Literature DB >> 34826772

Rectal MRI radiomics for predicting pathological complete response: Where we are.

Joao Miranda1, Gary Xia Vern Tan2, Maria Clara Fernandes3, Onur Yildirim4, John A Sims5, Jose de Arimateia Batista Araujo-Filho6, Felipe Augusto de M Machado7, Antonildes N Assuncao-Jr8, Cesar Higa Nomura9, Natally Horvat10.   

Abstract

Radiomics using rectal MRI radiomics has emerged as a promising approach in predicting pathological complete response. In this study, we present a typical pipeline of a radiomics analysis and review recent studies, exploring applications, development of radiomics methodologies and model construction in pCR prediction. Finally, we will offer our opinion about the future and discuss the next steps of rectal MRI radiomics for predicting pCR.
Copyright © 2021 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Machine learning; Magnetic resonance imaging; Neoadjuvant therapy; Radiomics; Rectal neoplasms

Mesh：

Year:  2021        PMID： 34826772      PMCID： PMC9119743          DOI： 10.1016/j.clinimag.2021.10.005

Source DB:  PubMed          Journal:  Clin Imaging        ISSN： 0899-7071            Impact factor:   2.420


  61 in total

1.  MRI prediction of pathological response in locally advanced rectal cancer: when apparent diffusion coefficient radiomics meets conventional volumetry.

Authors:  A Palmisano; A Di Chiara; A Esposito; P M V Rancoita; C Fiorino; P Passoni; L Albarello; R Rosati; A Del Maschio; F De Cobelli
Journal:  Clin Radiol       Date:  2020-07-22       Impact factor: 2.350

2.  Prognostic modeling for patients with colorectal liver metastases incorporating FDG PET radiomic features.

Authors:  Arman Rahmim; Kirstine P Bak-Fredslund; Saeed Ashrafinia; Lijun Lu; C Ross Schmidtlein; Rathan M Subramaniam; Anni Morsing; Susanne Keiding; Jacob Horsager; Ole L Munk
Journal:  Eur J Radiol       Date:  2019-02-08       Impact factor: 3.528

3.  Digital mammographic tumor classification using transfer learning from deep convolutional neural networks.

Authors:  Benjamin Q Huynh; Hui Li; Maryellen L Giger
Journal:  J Med Imaging (Bellingham)       Date:  2016-08-22

4.  Magnetic resonance imaging-detected tumor response for locally advanced rectal cancer predicts survival outcomes: MERCURY experience.

Authors:  Uday B Patel; Fiona Taylor; Lennart Blomqvist; Christopher George; Hywel Evans; Paris Tekkis; Philip Quirke; David Sebag-Montefiore; Brendan Moran; Richard Heald; Ashley Guthrie; Nicola Bees; Ian Swift; Kjell Pennert; Gina Brown
Journal:  J Clin Oncol       Date:  2011-08-29       Impact factor: 44.544

5.  Predicting the tumor response to chemoradiotherapy for rectal cancer: Model development and external validation using MRI radiomics.

Authors:  Philippe Bulens; Alice Couwenberg; Martijn Intven; Annelies Debucquoy; Vincent Vandecaveye; Eric Van Cutsem; André D'Hoore; Albert Wolthuis; Pritam Mukherjee; Olivier Gevaert; Karin Haustermans
Journal:  Radiother Oncol       Date:  2019-08-17       Impact factor: 6.280

6.  Pathologic Complete Response in Rectal Cancer: Can We Detect It? Lessons Learned From a Proposed Randomized Trial of Watch-and-Wait Treatment of Rectal Cancer.

Authors:  Sergio Carlos Nahas; Caio Sergio Rizkallah Nahas; Carlos Frederico Sparapan Marques; Ulysses Ribeiro; Guilherme Cutait Cotti; Antonio Rocco Imperiale; Fernanda Cunha Capareli; Andre Tsin Chih Chen; Paulo M Hoff; Ivan Cecconello
Journal:  Dis Colon Rectum       Date:  2016-04       Impact factor: 4.585

7.  Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer.

Authors:  Jie Fu; Xinran Zhong; Ning Li; Ritchell Van Dams; John Lewis; Kyunghyun Sung; Ann C Raldow; Jing Jin; X Sharon Qi
Journal:  Phys Med Biol       Date:  2020-04-02       Impact factor: 3.609

8.  Role of biopsies in patients with residual rectal cancer following neoadjuvant chemoradiation after downsizing: can they rule out persisting cancer?

Authors:  R O Perez; A Habr-Gama; G V Pereira; P B Lynn; P A Alves; I Proscurshim; V Rawet; J Gama-Rodrigues
Journal:  Colorectal Dis       Date:  2012-06       Impact factor: 3.788

9.  Predicting pathological complete response by comparing MRI-based radiomics pre- and postneoadjuvant radiotherapy for locally advanced rectal cancer.

Authors:  Yuqiang Li; Wenxue Liu; Qian Pei; Lilan Zhao; Cenap Güngör; Hong Zhu; Xiangping Song; Chenglong Li; Zhongyi Zhou; Yang Xu; Dan Wang; Fengbo Tan; Pei Yang; Haiping Pei
Journal:  Cancer Med       Date:  2019-10-22       Impact factor: 4.452

10.  Clinical utility of radiomics at baseline rectal MRI to predict complete response of rectal cancer after chemoradiation therapy.

Authors:  Iva Petkovska; Florent Tixier; Eduardo J Ortiz; Jennifer S Golia Pernicka; Viktoriya Paroder; David D Bates; Natally Horvat; James Fuqua; Juliana Schilsky; Marc J Gollub; Julio Garcia-Aguilar; Harini Veeraraghavan
Journal:  Abdom Radiol (NY)       Date:  2020-11
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