Literature DB >> 31650960

Radiomics with artificial intelligence: a practical guide for beginners.

Burak Koçak1, Emine Şebnem Durmaz2, Ece Ateş1, Özgür Kılıçkesmez1.   

Abstract

Radiomics is a relatively new word for the field of radiology, meaning the extraction of a high number of quantitative features from medical images. Artificial intelligence (AI) is broadly a set of advanced computational algorithms that basically learn the patterns in the data provided to make predictions on unseen data sets. Radiomics can be coupled with AI because of its better capability of handling a massive amount of data compared with the traditional statistical methods. Together, the primary purpose of these fields is to extract and analyze as much and meaningful hidden quantitative data as possible to be used in decision support. Nowadays, both radiomics and AI have been getting attention for their remarkable success in various radiological tasks, which has been met with anxiety by most of the radiologists due to the fear of replacement by intelligent machines. Considering ever-developing advances in computational power and availability of large data sets, the marriage of humans and machines in future clinical practice seems inevitable. Therefore, regardless of their feelings, the radiologists should be familiar with these concepts. Our goal in this paper was three-fold: first, to familiarize radiologists with the radiomics and AI; second, to encourage the radiologists to get involved in these ever-developing fields; and, third, to provide a set of recommendations for good practice in design and assessment of future works.

Entities:  

Mesh:

Year:  2019        PMID: 31650960      PMCID: PMC6837295          DOI: 10.5152/dir.2019.19321

Source DB:  PubMed          Journal:  Diagn Interv Radiol        ISSN: 1305-3825            Impact factor:   2.630


  62 in total

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Journal:  J Am Coll Radiol       Date:  2018-02-04       Impact factor: 5.532

2.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data.

Authors:  J G Sled; A P Zijdenbos; A C Evans
Journal:  IEEE Trans Med Imaging       Date:  1998-02       Impact factor: 10.048

3.  A collaborative enterprise for multi-stakeholder participation in the advancement of quantitative imaging.

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Journal:  Radiology       Date:  2011-03       Impact factor: 11.105

Review 4.  Deep learning.

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5.  IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics.

Authors:  Lifei Zhang; David V Fried; Xenia J Fave; Luke A Hunter; Jinzhong Yang; Laurence E Court
Journal:  Med Phys       Date:  2015-03       Impact factor: 4.071

6.  Artificial Intelligence in Cardiothoracic Radiology.

Authors:  William F Auffermann; Elliott K Gozansky; Srini Tridandapani
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Review 9.  Artificial intelligence in cancer imaging: Clinical challenges and applications.

Authors:  Wenya Linda Bi; Ahmed Hosny; Matthew B Schabath; Maryellen L Giger; Nicolai J Birkbak; Alireza Mehrtash; Tavis Allison; Omar Arnaout; Christopher Abbosh; Ian F Dunn; Raymond H Mak; Rulla M Tamimi; Clare M Tempany; Charles Swanton; Udo Hoffmann; Lawrence H Schwartz; Robert J Gillies; Raymond Y Huang; Hugo J W L Aerts
Journal:  CA Cancer J Clin       Date:  2019-02-05       Impact factor: 508.702

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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  61 in total

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Journal:  Mol Imaging Biol       Date:  2020-12       Impact factor: 3.488

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Authors:  Matjaz Vogrin; Teodor Trojner; Robi Kelc
Journal:  Radiol Oncol       Date:  2020-11-10       Impact factor: 2.991

3.  Radiogenomics of lower-grade gliomas: machine learning-based MRI texture analysis for predicting 1p/19q codeletion status.

Authors:  Burak Kocak; Emine Sebnem Durmaz; Ece Ates; Ipek Sel; Saime Turgut Gunes; Ozlem Korkmaz Kaya; Amalya Zeynalova; Ozgur Kilickesmez
Journal:  Eur Radiol       Date:  2019-11-05       Impact factor: 5.315

Review 4.  How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts.

Authors:  Burak Kocak; Ece Ates Kus; Ozgur Kilickesmez
Journal:  Eur Radiol       Date:  2020-10-01       Impact factor: 5.315

5.  Do the combination of multiparametric MRI-based radiomics and selected blood inflammatory markers predict the grade and proliferation in glioma patients?

Authors:  Jing Guo; Jialiang Ren; Junkang Shen; Rui Cheng; Yexin He
Journal:  Diagn Interv Radiol       Date:  2021-05       Impact factor: 2.630

6.  Machine learning-based quantitative texture analysis of conventional MRI combined with ADC maps for assessment of IDH1 mutation in high-grade gliomas.

Authors:  Deniz Alis; Omer Bagcilar; Yeseren Deniz Senli; Mert Yergin; Cihan Isler; Naci Kocer; Civan Islak; Osman Kizilkilic
Journal:  Jpn J Radiol       Date:  2019-11-18       Impact factor: 2.374

Review 7.  Role of Machine Learning and Artificial Intelligence in Interventional Oncology.

Authors:  Brian D'Amore; Sara Smolinski-Zhao; Dania Daye; Raul N Uppot
Journal:  Curr Oncol Rep       Date:  2021-04-20       Impact factor: 5.075

8.  Pre-operative MRI Radiomics for the Prediction of Progression and Recurrence in Meningiomas.

Authors:  Ching-Chung Ko; Yang Zhang; Jeon-Hor Chen; Kai-Ting Chang; Tai-Yuan Chen; Sher-Wei Lim; Te-Chang Wu; Min-Ying Su
Journal:  Front Neurol       Date:  2021-05-14       Impact factor: 4.003

Review 9.  Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods-A Critical Review of Literature.

Authors:  Chen-Yi Xie; Chun-Lap Pang; Benjamin Chan; Emily Yuen-Yuen Wong; Qi Dou; Varut Vardhanabhuti
Journal:  Cancers (Basel)       Date:  2021-05-19       Impact factor: 6.639

10.  CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies.

Authors:  Salvatore Gitto; Renato Cuocolo; Domenico Albano; Francesco Morelli; Lorenzo Carlo Pescatori; Carmelo Messina; Massimo Imbriaco; Luca Maria Sconfienza
Journal:  Insights Imaging       Date:  2021-06-02
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