multiparametric magnetic resonance imaging
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2022 ◽  
Vol 8 ◽  
Author(s):  
Danyan Li ◽  
Xiaowei Han ◽  
Jie Gao ◽  
Qing Zhang ◽  
Haibo Yang ◽  
...  

Background: Multiparametric magnetic resonance imaging (mpMRI) plays an important role in the diagnosis of prostate cancer (PCa) in the current clinical setting. However, the performance of mpMRI usually varies based on the experience of the radiologists at different levels; thus, the demand for MRI interpretation warrants further analysis. In this study, we developed a deep learning (DL) model to improve PCa diagnostic ability using mpMRI and whole-mount histopathology data.Methods: A total of 739 patients, including 466 with PCa and 273 without PCa, were enrolled from January 2017 to December 2019. The mpMRI (T2 weighted imaging, diffusion weighted imaging, and apparent diffusion coefficient sequences) data were randomly divided into training (n = 659) and validation datasets (n = 80). According to the whole-mount histopathology, a DL model, including independent segmentation and classification networks, was developed to extract the gland and PCa area for PCa diagnosis. The area under the curve (AUC) were used to evaluate the performance of the prostate classification networks. The proposed DL model was subsequently used in clinical practice (independent test dataset; n = 200), and the PCa detective/diagnostic performance between the DL model and different level radiologists was evaluated based on the sensitivity, specificity, precision, and accuracy.Results: The AUC of the prostate classification network was 0.871 in the validation dataset, and it reached 0.797 using the DL model in the test dataset. Furthermore, the sensitivity, specificity, precision, and accuracy of the DL model for diagnosing PCa in the test dataset were 0.710, 0.690, 0.696, and 0.700, respectively. For the junior radiologist without and with DL model assistance, these values were 0.590, 0.700, 0.663, and 0.645 versus 0.790, 0.720, 0.738, and 0.755, respectively. For the senior radiologist, the values were 0.690, 0.770, 0.750, and 0.730 vs. 0.810, 0.840, 0.835, and 0.825, respectively. The diagnosis made with DL model assistance for radiologists were significantly higher than those without assistance (P < 0.05).Conclusion: The diagnostic performance of DL model is higher than that of junior radiologists and can improve PCa diagnostic accuracy in both junior and senior radiologists.


BMJ Open ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. e050376
Author(s):  
Pranav Satish ◽  
Alex Freeman ◽  
Daniel Kelly ◽  
Alex Kirkham ◽  
Clement Orczyk ◽  
...  

IntroductionMultiparametric magnetic resonance imaging (mpMRI) has improved the triage of men with suspected prostate cancer, through precision prebiopsy identification of clinically significant disease. While multiple important characteristics, including tumour grade and size have been shown to affect conspicuity on mpMRI, tumour location and association with mpMRI visibility is an underexplored facet of this field. Therefore, the objective of this systematic review and meta-analysis is to collate the extant evidence comparing MRI performance between different locations within the prostate in men with existing or suspected prostate cancer. This review will help clarify mechanisms that underpin whether a tumour is visible, and the prognostic implications of our findings.Methods and analysisThe databases MEDLINE, PubMed, Embase and Cochrane will be systematically searched for relevant studies. Eligible studies will be full-text English-language articles that examine the effect of zonal location on mpMRI conspicuity. Two reviewers will perform study selection, data extraction and quality assessment. A third reviewer will be involved if consensus is not achieved. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines will inform the methodology and reporting of the review. Study bias will be assessed using a modified Newcastle-Ottawa scale. A thematic approach will be used to synthesise key location-based factors associated with mpMRI conspicuity. A meta-analysis will be conducted to form a pooled value of the sensitivity and specificity of mpMRI at different tumour locations.Ethics and disseminationEthical approval is not required as it is a protocol for a systematic review. Findings will be disseminated through peer-reviewed publications and conference presentations.PROSPERO registration numberCRD42021228087.


2021 ◽  
pp. 1-6
Author(s):  
Sebastian Berg ◽  
Karl Heinrich Tully ◽  
Nicolas von Landenberg ◽  
Henning Bahlburg ◽  
Florian Roghmann ◽  
...  

<b><i>Introduction:</i></b> This study aimed to investigate the number of cores needed in a systematic biopsy (SB) in men with clinical suspicion of prostate cancer (PCa) but negative prebiopsy multiparametric magnetic resonance imaging and to test prostate-specific antigen (PSA) density as an indicator for reduced SB. <b><i>Methods:</i></b> Two hundred and seventy-four patients were analyzed, extracted from an institutional database. Detection rates of any PCa and clinically significant (CS) PCa for different reduced biopsy protocols were compared by using Fisher’s exact test. <b><i>Results:</i></b> In total, 12-core SB revealed PCa in 103 (37.6%) men. Detection rates of reduced biopsy protocols were 74 (27%, 6-core) and 82 (29.9%, 8-core). Regarding CSPCa, 12-core SB revealed a detection rate of 26 (9.5%). Reduced biopsy protocols detected less CSPCa: 15 (5.5%) and 18 (6.6%), respectively. All differences were statistically significant, <i>p</i> &#x3c; 0.05. PSA density ≥0.15 did not help to filter out men in whom a reduced biopsy may be sufficient. <b><i>Conclusions:</i></b> Twelve-core SB still has the highest detection rate of any PCa and CSPCa compared to reduced biopsy protocols. If the investigator and patient agree – based on individual risk calculation – to perform a biopsy, this SB should contain at least 12 cores regardless of PSA density.


Author(s):  
Caterina Gaudiano ◽  
Arianna Rustici ◽  
Beniamino Corcioni ◽  
Federica Ciccarese ◽  
Lorenzo Bianchi ◽  
...  

Multiparametric magnetic resonance imaging has been established as the most accurate non-invasive diagnostic imaging tool for detecting prostate cancer (PCa) in both the peripheral zone and the transition zone (TZ) using the PI-RADS (Prostate Imaging Reporting and Data System) version 2.1 released in 2019 as a guideline to reporting. Transition zone PCa remains the most difficult to diagnose due to a markedly heterogeneous background and a wide variety of atypical imaging presentations as well as other anatomical and pathological processes mimicking PCa. The aim of this paper was to present a spectrum of PCa in the TZ, as a guide for radiologists.


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