Literature DB >> 34825946

A primer on texture analysis in abdominal radiology.

Natally Horvat1, Joao Miranda2, Maria El Homsi1, Jacob J Peoples3, Niamh M Long1, Amber L Simpson3,4, Richard K G Do5.   

Abstract

The number of publications on texture analysis (TA), radiomics, and radiogenomics has been growing exponentially, with abdominal radiologists aiming to build new prognostic or predictive biomarkers for a wide range of clinical applications including the use of oncological imaging to advance the field of precision medicine. TA is specifically concerned with the study of the variation of pixel intensity values in radiological images. Radiologists aim to capture pixel variation in radiological images to deliver new insights into tumor biology that cannot be derived from visual inspection alone. TA remains an active area of investigation and requires further standardization prior to its clinical acceptance and applicability. This review is for radiologists interested in this rapidly evolving field, who are thinking of performing research or want to better interpret results in this arena. We will review the main concepts in TA, workflow processes, and existing challenges and steps to overcome them, as well as look at publications in body imaging with external validation.
© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Computed tomography; Machine learning; Magnetic resonance imaging; Positron emission tomography; Radiomics; Texture analysis

Mesh:

Year:  2021        PMID: 34825946     DOI: 10.1007/s00261-021-03359-3

Source DB:  PubMed          Journal:  Abdom Radiol (NY)


  61 in total

Review 1.  Prostate MRI radiomics: A systematic review and radiomic quality score assessment.

Authors:  Arnaldo Stanzione; Michele Gambardella; Renato Cuocolo; Andrea Ponsiglione; Valeria Romeo; Massimo Imbriaco
Journal:  Eur J Radiol       Date:  2020-05-30       Impact factor: 3.528

Review 2.  Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges.

Authors:  Usman Bashir; Muhammad Musib Siddique; Emma Mclean; Vicky Goh; Gary J Cook
Journal:  AJR Am J Roentgenol       Date:  2016-06-15       Impact factor: 3.959

Review 3.  Artificial intelligence applications for oncological positron emission tomography imaging.

Authors:  Wanting Li; Haiyan Liu; Feng Cheng; Yanhua Li; Sijin Li; Jiangwei Yan
Journal:  Eur J Radiol       Date:  2020-11-30       Impact factor: 3.528

Review 4.  CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges.

Authors:  Meghan G Lubner; Andrew D Smith; Kumar Sandrasegaran; Dushyant V Sahani; Perry J Pickhardt
Journal:  Radiographics       Date:  2017 Sep-Oct       Impact factor: 5.333

5.  Computational Radiomics System to Decode the Radiographic Phenotype.

Authors:  Joost J M van Griethuysen; Andriy Fedorov; Chintan Parmar; Ahmed Hosny; Nicole Aucoin; Vivek Narayan; Regina G H Beets-Tan; Jean-Christophe Fillion-Robin; Steve Pieper; Hugo J W L Aerts
Journal:  Cancer Res       Date:  2017-11-01       Impact factor: 12.701

Review 6.  Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures.

Authors:  Ruben T H M Larue; Gilles Defraene; Dirk De Ruysscher; Philippe Lambin; Wouter van Elmpt
Journal:  Br J Radiol       Date:  2016-12-12       Impact factor: 3.039

Review 7.  Radiomics: the facts and the challenges of image analysis.

Authors:  Stefania Rizzo; Francesca Botta; Sara Raimondi; Daniela Origgi; Cristiana Fanciullo; Alessio Giuseppe Morganti; Massimo Bellomi
Journal:  Eur Radiol Exp       Date:  2018-11-14

8.  Repeatability and Reproducibility of Radiomic Features: A Systematic Review.

Authors:  Alberto Traverso; Leonard Wee; Andre Dekker; Robert Gillies
Journal:  Int J Radiat Oncol Biol Phys       Date:  2018-06-05       Impact factor: 7.038

9.  Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?

Authors:  Fergus Davnall; Connie S P Yip; Gunnar Ljungqvist; Mariyah Selmi; Francesca Ng; Bal Sanghera; Balaji Ganeshan; Kenneth A Miles; Gary J Cook; Vicky Goh
Journal:  Insights Imaging       Date:  2012-10-24

10.  Radiomics: Images Are More than Pictures, They Are Data.

Authors:  Robert J Gillies; Paul E Kinahan; Hedvig Hricak
Journal:  Radiology       Date:  2015-11-18       Impact factor: 11.105

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  1 in total

Review 1.  Rectal MRI radiomics inter- and intra-reader reliability: should we worry about that?

Authors:  Henry C Kwok; Charlotte Charbel; Jayasree Chakraborty; Natally Horvat; Sofia Danilova; Joao Miranda; Natalie Gangai; Iva Petkovska
Journal:  Abdom Radiol (NY)       Date:  2022-04-02
  1 in total

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