Literature DB >> 29732440

Computer-aided diagnosis of prostate cancer with MRI.

Baowei Fei1.   

Abstract

Multi-parametric magnetic resonance imaging (mp-MRI) has an increasingly important role in the diagnosis of prostate cancer. Due to the large amount of data and variations in mp-MRI, tumor detection can be affected by multiple factors, such as the observer's clinical experience, image quality, and appearance of the lesions. In order to improve the quantitative assessment of the disease and reduce the reporting time, various computer-aided diagnosis (CAD) systems have been designed to help radiologists identify lesions. This manuscript presents an overview of the literature regarding prostate CAD using mp-MRI, while focusing on the studies of the most recent five years. Current prostate CAD technologies and their utilization are discussed in this review.

Entities:  

Year:  2017        PMID： 29732440      PMCID： PMC5931723          DOI： 10.1016/j.cobme.2017.09.009

Source DB:  PubMed          Journal:  Curr Opin Biomed Eng        ISSN： 2468-4511


  48 in total

1.  Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI.

Authors:  Emilie Niaf; Olivier Rouvière; Florence Mège-Lechevallier; Flavie Bratan; Carole Lartizien
Journal:  Phys Med Biol       Date:  2012-05-29       Impact factor: 3.609

2.  Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 Tesla endorectal, in vivo T2-weighted MR imagery.

Authors:  Satish E Viswanath; Nicholas B Bloch; Jonathan C Chappelow; Robert Toth; Neil M Rofsky; Elizabeth M Genega; Robert E Lenkinski; Anant Madabhushi
Journal:  J Magn Reson Imaging       Date:  2012-02-15       Impact factor: 4.813

3.  Computer-assisted analysis of peripheral zone prostate lesions using T2-weighted and dynamic contrast enhanced T1-weighted MRI.

Authors:  Pieter C Vos; Thomas Hambrock; Jelle O Barenstz; Henkjan J Huisman
Journal:  Phys Med Biol       Date:  2010-03-02       Impact factor: 3.609

4.  Is it possible to model the risk of malignancy of focal abnormalities found at prostate multiparametric MRI?

Authors:  Olivier Rouvière; Matthieu Papillard; Nicolas Girouin; Romain Boutier; Muriel Rabilloud; Benjamin Riche; Florence Mège-Lechevallier; Marc Colombel; Albert Gelet
Journal:  Eur Radiol       Date:  2012-01-07       Impact factor: 5.315

5.  Multiparametric magnetic resonance imaging of the prostate with computer-aided detection: experienced observer performance study.

Authors:  Valentina Giannini; Simone Mazzetti; Enrico Armando; Silvia Carabalona; Filippo Russo; Alessandro Giacobbe; Giovanni Muto; Daniele Regge
Journal:  Eur Radiol       Date:  2017-04-06       Impact factor: 5.315

6.  Computer-aided detection of prostate cancer in MRI.

Authors:  Geert Litjens; Oscar Debats; Jelle Barentsz; Nico Karssemeijer; Henkjan Huisman
Journal:  IEEE Trans Med Imaging       Date:  2014-05       Impact factor: 10.048

7.  Enhanced Multi-Protocol Analysis via Intelligent Supervised Embedding (EMPrAvISE): Detecting Prostate Cancer on Multi-Parametric MRI.

Authors:  Satish Viswanath; B Nicolas Bloch; Jonathan Chappelow; Pratik Patel; Neil Rofsky; Robert Lenkinski; Elisabeth Genega; Anant Madabhushi
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2011-03-04

8.  Anatomic segmentation improves prostate cancer detection with artificial neural networks analysis of 1H magnetic resonance spectroscopic imaging.

Authors:  Lukasz Matulewicz; Jacobus F A Jansen; Louisa Bokacheva; Hebert Alberto Vargas; Oguz Akin; Samson W Fine; Amita Shukla-Dave; James A Eastham; Hedvig Hricak; Jason A Koutcher; Kristen L Zakian
Journal:  J Magn Reson Imaging       Date:  2013-11-15       Impact factor: 4.813

9.  Computer-assisted diagnosis of prostate cancer using DCE-MRI data: design, implementation and preliminary results.

Authors:  Philippe Puech; Nacim Betrouni; Nasr Makni; Anne-Sophie Dewalle; Arnauld Villers; Laurent Lemaitre
Journal:  Int J Comput Assist Radiol Surg       Date:  2008-10-21       Impact factor: 2.924

10.  ESUR prostate MR guidelines 2012.

Authors:  Jelle O Barentsz; Jonathan Richenberg; Richard Clements; Peter Choyke; Sadhna Verma; Geert Villeirs; Olivier Rouviere; Vibeke Logager; Jurgen J Fütterer
Journal:  Eur Radiol       Date:  2012-02-10       Impact factor: 5.315
View more
  4 in total

1.  Detection of Dominant Intra-prostatic Lesions in Patients With Prostate Cancer Using an Artificial Neural Network and MR Multi-modal Radiomics Analysis.

Authors:  Hassan Bagher-Ebadian; Branislava Janic; Chang Liu; Milan Pantelic; David Hearshen; Mohamed Elshaikh; Benjamin Movsas; Indrin J Chetty; Ning Wen
Journal:  Front Oncol       Date:  2019-11-26       Impact factor: 6.244

Review 2.  Evolution of prostate MRI: from multiparametric standard to less-is-better and different-is better strategies.

Authors:  Rossano Girometti; Lorenzo Cereser; Filippo Bonato; Chiara Zuiani
Journal:  Eur Radiol Exp       Date:  2019-01-28

3.  Effect of domain knowledge encoding in CNN model architecture-a prostate cancer study using mpMRI images.

Authors:  Piotr Sobecki; Rafał Jóźwiak; Katarzyna Sklinda; Artur Przelaskowski
Journal:  PeerJ       Date:  2021-03-09       Impact factor: 2.984

Review 4.  Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities.

Authors:  Huanye Li; Chau Hung Lee; David Chia; Zhiping Lin; Weimin Huang; Cher Heng Tan
Journal:  Diagnostics (Basel)       Date:  2022-01-24
  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.