Radiomics in Oncological PET/CT: a Methodological Overview
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Abstract
Radiomics is a medical imaging analysis approach based on computer-vision. Metabolic radiomics in particular analyses the spatial distribution patterns of molecular metabolism on PET images. Measuring intratumoral heterogeneity via image is one of the main targets of radiomics research, and it aims to build a image-based model for better patient management. The workflow of radiomics using texture analysis follows these steps: 1) imaging (image acquisition and reconstruction); 2) preprocessing (segmentation & quantization); 3) quantification (texture matrix design & texture feature extraction); and 4) analysis (statistics and/or machine learning). The parameters or conditions at each of these steps are effect on the results. In statistical testing or modeling, problems such as multiple comparisons, dependence on other variables, and high dimensionality of small sample size data should be considered. Standardization of methodology and harmonization of image quality are one of the most important challenges with radiomics methodology. Even though there are current issues in radiomics methodology, it is expected that radiomics will be clinically useful in personalized medicine for oncology.
Keywords
Radiomics Texture analysis Intratumoral heterogeneity FDG PET/CT OncologyAbbreviations
- AQ
Absolute quantization
- AUC-CSH
Area under curve of cumulative SUV-volume histogram
- CSH
Cumulative SUV-volume histogram
- CV
Coefficient of variation
- FBN
Fixed bin number
- FBS
Fixed bin size
- FCM
Fuzzy C-means
- FDR
False-discovery rate
- 18F-FDG
18F-fluorodeoxyglucose
- FLAB
Fuzzy locally adaptive Bayesian
- GLCM
Gray-level co-occurrence matrix
- GLRLM
Gray-level run-length matrix
- GLSZM
Gray-level size zone matrix
- IFH
Intensity frequency histogram
- ITH
Intratumoral heterogeneity
- IVH
Intensity volume histogram
- MTV
Metabolic tumor volume
- NGTDM
Neighborhood gray-tone difference matrix
- PET/CT
Positron emission tomography/computed tomography
- PVC
Partial volume correction
- RQ
Relative quantization
- SAM
Second angular moment
- SGLDM
Spatial gray-level dependence matrix
- SUV
Standardized uptake value
- TLG
Total lesion glycolysis
Notes
Compliance with Ethical Standards
Conflict of Interest
Seunggyun Ha, Hongyoon Choi, Jin Chul Paeng, and Gi Jeong Cheon declare no conflict of interest.
Ethical Statement
This work does not contain any studies with human participants or animals performed by any of the authors. For this type of study, formal consent is not required.
Informed Consent
Not applicable.
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