Literature DB >> 30316465

Metastasis Reporting and Data System for Prostate Cancer in Practice.

Anwar R Padhani1, Nina Tunariu2.   

Abstract

Whole-body MR imaging incorporating diffusion-weighted imaging is increasingly recommended as a radiation-free imaging method for assessing bone and soft tissue pathology, and for evaluating response to therapy. Metastasis Reporting and Data System for prostate cancer provides the minimum standards for whole-body MR imaging with diffusion imaging regarding image acquisitions, interpretation, and reporting of baseline and follow-up monitoring examinations of patients with advanced, metastatic cancers, focusing on prostate cancer. This article summarizes and illustrates Metastasis Reporting and Data System using a case-based approach in patients with advanced prostate cancer.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Imaging standard; MET-RADS; Prostate cancer; Systematic reporting; Therapy response; Whole-body MR imaging

Mesh:

Year:  2018        PMID: 30316465     DOI: 10.1016/j.mric.2018.06.004

Source DB:  PubMed          Journal:  Magn Reson Imaging Clin N Am        ISSN: 1064-9689            Impact factor:   2.266


  3 in total

1.  Semi-automatic quantitative analysis of the pelvic bony structures on apparent diffusion coefficient maps based on deep learning: establishment of reference ranges.

Authors:  Xiang Liu; Chao Han; Ziying Lin; Zhaonan Sun; Yaofeng Zhang; Xiangpeng Wang; Xiaodong Zhang; Xiaoying Wang
Journal:  Quant Imaging Med Surg       Date:  2022-01

2.  Detection and Segmentation of Pelvic Bones Metastases in MRI Images for Patients With Prostate Cancer Based on Deep Learning.

Authors:  Xiang Liu; Chao Han; Yingpu Cui; Tingting Xie; Xiaodong Zhang; Xiaoying Wang
Journal:  Front Oncol       Date:  2021-11-29       Impact factor: 6.244

3.  Whole-body magnetic resonance imaging (WB-MRI) reporting with the METastasis Reporting and Data System for Prostate Cancer (MET-RADS-P): inter-observer agreement between readers of different expertise levels.

Authors:  Paola Pricolo; Eleonora Ancona; Paul Summers; Jorge Abreu-Gomez; Sarah Alessi; Barbara Alicja Jereczek-Fossa; Ottavio De Cobelli; Franco Nolè; Giuseppe Renne; Massimo Bellomi; Anwar Roshanali Padhani; Giuseppe Petralia
Journal:  Cancer Imaging       Date:  2020-10-27       Impact factor: 3.909

  3 in total

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