDER: S-EPMC5503955 | biostudies-literature | 2017 Jun
REPOSITORIES: biostudies-literature
Publications
Role of mpMRI of the prostate in screening for prostate cancer.
Wallis Christopher J D CJD Haider Masoom A MA Nam Robert K RK
Translational andrology and urology 20170601 3
Prostate cancer screening offers the opportunity to significantly reduce morbidity and mortality from this disease. Currently, serum prostate-specific antigen (PSA) testing is the most widely used screening modality. However, PSA testing continues to have low positive and negative predictive value leading to unnecessary invasive prostate biopsy while missing patients with aggressive forms of the disease. Magnetic resonance imaging (MRI) has been gaining an increasingly large role in the manageme ...[more]
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Project description:PurposeTo improve preoperative risk stratification for prostate cancer (PCa) by incorporating multiparametric MRI (mpMRI) features into risk stratification tools for PCa, CAPRA and D'Amico.Methods807 consecutive patients operated on by robot-assisted radical prostatectomy at our institution during the period 2010-2015 were followed to identify biochemical recurrence (BCR). 591 patients were eligible for final analysis. We employed stepwise backward likelihood methodology and penalised Cox cross-validation to identify the most significant predictors of BCR including mpMRI features. mpMRI features were then integrated into image-adjusted (IA) risk prediction models and the two risk prediction tools were then evaluated both with and without image adjustment using receiver operating characteristics, survival and decision curve analyses.Results37 patients suffered BCR. Apparent diffusion coefficient (ADC) and radiological extraprostatic extension (rEPE) from mpMRI were both significant predictors of BCR. Both IA prediction models reallocated more than 20% of intermediate-risk patients to the low-risk group, reducing their estimated cumulative BCR risk from approximately 5% to 1.1%. Both IA models showed improved prognostic performance with a better separation of the survival curves.ConclusionIntegrating ADC and rEPE from mpMRI of the prostate into risk stratification tools improves preoperative risk estimation for BCR.Key points• MRI-derived features, ADC and EPE, improve risk stratification of biochemical recurrence. • Using mpMRI to stratify prostate cancer patients improves the differentiation between risk groups. • Using preoperative mpMRI will help urologists in selecting the most appropriate treatment.
| S-EPMC5811593 | biostudies-literature
Project description:BackgroundWe aimed to compare the detection rates of prostate cancer (PCa) and clinically significant prostate cancer(csPCa) by biparametric (bp-) and multiparameter magnetic resonance imaging (mpMRI).Materials and methodsA total of 699 patients who underwent transperineal prostate biopsy in the Department of Urology, the Second Affiliated Hospital of Nantong University from January 2018 to December 2021 were retrospectively reviewed. Multivariate analysis was used to explore the influencing factors associated with the detection rates of PCa and csPCa. According to MRI examination before biopsy, the patients were divided into bpMRI group and mpMRI group. The detection rates of PCa and csPCa by bpMRI and mpMRI were compared. Furthermore, stratified analysis was performed for patients in these two groups to compare the detection rates of PCa and csPCa at different tPSA intervals, different prostate volume (PV) intervals and different PI-RADS V2 scores.ResultsA total of 571 patients were finally analyzed in this study after exclusion, and the overall detection rate of PCa was 54.5%. Multivariate analysis showed that patient age, tPSA level, prostate volume and PI-RADS V2 score were independent risk factors affecting the detection rates of PCa and csPCa. The detection rates of PCa and csPCa by bpMRI and mpMRI were comparable (51.3% vs. 57.9%, 44.0% vs. 48.0%, both P > 0.05), with no statistical significance. In the tPSA 10-20 ng/ml interval, the detection rates of PCa (59.72% vs. 40.35%, P = 0.011) and csPCa (51.39% vs. 28.82%, P = 0.005) by mpMRI were significantly higher than those by bpMRI, while in other tPSA interval (tPSA < 4 ng/ml, 4-10 ng/ml, 20-100 ng/ml), different PVs (≤30 ml, 30-60 ml, >60 ml) and different PI-RADS V2 scores (3, 4, and 5), the detection rates of PCa and csPCa were comparable between the two groups.ConclusionFor detecting PCa and csPCa, bpMRI and mpMRI had similar diagnostic efficacies, whereas mpMRI detected more PCa and csPCa in the tPSA interval of 10-20 ng/ml.
| S-EPMC9885087 | biostudies-literature
Project description:PurposeRisk calculators (RC) aim to improve prebiopsy risk stratification. Their latest versions now include multiparametric magnetic resonance imaging (mpMRI) findings. For their implementation into clinical practice, critical external validations are needed.MethodsWe retrospectively analyzed the patient data of 554 men who underwent ultrasound-guided targeted and systematic prostate biopsies at 2 centers. We validated the mpMRI-RCs of Radtke et al. (RC-R) and Alberts et al. (RC-A), previously shown to predict prostate cancer (PCa) and clinically significant PCa (csPCa). We assessed these RCs' prediction accuracy by analyzing the receiver-operating characteristics (ROC) curve and evaluated their clinical utility using Decision Curve Analysis (DCA), including Net-Benefit and Net-Reduction curves.ResultsWe found that the Area Under the ROC Curve (AUC) for predicting PCa was 0.681 [confidence interval (CI) 95% 0.635-0.727] for RC-A. The AUCs for predicting csPCa were 0.635 (CI 95% 0.583-0.686) for RC-A and 0.676 (CI 95% 0.627-0.725) for RC-R. For example, at a risk threshold of 12%, RC-A needs to assess 334 and RC-R 500 patients to detect one additional true positive PCa or csPCa patient, respectively. At the same risk threshold of 12%, RC-A only needs to assess 6 and RC-R 16 patients to detect one additional true negative PCa or csPCa patient.ConclusionThe mpMRI-RCs, RC-R and RC-A, are robust and valuable tools for patient counseling. Although they do not improve PCa and csPCa detection rates by a clinically meaningful margin, they aid in avoiding unnecessary prostate biopsies. Their implementation could reduce overdiagnosis and reduce PCa screening morbidity.
| S-EPMC9512729 | biostudies-literature
Project description:PurposeTo evaluate the cancer detection rates of reduced-core biopsy schemes in patients with unilateral mpMRI-visible intraprostatic lesions and to analyze the contribution of systematic biopsy cores in clinically significant prostate cancer (csPCa) detection.Methods212 patients with mpMRI-visible unilateral intraprostatic lesions undergoing MRI/TRUS fusion-guided targeted biopsy (TBx) and systematic biopsy (SBx) were included. Cancer detection rates of TBx + SBx, as determined by highest Gleason Grade Group (GG), were compared to 3 reduced-core biopsy schemes: TBx alone, TBx + ipsilateral systematic biopsy (IBx; MRI-positive hemigland), and TBx + contralateral systematic biopsy (CBx; MRI-negative hemigland). Patient-level and biopsy core-level data were analyzed using descriptive statistics with confidence intervals. Univariable and multivariable logistic regression analysis was conducted to identify predictors of csPCa (≥ GG2) detected in MRI-negative hemiglands at p < 0.05.ResultsOverall, 43.4% (92/212) of patients had csPCa and 66.0% (140/212) of patients had any PCa detected by TBx + SBx. Of patients with csPCa, 81.5% had exclusively ipsilateral involvement (MRI-positive), 7.6% had only contralateral involvement (MRI-negative), and 10.9% had bilateral involvement. The csPCa detection rates of reduced-core biopsy schemes were 35.4% (75/212), 40.1% (85/212), and 39.6% (84/212) for TBx alone, TBx + IBx, and TBx + CBx, respectively, with detection sensitivities of 81.5%, 92.4%, and 91.3% compared to TBx + SBx.ConclusionReduced-core prostate biopsy strategies confined to the ipsilateral hemigland underestimate csPCa burden by at least 8% in patients with unilateral mpMRI-visible intraprostatic lesions. The combined TBx + SBx strategy maximizes csPCa detection.
| S-EPMC10765956 | biostudies-literature
Project description:Background: The accuracy of multi-parametric MRI (mpMRI) in the pre-operative staging of prostate cancer (PCa) remains controversial. Objective: The purpose of this study was to evaluate the ability of mpMRI to accurately predict PCa extra-prostatic extension (EPE) on a side-specific basis using a risk-stratified 5-point Likert scale. This study also aimed to assess the influence of mpMRI scan quality on diagnostic accuracy. Patients and Methods: We included 124 men who underwent robot-assisted RP (RARP) as part of the NeuroSAFE PROOF study at our centre. Three radiologists retrospectively reviewed mpMRI blinded to RP pathology and assigned a Likert score (1-5) for EPE on each side of the prostate. Each scan was also ascribed a Prostate Imaging Quality (PI-QUAL) score for assessing the quality of the mpMRI scan, where 1 represents the poorest and 5 represents the best diagnostic quality. Outcome measurements and statistical analyses: Diagnostic performance is presented for the binary classification of EPE, including 95% confidence intervals and the area under the receiver operating characteristic curve (AUC). Results: A total of 231 lobes from 121 men (mean age 56.9 years) were evaluated. Of these, 39 men (32.2%), or 43 lobes (18.6%), had EPE. A Likert score ≥3 had a sensitivity (SE), specificity (SP), NPV, and PPV of 90.4%, 52.3%, 96%, and 29.9%, respectively, and the AUC was 0.82 (95% CI: 0.77-0.86). The AUC was 0.76 (95% CI: 0.64-0.88), 0.78 (0.72-0.84), and 0.92 (0.88-0.96) for biparametric scans, PI-QUAL 1-3, and PI-QUAL 4-5 scans, respectively. Conclusions: MRI can be used effectively by genitourinary radiologists to rule out EPE and help inform surgical planning for men undergoing RARP. EPE prediction was more reliable when the MRI scan was (a) multi-parametric and (b) of a higher image quality according to the PI-QUAL scoring system.
| S-EPMC9139507 | biostudies-literature
Project description:ObjectiveUtilizing personalized risk assessment for clinically significant prostate cancer (csPCa) incorporating multiparametric magnetic resonance imaging (mpMRI) reduces biopsies and overdiagnosis. We validated both multi- and univariate risk models in biopsy-naïve men, with and without the inclusion of mpMRI data for csPCa detection.MethodsN = 565 men underwent mpMRI-targeted prostate biopsy, and the diagnostic performance of risk calculators (RCs), mpMRI alone, and clinical measures were compared using receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA). Subgroups were stratified based on mpMRI findings and quality.ResultscsPCa was detected in 56.3%. PI-RADS score achieved the highest area under the curve (AUC) when comparing univariate risk models (AUC 0.82, p < 0.001). Multivariate RCs showed only marginal improvement in csPCa detection compared to PI-RADS score alone, with just one of four RCs showing significant superiority. In mpMRI-negative cases, the non-MRI-based RC performed best (AUC 0.80, p = 0.016), with the potential to spare biopsies for 23%. PSA-density and multivariate RCs demonstrated comparable performance for PI-RADS 3 constellation (AUC 0.65 vs. 0.60-0.65, p > 0.5; saved biopsies 16%). In men with suspicious mpMRI, both mpMRI-based RCs and the PI-RADS score predicted csPCa excellently (AUC 0.82-0.79 vs. 0.80, p > 0.05), highlighting superior performance compared to non-MRI-based models (all p < 0.002). Quality-assured imaging consistently improved csPCa risk stratification across all subgroups.ConclusionIn tertiary centers serving a high-risk population, high-quality mpMRI provides a simple yet effective way to assess the risk of csPCa. Using multivariate RCs reduces multiple biopsies, especially in mpMRI-negative and PI-RADS 3 constellation.
| S-EPMC10964518 | biostudies-literature
Project description:Purpose The study aimed to construct a predictive model for clinically significant prostate cancer (csPCa) and investigate its clinical efficacy to reduce unnecessary prostate biopsies. Methods A total of 847 patients from institute 1 were included in cohort 1 for model development. Cohort 2 included a total of 208 patients from institute 2 for external validation of the model. The data obtained were used for retrospective analysis. The results of magnetic resonance imaging were obtained using Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1). Univariate and multivariate analyses were performed to determine significant predictors of csPCa. The diagnostic performances were compared using the receiver operating characteristic (ROC) curve and decision curve analyses. Results Age, prostate-specific antigen density (PSAD), and PI-RADS v2.1 scores were used as predictors of the model. In the development cohort, the areas under the ROC curve (AUC) for csPCa about age, PSAD, PI-RADS v2.1 scores, and the model were 0.675, 0.823, 0.875, and 0.938, respectively. In the external validation cohort, the AUC values predicted by the four were 0.619, 0.811, 0.863, and 0.914, respectively. Decision curve analysis revealed that the clear net benefit of the model was higher than PI-RADS v2.1 scores and PSAD. The model significantly reduced unnecessary prostate biopsies within the risk threshold of > 10%. Conclusions In both internal and external validation, the model constructed by combining age, PSAD, and PI-RADS v2.1 scores exhibited excellent clinical efficacy and can be utilized to reduce unnecessary prostate biopsies. Supplementary Information The online version contains supplementary material available at 10.1186/s12957-023-02959-1.
| S-EPMC9990202 | biostudies-literature
Project description:BackgroundProstate cancer is one of the most common cancers worldwide. Currently, convolution neural networks (CNNs) are achieving remarkable success in various computer vision tasks, and in medical imaging research. Various CNN architectures and methodologies have been applied in the field of prostate cancer diagnosis. In this work, we evaluate the impact of the adaptation of a state-of-the-art CNN architecture on domain knowledge related to problems in the diagnosis of prostate cancer. The architecture of the final CNN model was optimised on the basis of the Prostate Imaging Reporting and Data System (PI-RADS) standard, which is currently the best available indicator in the acquisition, interpretation, and reporting of prostate multi-parametric magnetic resonance imaging (mpMRI) examinations.MethodsA dataset containing 330 suspicious findings identified using mpMRI was used. Two CNN models were subjected to comparative analysis. Both implement the concept of decision-level fusion for mpMRI data, providing a separate network for each multi-parametric series. The first model implements a simple fusion of multi-parametric features to formulate the final decision. The architecture of the second model reflects the diagnostic pathway of PI-RADS methodology, using information about a lesion's primary anatomic location within the prostate gland. Both networks were experimentally tuned to successfully classify prostate cancer changes.ResultsThe optimised knowledge-encoded model achieved slightly better classification results compared with the traditional model architecture (AUC = 0.84 vs. AUC = 0.82). We found the proposed model to achieve convergence significantly faster.ConclusionsThe final knowledge-encoded CNN model provided more stable learning performance and faster convergence to optimal diagnostic accuracy. The results fail to demonstrate that PI-RADS-based modelling of CNN architecture can significantly improve performance of prostate cancer recognition using mpMRI.
| S-EPMC7953869 | biostudies-literature
Project description:ObjectiveTo compare Prostate Health Index (PHI) and prostate-specific antigen (PSA) density as secondary tests after multiparametric magnetic resonance imaging (mpMRI) in improving the detection accuracy of Gleason grade group (GG) 2-5 prostate cancer (PCa) and in decreasing unnecessary biopsies in a multiethnic biopsy-naïve population.MethodsFrom February 2017 to February 2020, we recruited consecutive biopsy-naïve men in participating urology clinics for elevated PSA levels. They all had a PHI score, mpMRI, and prostate biopsy. Experienced genitourinary radiologists read all mpMRI studies based on PIRADS version 2.0. Logistic regression models were used to generate receiver operating characteristic curves. Models were tested for effect modification between Race (Black vs White) and both PHI and PSA density, and Race and PIRADS to determine if race impacted their prediction accuracy. Sensitivity, specificity, and predictive values of PHI and PSA density thresholds were calculated by PIRADS scores. The primary outcome was GG2-5 PCa, that is, Gleason score ≥3 + 4.ResultsThe study included 143 men, of which 65 (45.5%) were self-reported Black. Median age was 62.0 years and 55 men (38.4%) had GG2-5 PCa. Overall, 18.1% had PIRADS 1-2, 32.9% had PIRADS 3, and 49.0% had PIRADS 4-5. For the binary logistic regressions, the interactions between PIRADS and Race (P = .08), Log (PHI) and Race (P = .17), and Log (PSA density) and Race (P = .42) were not statistically significant. Within PIRADS 3 lesions, a PHI ≥49 prevented unnecessary biopsies in 55% of men and missed no GG2-5 PCa, yielding a negative predictive value of 100%. There was no reliable PHI or PSA density threshold to avoid PCa biopsies in PIRADS 1-2 or 4-5.ConclusionsPHI and PSA density can be used after mpMRI to improve the detection of GG2-5 PCa in a biopsy-naïve cohort. PHI may be superior to PSA density in PIRADS 3 lesions by avoiding 55% of unnecessary biopsies. Using both PHI and PSA density in series may further increase specificity and lead to fewer unnecessary biopsies, but further larger studies are warranted to determine the optimal threshold of each biomarker.
| S-EPMC8988695 | biostudies-literature
Project description:BackgroundAI-based software may improve the performance of radiologists when detecting clinically significant prostate cancer (csPCa). This study aims to compare the performance of radiologists in detecting MRI-visible csPCa on MRI with and without AI-based software.Materials and methodsIn total, 480 multiparametric MRI (mpMRI) images were retrospectively collected from eleven different MR devices, with 349 csPCa lesions in 180 (37.5%) cases. The csPCa areas were annotated based on pathology. Sixteen radiologists from four hospitals participated in reading. Each radiologist was randomly assigned to 30 cases and diagnosed twice. Half cases were interpreted without AI, and the other half were interpreted with AI. After four weeks, the cases were read again in switched mode. The mean diagnostic performance was compared using sensitivity and specificity on lesion level and patient level. The median reading time and diagnostic confidence were assessed.ResultsOn lesion level, AI-aided improved the sensitivity from 40.1% to 59.0% (18.9% increased; 95% confidence interval (CI) [11.5, 26.1]; p < .001). On patient level, AI-aided improved the specificity from 57.7 to 71.7% (14.0% increase, 95% CI [6.4, 21.4]; p < .001) while preserving the sensitivity (88.3% vs. 93.9%, p = 0.06). AI-aided reduced the median reading time of one case by 56.3% from 423 to 185 s (238-s decrease, 95% CI [219, 260]; p < .001), and the median diagnostic confidence score was increased by 10.3% from 3.9 to 4.3 (0.4-score increase, 95% CI [0.3, 0.5]; p < .001).ConclusionsAI software improves the performance of radiologists by reducing false positive detection of prostate cancer patients and also improving reading times and diagnostic confidence.Clinical relevance statementThis study involves the process of data collection, randomization and crossover reading procedure.
| S-EPMC10149551 | biostudies-literature