Radiomics in Brain Tumor

Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

M. Zhou, J. Scott, B. Chaudhury, L. Hall, D. Goldgof, K. W. Yeom, M. Iv, Y. Ou, Jayashree Kalpathy-Cramer, S. Napel, R. Gillies, O. Gevaert, R. Gatenby

Research output: Contribution to journalReview article

43 Citations (Scopus)

Abstract

Radiomics describes a broad set of computational methods that extract quantitative features from radiographic images. The resulting features can be used to inform imaging diagnosis, prognosis, and therapy response in oncology. However, major challenges remain for methodologic developments to optimize feature extraction and provide rapid information flow in clinical settings. Equally important, to be clinically useful, predictive radiomic properties must be clearly linked to meaningful biologic characteristics and qualitative imaging properties familiar to radiologists. Here we use a cross-disciplinary approach to highlight studies in radiomics. We review brain tumor radiologic studies (eg, imaging interpretation) through computational models (eg, computer vision and machine learning) that provide novel clinical insights. We outline current quantitative image feature extraction and prediction strategies with different levels of available clinical classes for supporting clinical decision-making. We further discuss machine-learning challenges and data opportunities to advance radiomic studies.

Original languageEnglish (US)
Pages (from-to)208-216
Number of pages9
JournalAmerican Journal of Neuroradiology
Volume39
Issue number2
DOIs
StatePublished - Feb 1 2018
Externally publishedYes

Fingerprint

Brain Neoplasms
Computer Simulation
Machine Learning
Therapeutics
Radiologists
Clinical Decision-Making

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Clinical Neurology

Cite this

Radiomics in Brain Tumor : Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches. / Zhou, M.; Scott, J.; Chaudhury, B.; Hall, L.; Goldgof, D.; Yeom, K. W.; Iv, M.; Ou, Y.; Kalpathy-Cramer, Jayashree; Napel, S.; Gillies, R.; Gevaert, O.; Gatenby, R.

In: American Journal of Neuroradiology, Vol. 39, No. 2, 01.02.2018, p. 208-216.

Research output: Contribution to journalReview article

Zhou, M, Scott, J, Chaudhury, B, Hall, L, Goldgof, D, Yeom, KW, Iv, M, Ou, Y, Kalpathy-Cramer, J, Napel, S, Gillies, R, Gevaert, O & Gatenby, R 2018, 'Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches', American Journal of Neuroradiology, vol. 39, no. 2, pp. 208-216. https://doi.org/10.3174/ajnr.A5391
Zhou, M. ; Scott, J. ; Chaudhury, B. ; Hall, L. ; Goldgof, D. ; Yeom, K. W. ; Iv, M. ; Ou, Y. ; Kalpathy-Cramer, Jayashree ; Napel, S. ; Gillies, R. ; Gevaert, O. ; Gatenby, R. / Radiomics in Brain Tumor : Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches. In: American Journal of Neuroradiology. 2018 ; Vol. 39, No. 2. pp. 208-216.
@article{e33c93e806334e6ba93b8bd1404daa8e,
title = "Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches",
abstract = "Radiomics describes a broad set of computational methods that extract quantitative features from radiographic images. The resulting features can be used to inform imaging diagnosis, prognosis, and therapy response in oncology. However, major challenges remain for methodologic developments to optimize feature extraction and provide rapid information flow in clinical settings. Equally important, to be clinically useful, predictive radiomic properties must be clearly linked to meaningful biologic characteristics and qualitative imaging properties familiar to radiologists. Here we use a cross-disciplinary approach to highlight studies in radiomics. We review brain tumor radiologic studies (eg, imaging interpretation) through computational models (eg, computer vision and machine learning) that provide novel clinical insights. We outline current quantitative image feature extraction and prediction strategies with different levels of available clinical classes for supporting clinical decision-making. We further discuss machine-learning challenges and data opportunities to advance radiomic studies.",
author = "M. Zhou and J. Scott and B. Chaudhury and L. Hall and D. Goldgof and Yeom, {K. W.} and M. Iv and Y. Ou and Jayashree Kalpathy-Cramer and S. Napel and R. Gillies and O. Gevaert and R. Gatenby",
year = "2018",
month = "2",
day = "1",
doi = "10.3174/ajnr.A5391",
language = "English (US)",
volume = "39",
pages = "208--216",
journal = "American Journal of Neuroradiology",
issn = "0195-6108",
publisher = "American Society of Neuroradiology",
number = "2",

}

TY - JOUR

T1 - Radiomics in Brain Tumor

T2 - Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

AU - Zhou, M.

AU - Scott, J.

AU - Chaudhury, B.

AU - Hall, L.

AU - Goldgof, D.

AU - Yeom, K. W.

AU - Iv, M.

AU - Ou, Y.

AU - Kalpathy-Cramer, Jayashree

AU - Napel, S.

AU - Gillies, R.

AU - Gevaert, O.

AU - Gatenby, R.

PY - 2018/2/1

Y1 - 2018/2/1

N2 - Radiomics describes a broad set of computational methods that extract quantitative features from radiographic images. The resulting features can be used to inform imaging diagnosis, prognosis, and therapy response in oncology. However, major challenges remain for methodologic developments to optimize feature extraction and provide rapid information flow in clinical settings. Equally important, to be clinically useful, predictive radiomic properties must be clearly linked to meaningful biologic characteristics and qualitative imaging properties familiar to radiologists. Here we use a cross-disciplinary approach to highlight studies in radiomics. We review brain tumor radiologic studies (eg, imaging interpretation) through computational models (eg, computer vision and machine learning) that provide novel clinical insights. We outline current quantitative image feature extraction and prediction strategies with different levels of available clinical classes for supporting clinical decision-making. We further discuss machine-learning challenges and data opportunities to advance radiomic studies.

AB - Radiomics describes a broad set of computational methods that extract quantitative features from radiographic images. The resulting features can be used to inform imaging diagnosis, prognosis, and therapy response in oncology. However, major challenges remain for methodologic developments to optimize feature extraction and provide rapid information flow in clinical settings. Equally important, to be clinically useful, predictive radiomic properties must be clearly linked to meaningful biologic characteristics and qualitative imaging properties familiar to radiologists. Here we use a cross-disciplinary approach to highlight studies in radiomics. We review brain tumor radiologic studies (eg, imaging interpretation) through computational models (eg, computer vision and machine learning) that provide novel clinical insights. We outline current quantitative image feature extraction and prediction strategies with different levels of available clinical classes for supporting clinical decision-making. We further discuss machine-learning challenges and data opportunities to advance radiomic studies.

UR - http://www.scopus.com/inward/record.url?scp=85042155083&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85042155083&partnerID=8YFLogxK

U2 - 10.3174/ajnr.A5391

DO - 10.3174/ajnr.A5391

M3 - Review article

VL - 39

SP - 208

EP - 216

JO - American Journal of Neuroradiology

JF - American Journal of Neuroradiology

SN - 0195-6108

IS - 2

ER -