Literature DB >> 34109326

Emergence of Radiomics: Novel Methodology Identifying Imaging Biomarkers of Disease in Diagnosis, Response, and Progression.

Edward Florez1, Ali Fatemi1,2, Pier Paolo Claudio2,3, Candace M Howard1.   

Abstract

Radiomics is an emerging area within clinical radiology research. It seeks to take full advantage of all the information contained in multiple medical imaging modalities. With a radiomics approach, medical images are not limited to providing only a qualitative assessment but can also provide quantitative data by parameterizing image features. These parameters can be used to identify regions and volumes of interest and discriminate normal healthy tissue from abnormal or diseased tissue. Radiomics is an interlinked sequence of processes of vital importance that begins with the acquisition and selection of medical images that involve standardization of acquisition protocols and inter-equipment normalization. This is followed by the identification and segmentation of regions or volumes of interest by expert radiologists through the use of computational tools that offer speed while reducing variability and bias. The segmentation process is the most critical stage in radiomics. This sometimes requires the incorporation of a pre-processing stage consisting of advanced techniques (reconstruction processes, filtering, etc.). Thereafter, representative characteristics of the region or volume of interest are extracted by approaches based on statistics, morphological features, and transform-based variables. Next, a statistical selection of the parameters that provide a high association and correlation with the clinical condition of interest is performed. Finally, processes such as data integration, standardization, classification, and mining processes can be applied as needed for particular applications. Ongoing research in radiomics aims to reduce the time and costs involved in interpreting medical images while simultaneously increasing the quality of diagnoses and monitoring of as well as the selection of treatment strategies. The results of many studies combining radiomics with standard medical techniques are highly encouraging, and these new approaches are increasingly used. This review article details the components of radiomics and discusses its applications, challenges, and future directions for this exciting new field of study.

Entities:  

Keywords:  Big Data; Medical Imaging; Radiomics; Segmentation; Tumors

Year:  2018        PMID: 34109326      PMCID: PMC8186380     

Source DB:  PubMed          Journal:  SM J Clin Med Imaging


  68 in total

1.  Nonlocal means-based speckle filtering for ultrasound images.

Authors:  Pierrick Coupé; Pierre Hellier; Charles Kervrann; Christian Barillot
Journal:  IEEE Trans Image Process       Date:  2009-05-27       Impact factor: 10.856

2.  The method and efficacy of support vector machine classifiers based on texture features and multi-resolution histogram from (18)F-FDG PET-CT images for the evaluation of mediastinal lymph nodes in patients with lung cancer.

Authors:  Xuan Gao; Chunyu Chu; Yingci Li; Peiou Lu; Wenzhi Wang; Wanyu Liu; Lijuan Yu
Journal:  Eur J Radiol       Date:  2014-11-18       Impact factor: 3.528

3.  Factors influencing self-reported vision-related activity limitation in the visually impaired.

Authors:  Daryl R Tabrett; Keziah Latham
Journal:  Invest Ophthalmol Vis Sci       Date:  2011-07-15       Impact factor: 4.799

4.  Special Section Guest Editorial: Radiomics and Deep Learning.

Authors:  Despina Kontos; Ronald M Summers; Maryellen Giger
Journal:  J Med Imaging (Bellingham)       Date:  2018-01-04

5.  Association of Radiomics and Metabolic Tumor Volumes in Radiation Treatment of Glioblastoma Multiforme.

Authors:  Christopher J Lopez; Natalya Nagornaya; Nestor A Parra; Deukwoo Kwon; Fazilat Ishkanian; Arnold M Markoe; Andrew Maudsley; Radka Stoyanova
Journal:  Int J Radiat Oncol Biol Phys       Date:  2016-11-15       Impact factor: 7.038

6.  A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists' delineations and with the surgical specimen.

Authors:  Emmanuel Rios Velazquez; Hugo J W L Aerts; Yuhua Gu; Dmitry B Goldgof; Dirk De Ruysscher; Andre Dekker; René Korn; Robert J Gillies; Philippe Lambin
Journal:  Radiother Oncol       Date:  2012-11-15       Impact factor: 6.280

7.  Identification of noninvasive imaging surrogates for brain tumor gene-expression modules.

Authors:  Maximilian Diehn; Christine Nardini; David S Wang; Susan McGovern; Mahesh Jayaraman; Yu Liang; Kenneth Aldape; Soonmee Cha; Michael D Kuo
Journal:  Proc Natl Acad Sci U S A       Date:  2008-03-24       Impact factor: 11.205

8.  Decoding global gene expression programs in liver cancer by noninvasive imaging.

Authors:  Eran Segal; Claude B Sirlin; Clara Ooi; Adam S Adler; Jeremy Gollub; Xin Chen; Bryan K Chan; George R Matcuk; Christopher T Barry; Howard Y Chang; Michael D Kuo
Journal:  Nat Biotechnol       Date:  2007-05-21       Impact factor: 54.908

Review 9.  Advances in medical imaging for cancer diagnosis and treatment.

Authors:  H N Wagner; P S Conti
Journal:  Cancer       Date:  1991-02-15       Impact factor: 6.860

10.  Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology.

Authors:  Weimiao Wu; Chintan Parmar; Patrick Grossmann; John Quackenbush; Philippe Lambin; Johan Bussink; Raymond Mak; Hugo J W L Aerts
Journal:  Front Oncol       Date:  2016-03-30       Impact factor: 6.244

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  1 in total

1.  A Multiparametric Fusion Radiomics Signature Based on Contrast-Enhanced MRI for Predicting Early Recurrence of Hepatocellular Carcinoma.

Authors:  Wencui Li; Hongru Shen; Lizhu Han; Jiaxin Liu; Bohan Xiao; Xubin Li; Zhaoxiang Ye
Journal:  J Oncol       Date:  2022-09-28       Impact factor: 4.501

  1 in total

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