Literature DB >> 30762223

Multiparametric MRI and radiomics in prostate cancer: a review.

Yu Sun1,2, Hayley M Reynolds3, Bimal Parameswaran4, Darren Wraith5, Mary E Finnegan6,7, Scott Williams3, Annette Haworth8.   

Abstract

Multiparametric MRI (mpMRI) is an imaging modality that combines anatomical MR imaging with one or more functional MRI sequences. It has become a versatile tool for detecting and characterising prostate cancer (PCa). The traditional role of mpMRI was confined to PCa staging, but due to the advanced imaging techniques, its role has expanded to various stages in clinical practises including tumour detection, disease monitor during active surveillance and sequential imaging for patient follow-up. Meanwhile, with the growing speed of data generation and the increasing volume of imaging data, it is highly demanded to apply computerised methods to process mpMRI data and extract useful information. Hence quantitative analysis for imaging data using radiomics has become an emerging paradigm. The application of radiomics approaches in prostate cancer has not only enabled automatic localisation of the disease but also provided a non-invasive solution to assess tumour biology (e.g. aggressiveness and the presence of hypoxia). This article reviews mpMRI and its expanding role in PCa detection, staging and patient management. Following that, an overview of prostate radiomics will be provided, with a special focus on its current applications as well as its future directions.

Entities:  

Keywords:  Heterogeneity; Machine learning; Multiparametric MRI; Prostate cancer; Radiomics; Tumour

Mesh:

Year:  2019        PMID: 30762223     DOI: 10.1007/s13246-019-00730-z

Source DB:  PubMed          Journal:  Australas Phys Eng Sci Med        ISSN: 0158-9938            Impact factor:   1.430


  27 in total

1.  Preoperative prediction of pelvic lymph nodes metastasis in prostate cancer using an ADC-based radiomics model: comparison with clinical nomograms and PI-RADS assessment.

Authors:  Xiang Liu; Xiangpeng Wang; Yaofeng Zhang; Zhaonan Sun; Xiaodong Zhang; Xiaoying Wang
Journal:  Abdom Radiol (NY)       Date:  2022-06-28

Review 2.  Pancreas image mining: a systematic review of radiomics.

Authors:  Bassam M Abunahel; Beau Pontre; Haribalan Kumar; Maxim S Petrov
Journal:  Eur Radiol       Date:  2020-11-05       Impact factor: 5.315

3.  Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer.

Authors:  Harri Merisaari; Pekka Taimen; Rakesh Shiradkar; Otto Ettala; Marko Pesola; Jani Saunavaara; Peter J Boström; Anant Madabhushi; Hannu J Aronen; Ivan Jambor
Journal:  Magn Reson Med       Date:  2019-11-08       Impact factor: 4.668

4.  Multiple analyses suggests texture features can indicate the presence of tumor in the prostate tissue.

Authors:  Sérgio Augusto Santana Souza; Leonardo Oliveira Reis; Allan Felipe Fattori Alves; Letícia Cotinguiba Silva; Maria Clara Korndorfer Medeiros; Danilo Leite Andrade; Athanase Billis; João Luiz Amaro; Daniel Lahan Martins; André Petean Trindade; José Ricardo Arruda Miranda; Diana Rodrigues Pina
Journal:  Phys Eng Sci Med       Date:  2022-03-24

5.  [10-Year mortality, disease progression, and treatment-related side effects in men with localized prostate cancer from the ProtecT Randomised Controlled Trial, analyzed according to treatment received].

Authors:  Simon K B Spohn; Anca-Ligia Grosu
Journal:  Strahlenther Onkol       Date:  2021-05-07       Impact factor: 3.621

6.  Factors Influencing Variability in the Performance of Multiparametric Magnetic Resonance Imaging in Detecting Clinically Significant Prostate Cancer: A Systematic Literature Review.

Authors:  Armando Stabile; Francesco Giganti; Veeru Kasivisvanathan; Gianluca Giannarini; Caroline M Moore; Anwar R Padhani; Valeria Panebianco; Andrew B Rosenkrantz; Georg Salomon; Baris Turkbey; Geert Villeirs; Jelle O Barentsz
Journal:  Eur Urol Oncol       Date:  2020-03-17

7.  Development and validation of a multiparametric MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer.

Authors:  Liuhui Zhang; Donggen Jiang; Chujie Chen; Xiangwei Yang; Hanqi Lei; Zhuang Kang; Hai Huang; Jun Pang
Journal:  Br J Radiol       Date:  2021-09-29       Impact factor: 3.039

Review 8.  The Biological Meaning of Radiomic Features.

Authors:  Michal R Tomaszewski; Robert J Gillies
Journal:  Radiology       Date:  2021-01-05       Impact factor: 11.105

9.  Value of radiomics model based on multi-parametric magnetic resonance imaging in predicting epidermal growth factor receptor mutation status in patients with lung adenocarcinoma.

Authors:  Yuze Wang; Qi Wan; Xiaoying Xia; Jianfeng Hu; Yuting Liao; Peng Wang; Yu Peng; Hongyan Liu; Xinchun Li
Journal:  J Thorac Dis       Date:  2021-06       Impact factor: 2.895

10.  Co-Clinical Imaging Resource Program (CIRP): Bridging the Translational Divide to Advance Precision Medicine.

Authors:  Kooresh I Shoghi; Cristian T Badea; Stephanie J Blocker; Thomas L Chenevert; Richard Laforest; Michael T Lewis; Gary D Luker; H Charles Manning; Daniel S Marcus; Yvonne M Mowery; Stephen Pickup; Ann Richmond; Brian D Ross; Anna E Vilgelm; Thomas E Yankeelov; Rong Zhou
Journal:  Tomography       Date:  2020-09
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