Literature DB >> 25747341

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review.

Guillaume Lemaître1, Robert Martí2, Jordi Freixenet3, Joan C Vilanova4, Paul M Walker5, Fabrice Meriaudeau6.   

Abstract

Prostate cancer is the second most diagnosed cancer of men all over the world. In the last few decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed to improve diagnosis. In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systems have been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field of research for the last 10 years. This survey aims to provide a comprehensive review of the state-of-the-art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aided system. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to the research community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Computer-Aided Detection; Computer-Aided Diagnosis; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy Imaging; Prostate cancer

Mesh:

Year:  2015        PMID: 25747341     DOI: 10.1016/j.compbiomed.2015.02.009

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  30 in total

1.  A Deep Learning-Based Approach for the Detection and Localization of Prostate Cancer in T2 Magnetic Resonance Images.

Authors:  Ruba Alkadi; Fatma Taher; Ayman El-Baz; Naoufel Werghi
Journal:  J Digit Imaging       Date:  2019-10       Impact factor: 4.056

2.  A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning.

Authors:  Naji Khosravan; Haydar Celik; Baris Turkbey; Elizabeth C Jones; Bradford Wood; Ulas Bagci
Journal:  Med Image Anal       Date:  2018-10-28       Impact factor: 8.545

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

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

4.  Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings.

Authors:  Rakesh Shiradkar; Soumya Ghose; Ivan Jambor; Pekka Taimen; Otto Ettala; Andrei S Purysko; Anant Madabhushi
Journal:  J Magn Reson Imaging       Date:  2018-05-07       Impact factor: 4.813

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

6.  Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI.

Authors:  Prathyush Chirra; Patrick Leo; Michael Yim; B Nicolas Bloch; Ardeshir R Rastinehad; Andrei Purysko; Mark Rosen; Anant Madabhushi; Satish E Viswanath
Journal:  J Med Imaging (Bellingham)       Date:  2019-06-14

Review 7.  Texture analysis of medical images for radiotherapy applications.

Authors:  Elisa Scalco; Giovanna Rizzo
Journal:  Br J Radiol       Date:  2016-11-25       Impact factor: 3.039

8.  Semi-automatic classification of prostate cancer on multi-parametric MR imaging using a multi-channel 3D convolutional neural network.

Authors:  Nader Aldoj; Steffen Lukas; Marc Dewey; Tobias Penzkofer
Journal:  Eur Radiol       Date:  2019-08-29       Impact factor: 5.315

Review 9.  HistoScanningTM to Detect and Characterize Prostate Cancer-a Review of Existing Literature.

Authors:  James S Wysock; Alex Xu; Clement Orczyk; Samir S Taneja
Journal:  Curr Urol Rep       Date:  2017-10-24       Impact factor: 3.092

10.  Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization.

Authors:  Jussi Toivonen; Ileana Montoya Perez; Parisa Movahedi; Harri Merisaari; Marko Pesola; Pekka Taimen; Peter J Boström; Jonne Pohjankukka; Aida Kiviniemi; Tapio Pahikkala; Hannu J Aronen; Ivan Jambor
Journal:  PLoS One       Date:  2019-07-08       Impact factor: 3.240

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