Prostate Magnetic Resonance Imaging Interpretation Varies Substantially Across Radiologists

Sonn, Geoffrey A.; Fan, Richard E.; Ghanouni, Pejman; Wang, Nancy N.; Brooks, James D.; Loening, Andreas M.; Daniel, Bruce L.; To’o, Katherine; Thong, Alan; Leppert, John T.

doi:10.1016/j.euf.2017.11.010

Cited by 225 publications

(164 citation statements)

References 18 publications

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“…If slightly different criteria suggested by other authors were applied to our cohort (i.e., PIRADS = 3 and PSAD < 0.15) [14,16,17], it would result in a 50% reduction in the number of biopsies but would also miss 21% of significant cancers. We speculate that the discrepancy between this finding and those reported in other papers might be explained by the performance of the mpMRI [3,18,19]. This study has some limitations.…”

Section: Discussioncontrasting

confidence: 49%

Use of Prostate Specific Antigen Density Combined with Multiparametric Magnetic Resonance Imaging Improves Triage for Prostate Biopsy

et al. 2019

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Background: Multi-parametric magnetic resonance imaging (mpMRI)-directed biopsy for prostate cancer (PC) diagnosis improves the detection of clinically significant prostate cancer (CSPC) and decreases the rate of over-diagnosis of insignificant disease. The aim of this study was to investigate the value of mpMRI combined with prostate specific antigen density (PSAD) in the decision making related to the biopsy. Methods: mpMRI and mpMRI/transrectal ultrasound fusion targeted biopsies with subsequent systematic biopsies were performed in 397 patients (223 biopsy-naïve and 174 with a previous biopsy). Detection rates of (CSPC) and insignificant PC were stratified using the PIRADS score, and the number of avoidable biopsies and missed (CSPC) were plotted against PSAD values of 0.1–0.5 ng/mL². Results: PIRADS <3 and PSAD <0.2 ng/mL² were the safest criteria for not performing a biopsy. When applied to the biopsy-naïve group, 21.5% (48/223) of the biopsies could have been avoided and 3.7% (3/82) of CSPC would have been missed. In the repeat biopsy group, 12.6% (22/174) of biopsies could have been avoided and 6.9% (4/58) of (CSPC) would have been missed. Conclusions: A combination of mpMRI and PSAD might reduce the number of biopsies performed with the cost of missing <4% of CSPC.

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Section: Discussioncontrasting

confidence: 49%

Use of Prostate Specific Antigen Density Combined with Multiparametric Magnetic Resonance Imaging Improves Triage for Prostate Biopsy

et al. 2019

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“…However, PSA screening leads to over-diagnosis, which leads to unnecessary expensive and painful needle biopsies and potential over-treatment 2 . Multiparametric MRI which relies heavily on diffusionweighted imaging (DWI) has been increasingly becoming the standard of care for prostate cancer diagnosis in radiology setting where the area under the receiver operating characteristic curve (ROC) varies from 0.69 to 0.81 for radiologists detecting PCa 3 . A standardized approach to image interpretation called Pi-Rads v2 4 has been developed for radiologists, however, there remain issues with inter-observer variability in the use of the Pi-Rads scheme 5 .…”

Section: Introductionmentioning

confidence: 99%

Prostate Cancer Detection using Deep Convolutional Neural Networks

Yoo¹,

Gujrathi²,

Haider³

et al. 2019

Sci Rep

174

115

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Prostate cancer is one of the most common forms of cancer and the third leading cause of cancer death in North America. As an integrated part of computer-aided detection (CAD) tools, diffusion-weighted magnetic resonance imaging (DWI) has been intensively studied for accurate detection of prostate cancer. With deep convolutional neural networks (CNNs) significant success in computer vision tasks such as object detection and segmentation, different CNN architectures are increasingly investigated in medical imaging research community as promising solutions for designing more accurate CAD tools for cancer detection. In this work, we developed and implemented an automated CNN-based pipeline for detection of clinically significant prostate cancer (PCa) for a given axial DWI image and for each patient. DWI images of 427 patients were used as the dataset, which contained 175 patients with PCa and 252 patients without PCa. To measure the performance of the proposed pipeline, a test set of 108 (out of 427) patients were set aside and not used in the training phase. The proposed pipeline achieved area under the receiver operating characteristic curve (AUC) of 0.87 (95 Confidence Interval (CI): 0.84–0.90) and 0.84 (95 CI: 0.76–0.91) at slice level and patient level, respectively.

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“…Multiparametric magnetic resonance imaging (MP‐MRI) has been developed as a triage tool to reduce the rates of negative biopsy and its use has become increasingly widespread since its validation 7 . However, MP‐MRI is relatively expensive and has shown a high rate of interoperator and intermachine variability, leading to MP‐MRI missing up to 28% of clinically significant diseases in practice 4,8‐10 …”

Section: Introductionmentioning

confidence: 99%

“…7 However, MP-MRI is relatively expensive and has shown a high rate of interoperator and intermachine variability, leading to MP-MRI missing up to 28% of clinically significant diseases in practice. 4,[8][9][10] The interconnected nature of the male urological system makes it an ideal candidate for liquid biopsy and noninvasive biomarkers for prostate cancer. There is a sizeable interest in the development of such noninvasive tests and classifiers capable of reducing the rates of initial biopsy in men while retaining the sensitivity to detect aggressive disease.…”

Section: Introductionmentioning

confidence: 99%

Development of a multivariable risk model integrating urinary cell DNA methylation and cell‐free RNA data for the detection of significant prostate cancer

et al. 2020

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Background Prostate cancer exhibits severe clinical heterogeneity and there is a critical need for clinically implementable tools able to precisely and noninvasively identify patients that can either be safely removed from treatment pathways or those requiring further follow up. Our objectives were to develop a multivariable risk prediction model through the integration of clinical, urine‐derived cell‐free messenger RNA (cf‐RNA) and urine cell DNA methylation data capable of noninvasively detecting significant prostate cancer in biopsy naïve patients. Methods Post‐digital rectal examination urine samples previously analyzed separately for both cellular methylation and cf‐RNA expression within the Movember GAP1 urine biomarker cohort were selected for a fully integrated analysis (n = 207). A robust feature selection framework, based on bootstrap resampling and permutation, was utilized to find the optimal combination of clinical and urinary markers in a random forest model, deemed ExoMeth. Out‐of‐bag predictions from ExoMeth were used for diagnostic evaluation in men with a clinical suspicion of prostate cancer (PSA ≥ 4 ng/mL, adverse digital rectal examination, age, or lower urinary tract symptoms). Results As ExoMeth risk score (range, 0‐1) increased, the likelihood of high‐grade disease being detected on biopsy was significantly greater (odds ratio = 2.04 per 0.1 ExoMeth increase, 95% confidence interval [CI]: 1.78‐2.35). On an initial TRUS biopsy, ExoMeth accurately predicted the presence of Gleason score ≥3 + 4, area under the receiver‐operator characteristic curve (AUC) = 0.89 (95% CI: 0.84‐0.93) and was additionally capable of detecting any cancer on biopsy, AUC = 0.91 (95% CI: 0.87‐0.95). Application of ExoMeth provided a net benefit over current standards of care and has the potential to reduce unnecessary biopsies by 66% when a risk threshold of 0.25 is accepted. Conclusion Integration of urinary biomarkers across multiple assay methods has greater diagnostic ability than either method in isolation, providing superior predictive ability of biopsy outcomes. ExoMeth represents a more holistic view of urinary biomarkers and has the potential to result in substantial changes to how patients suspected of harboring prostate cancer are diagnosed.

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Prostate Magnetic Resonance Imaging Interpretation Varies Substantially Across Radiologists

Abstract: We evaluated the interpretation of multiparametric magnetic resonance imaging of the prostate in routine clinical care. Diagnostic accuracy depends on the Prostate Imaging Reporting and Data System score and the radiologist.

Cited by 225 publications

References 18 publications

Use of Prostate Specific Antigen Density Combined with Multiparametric Magnetic Resonance Imaging Improves Triage for Prostate Biopsy

Use of Prostate Specific Antigen Density Combined with Multiparametric Magnetic Resonance Imaging Improves Triage for Prostate Biopsy

Prostate Cancer Detection using Deep Convolutional Neural Networks

Development of a multivariable risk model integrating urinary cell DNA methylation and cell‐free RNA data for the detection of significant prostate cancer

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