TY - JOUR
T1 - Potential and limitations of radiomics in neuro-oncology
AU - Taha, Birra
AU - Boley, Daniel
AU - Sun, Ju
AU - Chen, Clark
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/8
Y1 - 2021/8
N2 - Radiomics seeks to apply classical methods of image processing to obtain quantitative parameters from imaging. Derived features are subsequently fed into algorithmic models to aid clinical decision making. The application of radiomics and machine learning techniques to clinical medicine remains in its infancy. The great potential of radiomics lies in its objective, granular approach to investigating clinical imaging. In neuro-oncology, advanced machine learning techniques, particularly deep learning, are at the forefront of new discoveries in the field. However, despite the great promise of machine learning aided radiomic approaches, the current use remains confined to scholarly research, without real-world deployment in neuro-oncology. The paucity of data, inconsistencies in preprocessing, radiomic feature instability, and the rarity of the events of interest are critical barriers to clinical translation. In this article, we will outline the major steps in the process of radiomics, as well as review advances and challenges in the field as they pertain to neuro-oncology.
AB - Radiomics seeks to apply classical methods of image processing to obtain quantitative parameters from imaging. Derived features are subsequently fed into algorithmic models to aid clinical decision making. The application of radiomics and machine learning techniques to clinical medicine remains in its infancy. The great potential of radiomics lies in its objective, granular approach to investigating clinical imaging. In neuro-oncology, advanced machine learning techniques, particularly deep learning, are at the forefront of new discoveries in the field. However, despite the great promise of machine learning aided radiomic approaches, the current use remains confined to scholarly research, without real-world deployment in neuro-oncology. The paucity of data, inconsistencies in preprocessing, radiomic feature instability, and the rarity of the events of interest are critical barriers to clinical translation. In this article, we will outline the major steps in the process of radiomics, as well as review advances and challenges in the field as they pertain to neuro-oncology.
KW - Deep learning
KW - Imaging
KW - Machine learning
KW - Neuro-oncology
KW - Radiomics
UR - http://www.scopus.com/inward/record.url?scp=85107849829&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85107849829&partnerID=8YFLogxK
U2 - 10.1016/j.jocn.2021.05.015
DO - 10.1016/j.jocn.2021.05.015
M3 - Review article
C2 - 34275550
AN - SCOPUS:85107849829
SN - 0967-5868
VL - 90
SP - 206
EP - 211
JO - Journal of Clinical Neuroscience
JF - Journal of Clinical Neuroscience
ER -