Journal Article10.1002/JMRI.27191
Quantitative Prostate MRI
Nicola Schieda,Christopher S. Lim,Fatemeh Zabihollahy,Jorge Abreu-Gomez,Satheesh Krishna,Sungmin Woo,Gerd Melkus,Eran Ukwatta,Baris Turkbey +8 more
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TL;DR: Advances in quantitative prostate MRI are reviewed, 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.
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Abstract: 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.
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Diffusion-weighted imaging in prostate cancer.
TL;DR: In this paper, Wang et al. discussed DWI acquisition problems in standard DWI using the single-shot echo-planar imaging sequence; discuss solutions that employ newly developed imaging techniques, state-of-the-art technologies, and sequences in DWI; and evaluate the current status of quantitative DWI for assessment of tumor aggressiveness in PC.
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The Quantitative Assessment of Using Multiparametric MRI for Prediction of Extraprostatic Extension in Patients Undergoing Radical Prostatectomy: A Systematic Review and Meta-Analysis
TL;DR: In this paper, the diagnostic performance of using quantitative assessment with multiparametric MRI (mpMRI) for prediction of extraprostatic extension (EPE) in patients with prostate cancer (PCa) was investigated.
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Comparison of single-shot EPI and multi-shot EPI in prostate DWI at 3.0 T
Tsutomu Tamada,Ayumu Kido,Yu Ueda,Mitsuru Takeuchi,Akihiko Kanki,Jaladhar Neelavalli,Akira Yamamoto +6 more
TL;DR: In this article , the authors compared the image quality and diagnostic performance for clinically significant prostate cancer (csPC) between single-shot EPI DWI and msEPI DWI.
Diagnostic Performance of Extraprostatic Extension Grading System for Detection of Extraprostatic Extension in Prostate Cancer: A Diagnostic Systematic Review and Meta-Analysis
Wei Liu,Wenwen Shang,Feng Lu,Yuanhui Sun,Jun Tian,Yiman Wu,Anding Dong +6 more
TL;DR: The EPE grading system demonstrated high sensitivity and moderate specificity, with a good inter-reader agreement, however, this scoring system needs more studies to be validated in clinical practice.
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PI-RADS Prostate Imaging - Reporting and Data System: 2015, Version 2
Jeffrey C. Weinreb,Jelle O. Barentsz,Peter L. Choyke,François Cornud,Masoom A. Haider,Katarzyna J. Macura,Daniel Margolis,Mitchell D. Schnall,Faina Shtern,Clare M. Tempany,Harriet C. Thoeny,Sadna Verma +11 more
TL;DR: The Prostate Imaging - Reporting and Data System Version 2 (PI-RADS™ v2) simplifies and standardizes terminology and content of reports, and provides assessment categories that summarize levels of suspicion or risk of clinically significant prostate cancer that can be used to assist selection of patients for biopsies and management.
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MRI-Targeted or Standard Biopsy for Prostate-Cancer Diagnosis
Veeru Kasivisvanathan,Antti Rannikko,Marcelo Borghi,Valeria Panebianco,Lance A. Mynderse,Markku H. Vaarala,Alberto Briganti,Lars Budäus,Giles Hellawell,Richard G. Hindley,Monique J. Roobol,Scott E. Eggener,Maneesh Ghei,Arnauld Villers,Franck Bladou,Geert M. Villeirs,Jaspal Virdi,S. Boxler,Grégoire Robert,Paras B. Singh,Wulphert Venderink,Boris Hadaschik,Alain Ruffion,Jim C. Hu,Daniel Margolis,Sebastien Crouzet,Laurence Klotz,Samir S. Taneja,Peter A. Pinto,Inderbir S. Gill,Clare Allen,Francesco Giganti,Alex Freeman,Stephen Morris,Shonit Punwani,Norman R. Williams,Chris Brew-Graves,Jonathan J Deeks,Yemisi Takwoingi,Mark Emberton,Caroline M. Moore +40 more
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Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2.
Baris Turkbey,Andrew B. Rosenkrantz,Masoom A. Haider,Anwar R. Padhani,Geert Villeirs,Katarzyna J. Macura,Clare M. Tempany,Peter L. Choyke,François Cornud,Daniel Margolis,Harriet C. Thoeny,Sadhna Verma,Jelle O. Barentsz,Jeffrey C. Weinreb +13 more
TL;DR: The PI-RADS Steering Committee, using a consensus-based process, has recommended several modifications to PI- RADS v2, maintaining the framework of assigning scores to individual sequences and using these scores to derive an overall assessment category.
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Repeatability and Reproducibility of Radiomic Features: A Systematic Review.
TL;DR: Investigations of feature repeatability and reproducibility are currently limited to a small number of cancer types and there was no emergent consensus regarding either shape metrics or textural features; however, coarseness and contrast appeared among the least reproducible features.
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