Literature DB >> 27133005

Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications.

Lizhi Liu1, Zhiqiang Tian2, Zhenfeng Zhang3, Baowei Fei4.   

Abstract

One in six men will develop prostate cancer in his lifetime. Early detection and accurate diagnosis of the disease can improve cancer survival and reduce treatment costs. Recently, imaging of prostate cancer has greatly advanced since the introduction of multiparametric magnetic resonance imaging (mp-MRI). Mp-MRI consists of T2-weighted sequences combined with functional sequences including dynamic contrast-enhanced MRI, diffusion-weighted MRI, and magnetic resonance spectroscopy imaging. Because of the big data and variations in imaging sequences, detection can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. To improve quantitative assessment of the disease, various computer-aided detection systems have been designed to help radiologists in their clinical practice. This review paper presents an overview of literatures on computer-aided detection of prostate cancer with mp-MRI, which include the technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.
Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  MR imaging; Prostate cancer; computer-aided detection; image quantification

Mesh:

Year:  2016        PMID: 27133005      PMCID: PMC5355004          DOI: 10.1016/j.acra.2016.03.010

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  181 in total

1.  Effects of cell volume fraction changes on apparent diffusion in human cells.

Authors:  A W Anderson; J Xie; J Pizzonia; R A Bronen; D D Spencer; J C Gore
Journal:  Magn Reson Imaging       Date:  2000-07       Impact factor: 2.546

2.  Efficient method for calculating kinetic parameters using T1-weighted dynamic contrast-enhanced magnetic resonance imaging.

Authors:  Kenya Murase
Journal:  Magn Reson Med       Date:  2004-04       Impact factor: 4.668

3.  3D ultrasound image segmentation using wavelet support vector machines.

Authors:  Hamed Akbari; Baowei Fei
Journal:  Med Phys       Date:  2012-06       Impact factor: 4.071

4.  A feasibility study of MR elastography in the diagnosis of prostate cancer at 3.0T.

Authors:  Saying Li; Min Chen; Wenchao Wang; Weifeng Zhao; Jianye Wang; Xuna Zhao; Cheng Zhou
Journal:  Acta Radiol       Date:  2011-04-01       Impact factor: 1.990

5.  Prostate MRI segmentation using learned semantic knowledge and graph cuts.

Authors:  Dwarikanath Mahapatra; Joachim M Buhmann
Journal:  IEEE Trans Biomed Eng       Date:  2013-11-06       Impact factor: 4.538

6.  Characterization of prostate cancer using T2 mapping at 3T: a multi-scanner study.

Authors:  A Hoang Dinh; R Souchon; C Melodelima; F Bratan; F Mège-Lechevallier; M Colombel; O Rouvière
Journal:  Diagn Interv Imaging       Date:  2014-12-23       Impact factor: 4.026

7.  Pilot study of a novel tool for input-free automated identification of transition zone prostate tumors using T2- and diffusion-weighted signal and textural features.

Authors:  Joseph N Stember; Fang-Ming Deng; Samir S Taneja; Andrew B Rosenkrantz
Journal:  J Magn Reson Imaging       Date:  2013-10-29       Impact factor: 4.813

8.  MR elastography of liver tumors: preliminary results.

Authors:  Sudhakar K Venkatesh; Meng Yin; James F Glockner; Naoki Takahashi; Philip A Araoz; Jayant A Talwalkar; Richard L Ehman
Journal:  AJR Am J Roentgenol       Date:  2008-06       Impact factor: 3.959

9.  Nonrigid point registration for 2D curves and 3D surfaces and its various applications.

Authors:  Hesheng Wang; Baowei Fei
Journal:  Phys Med Biol       Date:  2013-06-04       Impact factor: 3.609

10.  MAPS: A Quantitative Radiomics Approach for Prostate Cancer Detection.

Authors:  Andrew Cameron; Farzad Khalvati; Masoom A Haider; Alexander Wong
Journal:  IEEE Trans Biomed Eng       Date:  2015-10-01       Impact factor: 4.538

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  19 in total

1.  A semiautomatic approach for prostate segmentation in MR images using local texture classification and statistical shape modeling.

Authors:  Maysam Shahedi; Martin Halicek; Qinmei Li; Lizhi Liu; Zhenfeng Zhang; Sadhna Verma; David M Schuster; Baowei Fei
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2019-03-08

2.  Computer-aided diagnosis of prostate cancer with MRI.

Authors:  Baowei Fei
Journal:  Curr Opin Biomed Eng       Date:  2017-09

3.  Classification of Clinical Significance of MRI Prostate Findings Using 3D Convolutional Neural Networks.

Authors:  Alireza Mehrtash; Alireza Sedghi; Mohsen Ghafoorian; Mehdi Taghipour; Clare M Tempany; William M Wells; Tina Kapur; Parvin Mousavi; Purang Abolmaesumi; Andriy Fedorov
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2017-03-03

4.  Computer-aided diagnosis prior to conventional interpretation of prostate mpMRI: an international multi-reader study.

Authors:  Matthew D Greer; Nathan Lay; Joanna H Shih; Tristan Barrett; Leonardo Kayat Bittencourt; Samuel Borofsky; Ismail Kabakus; Yan Mee Law; Jamie Marko; Haytham Shebel; Francesca V Mertan; Maria J Merino; Bradford J Wood; Peter A Pinto; Ronald M Summers; Peter L Choyke; Baris Turkbey
Journal:  Eur Radiol       Date:  2018-04-12       Impact factor: 5.315

5.  Classification of suspicious lesions on prostate multiparametric MRI using machine learning.

Authors:  Deukwoo Kwon; Isildinha M Reis; Adrian L Breto; Yohann Tschudi; Nicole Gautney; Olmo Zavala-Romero; Christopher Lopez; John C Ford; Sanoj Punnen; Alan Pollack; Radka Stoyanova
Journal:  J Med Imaging (Bellingham)       Date:  2018-09-06

Review 6.  Integration of advances in social media and mHealth technology are pivotal to successful cancer prevention and control.

Authors:  D Peter O'Leary; Amir Zaheer; H Paul Redmond; Mark A Corrigan
Journal:  Mhealth       Date:  2016-10-20

Review 7.  Artificial intelligence at the intersection of pathology and radiology in prostate cancer.

Authors:  Stephnie A Harmon; Sena Tuncer; Thomas Sanford; Peter L Choyke; Barış Türkbey
Journal:  Diagn Interv Radiol       Date:  2019-05       Impact factor: 2.630

Review 8.  [Artificial intelligence and radiomics in MRI-based prostate diagnostics].

Authors:  Charlie Alexander Hamm; Nick Lasse Beetz; Lynn Jeanette Savic; Tobias Penzkofer
Journal:  Radiologe       Date:  2020-01       Impact factor: 0.635

9.  Rapid Assessment of Acute Ischemic Stroke by Computed Tomography Using Deep Convolutional Neural Networks.

Authors:  Peng-Hsiang Hung; Daw-Tung Lin; Chung-Ming Lo
Journal:  J Digit Imaging       Date:  2021-05-07       Impact factor: 4.903

10.  Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers.

Authors:  Valentina Giannini; Simone Mazzetti; Giovanni Cappello; Valeria Maria Doronzio; Lorenzo Vassallo; Filippo Russo; Alessandro Giacobbe; Giovanni Muto; Daniele Regge
Journal:  Diagnostics (Basel)       Date:  2021-05-28
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