: Prostate MRI is reported in clinical practice using the Prostate Imaging and Data Reporting System (PI-RADS). PI-RADS aims to standardize, as much as possible, the acquisition, interpretation, reporting, and ultimately the performance of prostate MRI. PI-RADS relies upon mainly subjective analysis of MR imaging findings, with very few incorporated quantitative features. The shortcomings of PI-RADS are mainly: low-to-moderate interobserver agreement and modest accuracy for detection of clinically significant tumors in the transition zone. The use of a more quantitative analysis of prostate MR imaging findings is therefore of interest. Quantitative MR imaging features including: tumor size and volume, tumor length of capsular contact, tumor apparent diffusion coefficient (ADC) metrics, tumor T1 and T2 relaxation times, tumor shape, and texture analyses have all shown value for improving characterization of observations detected on prostate MRI and for differentiating between tumors by their pathological grade and stage. Quantitative analysis may therefore improve diagnostic accuracy for detection of cancer and could be a noninvasive means to predict patient prognosis and guide management. Since quantitative analysis of prostate MRI is less dependent on an individual users' assessment, it could also improve interobserver agreement. Semi- and fully automated analysis of quantitative (radiomic) MRI features using artificial neural networks represent the next step in quantitative prostate MRI and are now being actively studied. Validation, through high-quality multicenter studies assessing diagnostic accuracy for clinically significant prostate cancer detection, in the domain of quantitative prostate MRI is needed. This article reviews advances in quantitative prostate MRI, highlighting the strengths and limitations of existing and emerging techniques, as well as discussing opportunities and challenges for evaluation of prostate MRI in clinical practice when using quantitative assessment. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
Abstract Purpose To determine the diagnostic performance of texture analysis of prostate MRI for the diagnosis of prostate cancer among Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions. Materials and Methods Forty-three patients with at least 1 PI-RADS 3 lesion on prostate MRI performed between June 2016 and January 2019 were retrospectively included. Reference standard was pathological analysis of radical prostatectomy specimens or MRI-targeted biopsies. Texture analysis extractio...
Last. Andreas Wienke(MLU: Martin Luther University of Halle-Wittenberg)H-Index: 30
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Abstract Background Reported data regarding the associations between apparent diffusion coefficient (ADC) of diffusion-weighted imaging (DWI) and Gleason score in prostate cancer (PC) are inconsistent. Objective The aim of the present systematic review was to analyze relationships between ADC and Gleason score in PC. Design, setting, and participants MEDLINE library, SCOPUS, and EMBASE databases were screened for relationships between ADC and Gleason score in PC up to April 2018. Overall, 39 stu...
OBJECTIVE: The purpose was to review the diagnostic performance of the length of tumor capsular contact (LCC) on magnetic resonance imaging (MRI) for detecting prostate cancer extraprostatic extension (EPE). MATERIALS AND METHODS: PubMed and EMBASE databases were searched up to March 24, 2019. We included diagnostic accuracy studies that evaluated LCC on MRI for EPE detection using radical prostatectomy specimen histopathology as the reference standard. Quality of studies was assessed using the ...
#2Eranga Ukwatta(U of G: University of Guelph)H-Index: 14
Last. Nicola Schieda(U of O: University of Ottawa)H-Index: 28
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Background: Accurate detection and localization of prostate cancer (PCa) in men undergoing prostate MRI is a fundamental step for future targeted prostate biopsies and treatment planning. Fully automated localization of peripheral zone (PZ) PCa using the apparent diffusion coefficient (ADC) map might be clinically useful. Purpose/Hypothesis: To describe automated localization of PCa in the PZ on ADC map MR images using an ensemble U-Net-based model. Study Type: Retrospective, case–control. Popul...
Abstract Background Acquiring multiparametric magnetic resonance images of the prostate is not a simple "push-button" approach. Objective To show how image acquisition of prostate multiparametric Magnetic Resonance Imaging (mpMRI) can be optimized. Design, setting, and participants Image protocols, magnetic field strength choice, and the use of receiver coils are discussed. In addition, patient preparation and the recognition, prevention, and mitigation of artifacts are evaluated. Surgical proce...
PURPOSE: The PRECISE score estimates the likelihood of radiological progression in patients on active surveillance (AS) for prostate cancer (PCa) with serial multiparametric magnetic resonance imaging (mpMRI). A PRECISE score of 1 or 2 denotes radiological regression, PRECISE 3 indicates stability and PRECISE 4 or 5 implies progression. We evaluated the inter-reader reproducibility of different apparent diffusion coefficient (ADC) calculations and their relationship to the PRECISE score. MATERIA...
#2Daniel Walker(U of O: University of Ottawa)H-Index: 5
Last. Nicola Schieda(U of O: University of Ottawa)H-Index: 28
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To compare observation size and apparent diffusion coefficient (ADC) values in Prostate Imaging Reporting and Data System (PI-RADS) v2.1 category 4 and 5 observations to adverse pathological features. With institutional review board approval, 267 consecutive men with 3-T MRI before radical prostatectomy (RP) between 2012 and 2018 were evaluated by two blinded radiologists who assigned PI-RADS v2.1 scores. Discrepancies were resolved by consensus. A third blinded radiologist measured observation ...
Objective: To explore the value of a radiomics approach based on MRI texture analysis (TA) in identifying intraductal carcinoma of the prostate gland (IDCP). Methods: MRI images of 56 patients with pathological proven prostate cancer were analyzed retrospectively, including 31 patients with pure prostate adenocarcinoma and 25 patients with IDCP component in the prostate adenocarcinoma lesions. After imaging features of lesions were analyzed, then enhanced arterial and venous phase images were im...
#1Zhiwei Wang(HUST: Huazhong University of Science and Technology)H-Index: 7
#2Yi Lin(HUST: Huazhong University of Science and Technology)H-Index: 4
Last. Xin Yang(HUST: Huazhong University of Science and Technology)H-Index: 18
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Abstract The availability of a large amount of annotated data is critical for many medical image analysis applications, in particular for those relying on deep learning methods which are known to be data-hungry. However, annotated medical data, especially multimodal data, is often scarce and costly to obtain. In this paper, we address the problem of synthesizing multi-parameter magnetic resonance imaging data (i.e. mp-MRI), which typically consists of Apparent Diffusion Coefficient (ADC) and T2-...
#1Yachao Liu(Chinese PLA General Hospital)H-Index: 1
#2Yanliang Dong(Chinese PLA General Hospital)H-Index: 1
Last. Baixuan Xu(Chinese PLA General Hospital)H-Index: 6
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Background null We aimed to compare the diagnostic performance of 18 F-DCFPyL positron emission tomography (PET) and multiparameter magnetic resonance imaging (mp-MRI) in detecting transition zone (TZ) prostate cancer (PCa). null Methods null This retrospective study included 20 patients who underwent 18 F-DCFPyL PET/MRI and 32 patients who underwent 18 F-DCFPyL PET/CT and MRI from January 2019 to June 2020. All patients had TZ lesions and underwent prostate biopsies. One senior (reader 1) and o...
Last. Akira Yamamoto(Kawasaki Medical School)H-Index: 178
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Diffusion-weighted imaging (DWI), a key component in multiparametric MRI (mpMRI), is useful for tumor detection and localization in clinically significant prostate cancer (csPCa). The Prostate Imaging Reporting and Data System versions 2 and 2.1 (PI-RADS v2 and PI-RADS v2.1) emphasize the role of DWI in determining PIRADS Assessment Category in each of the transition and peripheral zones. In addition, several recent studies have demonstrated comparable performance of abbreviated biparametric MRI...
Last. Nicola Schieda(U of O: University of Ottawa)H-Index: 28
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Purpose null To evaluate if machine learning (ML) of radiomic features extracted from apparent diffusion coefficient (ADC) and T2-weighted (T2W) MRI can predict prostate cancer (PCa) diagnosis in Prostate Imaging-Reporting and Data System (PI-RADS) version 2.1 category 3 lesions. null Methods null This multi-institutional review board-approved retrospective case-control study evaluated 158 men with 160 PI-RADS category 3 lesions (79 peripheral zone, 81 transition zone) diagnosed at 3-Tesla MRI w...
#2Nicola Schieda(U of O: University of Ottawa)H-Index: 28
Last. Eranga Ukwatta(U of G: University of Guelph)H-Index: 14
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Purpose null Accurate detection of transition zone (TZ) prostate cancer (PCa) on magnetic resonance imaging (MRI) remains challenging using clinical subjective assessment due to overlap between PCa and benign prostatic hyperplasia (BPH). The objective of this paper is to describe a deep-learning-based framework for fully automated detection of PCa in the TZ using T2-weighted (T2W) and apparent diffusion coefficient (ADC) map MR images. null Method null This was a single-center IRB-approved cross...
Objectives:To assess the apparent diffusion coefficient (ADC) values and the T1 and T2 values derived from nonenhanced (NE) and contrast-enhanced (CE) magnetic resonance fingerprinting (MRF) in the...
Abstract null null Purpose null We aimed to evaluate the diagnostic performance of the European Society of Urogenital Radiology (ESUR) scoring system for detection of extraprostatic extension (EPE) in prostate cancer (PCa) by performing a meta-analysis. null null null Materials and Methods null A literature search of MEDLINE, EMBASE, Cochrane Library, Web of Science, and Google Scholar was performed to identify relevant studies from January 2012 to December 2020. We included diagnostic accuracy ...
BACKGROUND Currently, it is necessary to investigate how to combine biparametric magnetic resonance imaging (bpMRI) with various clinical parameters for the detection of clinically significant prostate cancer (csPCa). PURPOSE To develop a multivariate prebiopsy nomogram using clinical and bpMRI parameters for estimating the probability of csPCa. STUDY TYPE Retrospective, single-center study. SUBJECTS Two hundred and twenty-six patients who underwent targeted biopsy (TBx) for the MRI-suspected in...
#2Nicola Schieda(U of O: University of Ottawa)H-Index: 28
Last. Gerd Melkus(U of O: University of Ottawa)H-Index: 19
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BACKGROUND Diffusion weighted imaging (DWI) is fundamental for prostate cancer (PCa) detection with MRI; however, limited by susceptibility artifact from hip prosthesis. PURPOSE To evaluate image quality and ability to detect PCa with quantitative T2-mapping and DWI in men with hip prosthesis undergoing prostate MRI. STUDY TYPE Prospective, cross-sectional study. POPULATION Thirty consecutive men with hip replacement (18 unilateral, 12 bilateral) undergoing prostate MRI from 2019 to 2021. FIELD ...
Nearly half of patients with prostate cancer (PCa) harbour low- or intermediate-risk disease considered suitable for active surveillance (AS). However, up to 44% of patients discontinue AS within the first five years, highlighting the unmet clinical need for robust baseline risk-stratification tools that enable timely and accurate prediction of tumour progression. In this proof-of-concept study, we sought to investigate the added value of MRI-derived radiomic features to standard-of-care clinica...
Last. Annette Haworth(USYD: University of Sydney)H-Index: 23
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Magnetic resonance imaging (MRI) is increasingly used in the management of prostate cancer (PCa). Quantitative MRI (qMRI) parameters, derived from multi-parametric MRI, provide indirect measures of tumour characteristics such as cellularity, angiogenesis and hypoxia. Using Artificial Intelligence (AI), relevant information and patterns can be efficiently identified in these complex data to develop quantitative imaging biomarkers (QIBs) of tumour function and biology. Such QIBs have already demon...
