Literature DB >> 33289435

MRI radiomics in the prediction of therapeutic response to neoadjuvant therapy for locoregionally advanced rectal cancer: a systematic review.

Angelina Marina Di Re1,2, Yu Sun2, Purnima Sundaresan3,4, Eric Hau3,4,5, James Wei Tatt Toh1,4,5, Harriet Gee3,4, Michelle Or3, Annette Haworth2.   

Abstract

Introduction: The standard of care for locoregionally advanced rectal cancer is neoadjuvant therapy (NA CRT) prior to surgery, of which 10-30% experience a complete pathologic response (pCR). There has been interest in using imaging features, also known as radiomics features, to predict pCR and potentially avoid surgery. This systematic review aims to describe the spectrum of MRI studies examining high-performing radiomic features that predict NA CRT response. Areas covered: This article reviews the use of pre-therapy MRI in predicting NA CRT response for patients with locoregionally advanced rectal cancer (T3/T4 and/or N1+). The primary outcome was to identify MRI radiomic studies; secondary outcomes included the power and the frequency of use of radiomic features. Expert opinion: Advanced models incorporating multiple radiomics categories appear to be the most promising. However, there is a need for standardization across studies with regards to; the definition of NA CRT response, imaging protocols, and radiomics features incorporated. Further studies are needed to validate current radiomics models and to fully ascertain the value of MRI radiomics in the response prediction for locoregionally advanced rectal cancer.

Entities:  

Keywords:  Radiomics; long course neoadjuvant chemoradiotherapy; magnetic resonance imaging; rectal Cancer; therapeutic response

Year:  2021        PMID: 33289435     DOI: 10.1080/14737140.2021.1860762

Source DB:  PubMed          Journal:  Expert Rev Anticancer Ther        ISSN: 1473-7140            Impact factor:   4.512


  4 in total

1.  Combined artificial intelligence and radiologist model for predicting rectal cancer treatment response from magnetic resonance imaging: an external validation study.

Authors:  Natally Horvat; Harini Veeraraghavan; Caio S R Nahas; David D B Bates; Felipe R Ferreira; Junting Zheng; Marinela Capanu; James L Fuqua; Maria Clara Fernandes; Ramon E Sosa; Vetri Sudar Jayaprakasam; Giovanni G Cerri; Sergio C Nahas; Iva Petkovska
Journal:  Abdom Radiol (NY)       Date:  2022-06-16

2.  Predicting Neoadjuvant Treatment Response in Rectal Cancer Using Machine Learning: Evaluation of MRI-Based Radiomic and Clinical Models.

Authors:  Kent J Peterson; Matthew T Simpson; Melissa K Drezdzon; Aniko Szabo; Robin A Ausman; Andrew S Nencka; Paul M Knechtges; Carrie Y Peterson; Kirk A Ludwig; Timothy J Ridolfi
Journal:  J Gastrointest Surg       Date:  2022-10-21       Impact factor: 3.267

3.  Simulation CT-based radiomics for prediction of response after neoadjuvant chemo-radiotherapy in patients with locally advanced rectal cancer.

Authors:  Pierluigi Bonomo; Jairo Socarras Fernandez; Daniela Thorwarth; Marta Casati; Lorenzo Livi; Daniel Zips; Cihan Gani
Journal:  Radiat Oncol       Date:  2022-04-28       Impact factor: 4.309

4.  The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study.

Authors:  Bianca Boca Petresc; Cosmin Caraiani; Loredana Popa; Andrei Lebovici; Diana Sorina Feier; Carmen Bodale; Mircea Marian Buruian
Journal:  Biology (Basel)       Date:  2022-03-16
  4 in total

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