Table 1.
Summaries of selected radiomics studies
| Author (year) | Objective of study | Radiomics features | Techniques used | Performance |
| Computed tomography | ||||
| Hunter et al. (2013) | Identify radiomics features that are consistent across multiple diagnostic tools | Geometry, intensity histogram, absolute gradient image, textural features | Hierarchical clustering, dice similaritycoefficient, Jaccard index, CCC | Feature reproducibility is depended on the machine and images |
| Velazquez et al. (2013) | Evaluate the clinical applicability of a semiautomatic segmentation method | 3D-Slicer algorithm | It is noted that 3D-Slicer illustrated high agreement, smaller uncertainty areas, and lower volume differences as compared to manual slice delineation | |
| Aerts et al. (2014) | Assess the correlation of radiomics features with clinical data and the predictive capability of radiomics features | Intensity histogram, shape, textural features, wavelet features | Friedman test, Wilcoxon test, Kaplan-Meier analysis | It is concluded that radiomics features possess prognostic capability |
| Balagurunathan et al. (2014b) | Assess the reproducibility of quantitative imaging features | Intensity histogram, textural features, Laws’ kernels features | Test-retest CCC, Kaplan-Meier plot | Texture features are statistically significant |
| Balagurunathan et al. (2014a) | Assess the reproducibility of image features | Morphological features, first-order features, wavelets feature | CCC, dynamic range, redundancy reduction, weighted Kappa index, optical threshold linear discriminant analysis, ROC curve | It is concluded that most features show high reproducibility with semi-automated segmentation |
| Fried et al. (2014) | Investigate if pre-treatment textural features can enhance the predictive capability in stage III non-small cell lung cancer | Intensity histogram, textural features | Kaplan-Meier curves, leave-one-out cross-validation, CCC | Pre-treatment textural features have better predictive values |
| Parmar et al. (2014) | Evaluate the performance of using 3D-Slicer | Intra-class correlation coefficient | Radiomics features obtained from 3D-Slicer had notably higher reproducibility than those from manual delineation | |
| Hawkins et al. (2014) | Propose an automated prognostic model | Intensity histogram, textural features, geometric features | Decision tree, leave-one-out cross-validation | Accuracy=77.50% |
| Coroller et al. (2015) | Prognostic capability to identify distant metastasis for lung adenocarcinoma patients | Intensity histogram, shape and texture | Laplacian of Gaussian filter, wavelet filter, minimum relevance feature selection | Radiomics features possess strong prognostic capability |
| Parmar et al. (2015a) | Compare radiomics features across different cancer types | Intensity histogram, shape, textural and wavelet features | Consensus clustering, ROC analysis, concordance index, logistic regression, Jaccard index | Radiomics features possess clustering and prognostic characteristics |
| Parmar et al. (2015b) | Assess the different feature selection and classification methods in terms of their performance and prognostic capability | Intensity histogram, textural features | Various feature selection and classification techniques implemented | It is concluded that conditional infomax feature extraction, minimum redundancy maximum relevance, and mutual information feature selection produced the highest stability and predictive capability |
| Cunliffe et al. (2015) | Investigate the correlation of radiation dose and radiomics features and the capability of radiomics features in the analysis of radiation pneumonitis | Textural features, fractal features | Laws’ filters, linear regression model, ANOVA, ROC curve | Radiomics is a promising methodology that can diagnose patients with radiation pneumonitis |
| Mackin et al. (2015) | Study the variability between different CT scanners | Textural features | General Electric, Philips, Siemens, and Toshiba scanners, feature noise introduced | It is verified that the variability in features corresponded in the variability seen in the radiomics features obtained from the images |
| Mattonen et al. (2016) | Physician assessment versus radiomic assessment in the detection of local cancer recurrence for lung cancer | First-order statistics features, textural features | PRTools 5.0 (Delft Pattern Recognition Research, Delft) was utilised for feature selection and classification, leave-one-out cross-validation, SVM classifier | Radiomics can detect early changes associated with local recurrence |
| Mattonen et al. (2015) | Analysis of features for prognostic capability of lung cancer recurrence | Textural features | PRTools 5.0 (Delft Pattern Recognition Research, Delft) was utilised for classification, Wilcoxon signed rank test | High prognostic accuracy using GLCM |
| Wang et al. (2016) | Initiate and evaluate a normalised set of features obtained from CT images and their correlations with overall survival | Lexicon of BI-RADS, Fleischner Society as a guide to establish features | Weighted Kappa index, Kaplan-Meier analysis, CART classifier, PCA | It was reported that textural features are important in the characterisation of lung adenocarcinomas |
| Fave et al. (2015) | Validate the usefulness of predictive models using radiomics features extracted from CT images | Textural features | CCC analysis | Certain radiomics features are resistant to poor-quality CBCT images and noises |
| Huynh et al. (2016) | Analyse stereotactic body radiation therapy with lung cancer | Textural, shape, and statistical features | PCA, factor analysis, Wilcoxon rank-sum test | Radiomics features possess the potential to be predictive |
| Szigeti et al. (2016) | Propose an innovative technique to diagnose lung diseases | Nonlinear features | Radiomics-based fractal dimension analysis, Kruskal-Wallis test, Mann-Whitney post hoc test, Gaussian curves | It is reported that the proposed method is promising and can be implemented in clinical practices |
| Coroller et al. (2016) | Investigate if pre-treatment radiomics features have the prognostic capability in non-small cell lung cancer | Textural features | Wilcoxon-test, Kaplan-Meier analysis, logistic regression, ROC curve | Radiomics features are predictive |
| Zhao et al. (2016) | Investigate the reproducibility of radiomics features | Intensity histogram, morphological features, textural features | Gabor energy, wavelets, Laplacian of Gaussian, model-based feature of fractal dimension, CCC analysis | It is noted that radiomics features are reproducible over different algorithms |
| Yang et al. (2016) | Analyse quantitative imaging features in lung tumours | Intensity histogram, textural features, geometric shape | CCC analysis, Spearman’s correlation, PCA, edge-preserving smoothing filter | Texture features could be utilised for predictive analysis in the future |
| Song et al. (2016) | Investigate if tumour heterogeneity of non-small cell lung cancer can be predicted with radiomics | Textural features | Statistical analysis | Radiomics has prognostic capability for clinical aided diagnosis |
| Wu et al. (2016) | Identify the classifiers for radiomics in lung cancer histology | Intensity histogram, shape features, textural features | Twenty-four feature selection methods, random forest classifier, naïve Bayes classifier, K-NN classifier, ROC curve | Naïve Bayes classifier performed the best; the area under ROC curve: accuracy=0.72 |
| Fave et al. (2016) | Evaluate how different image pre-processing techniques may impact the predictive outcome in univariate analysis | Intensity histogram, textural features | Harrell’s concordance index (c-index), Benjamini-Hochberg procedure | Pre-processing of CT images has an impact on the volume dependence of a feature |
| He et al. (2016) | Examine the effects of contrast-enhancement, reconstruction slice thickness, and convolution kernel on the diagnosis performance of radiomics | Textural features | Laplacian of Gaussian spatial band-pass filter, statistical analysis, Mann-Whitney U-test | It is noted that contrast-enhancement, reconstruction slice thickness, and convolution kernel affect the performance of radiomics |
| Emaminejad | Develop a new quantitative image feature analysis scheme and | Textural features | Naïve Bayesian network-based classifier, leave-one-case-out cross-validation, synthetic minority oversampling technique | Accuracy=80.00% |
| Liang et al. (2016) | Review the prognostic ability of radiomics features for the staging of colorectal cancer | Textural features | Logistic regression model, Mann-Whitney U-test, ROC curve | The area under ROC curve: accuracy=0.792; sensitivity=0.611; specificity=0.680 |
| Huang et al. (2016) | Justify the prognostic ability of lymph node metastasis in patients using radiomics nomogram | Textural features | Statistical analysis, multivariable logistic regression analysis | Radiomics nomogram is proved to be useful in clinical settings |
| Kumar et al. (2016) | Analyse an automatic liver segmentation | Statistical parameter-based approach | It is proven that the proposed method has prognostic capability | |
|
Magnetic resonance imaging | ||||
| Egger et al. (2013) | Assess the advantages of 3D-Slicer over manual segmentation | Dice similarity coefficient, Hausdorff distance | It is reported that 3D-Slicer is more time-efficient and is statistically comparable to manual segmentation | |
| Zhou et al. (2014) | Determine the differentiating ROIs within the tumour for clinical practices | Textural features | K-NN classifier | K-NN classifier: accuracy=93.75% |
| Coquery et al. (2014) | Assess the possibility if histologic properties can be extracted based on an automated analysis | Gaussian mixture modelling, t-tests, linear discriminant analysis | It is reported that the integration of spatial selection and cluster analysis can be implemented to form information obtained from the multiparametric MRI | |
| Chaddad et al. (2015) | Validate the effectiveness of features extracted from GLCM in glioblastoma phenotypes | Textural features | Nearest neighbour classifier | Accuracy=75.58%; sensitivity=63.95%; specificity=90.69% |
| Chaudhury | Validate a new method in the study of heterogeneity in breast cancer | Textural kinetic features | CCC-based random subspace method, naïve Bayes classifier, decision tree classifier, SVM classifier, Kappa statistic | It was concluded that textural kinetic features were more prognostic than features obtained from the whole tumour |
| Upadhaya et al. (2015a) | Study on multimodal MRI in glioblastoma multiforme | Intensity histogram, textural features | SVM classifier | Accuracy=90.00%; sensitivity=85.00%; specificity=95.00% |
| Upadhaya et al. (2015b) | Propose a workflow for a predictive model based on textural features | Intensity histogram, textural features | SVM classifier | T1 pre-contrast: accuracy=60.00%; T1 post-contrast: accuracy=82.50%;T2: accuracy=72.50%;FLAIR: accuracy=75.00%; T1 pre-contrast/T1 post-contrast: accuracy=90.00% |
| Lee et al. (2015) | Identify the correlation of radiomics features with EGFR-driven tumours | Spatial diversity features | Spatial diversity analysis, ROC curve, | Characterising EGFR-driven tumours, the area under ROC curve: accuracy=0.790 |
| Depeursinge et al. (2015) | Assess the significance of intensity and textural features | Nonlinear features, textural features | Riesz wavelet, concordance index, SVM classifier, Cox-LASSO predictive model | C-index=0.81±0.02. It is concluded that accuracy is better when features are extracted based on solid components of a tumour instead of the entire tumour |
| Khalvati et al. (2015) | The automatic detection system of prostate cancer using texture features | Statistical features, textural features, Gabor features | Gabor filter, SVM classifier | Proposed technique surpasses conventional model |
| Velazquez et al. (2015) | Assess the predictive value of glioblastoma automatically segmented and compare it with manual segmentation | VASARI features | The brain tumour image analysis software was utilised, statistical analysis, Spearman rank correlation, CCC analysis, decision tree classifier | It is reported that the automatically segmented datasets have potential in medical imaging research |
| Guo et al. (2015) | Determine the efficiency of the prognostic outcome when combining genomic and radiomics features | Morphological texture, kinetic curve assessment, enhancement-variance kinetic features | Quantitative radiomics analysis, logistic regression, Benjamini-Hochberg procedure, cross-validation with ROC curve | Radiomics features have prognostic capability in determining pathological stage |
| Chung et al. (2015) | Present an innovative automated prostate detection algorithm | Intensity histogram, textural features | Radiomics-driven conditional random field framework, Kirsch edge detection, Gabor filters, SVM classifier | Accuracy=91.17%; sensitivity=71.47%; specificity=91.93% |
| van den Burg et al. (2016) | Analysis of labyrinth | Intensity histogram | Fourier DCT filter, edge detection, gradient orientation filter, entropy filter, Laplacian filter, ridge filter, discrete wavelet transform, image saliency filter, clustering components, morphological components, and binarize colour tone mapping | There was significant statistical difference between normal and patients |
| Grossmann et al. (2016) | Investigate the correlation of radiomics features and molecular pathways | Volumetric features | Gene expression, pathway analysis | It is noted that radiomics features contain highly significant features |
|
Positron emission tomography | ||||
| Nair et al. (2012) | Investigate radiogenomics with PET | Textural features | Student’s t-test, chi-squared test, Fisher’s exact test, PCA, SUV, Kaplan-Meier curves | It is noted that there is a correlation between gene signatures and features extracted with ʏ18һF-FDG |
| Leijenaar et al. (2013) | Carry out a stability analysis of radiomics features in non-small cell lung carcinoma | Intensity histogram, textural features | SUV discretisation, ICC, non-parametric ANOVA | Radiomics features possess prognostic capability |
| Leijenaar et al. (2015) | Determine the importance of a standardised procedure in tumour texture analysis | Textural features | SUV discretisation, Kruskal-Wallis one-way ANOVA | Experiments showed the importance of standardised procedure in tumour texture analysis |
| Ypsilantis et al. (2015) | Determine if machine learning techniques can predict cancer’s metabolic profile and compare the performance of machine learning algorithms with 3S-convolutional neural network | Textural features | Convolutional neural networks, SUV, PCA, SVM classifier, logistic regression | Sensitivity=80.70%; specificity=81.60%. Convolutional neural networks possess better prognostic capability of response to therapy |
| Tixier et al. (2015) | Compare the results of the visual and prognostic assessment in non-small cell lung cancer | Textural features | SUV, t-test, Spearman rank coefficient, Kaplan-Meier method, Cox regression model | The assessments of both visual and prognostic using textural features are not contradictory to each other |
| Nyflot et al. (2015) | Study the influence of stochastic outcome on textural features | Intensity histogram, textural features | Power analysis | It is concluded that protocols should be implemented to ensure that textural features extracted are standardised |
| Grootjans et al. (2016) | Investigate the effect of respiratory gating and noises in PET images | Textural features | Fuzzy locally adaptive Bayesian segmentation algorithm, ranking tests | It is reported that textural features in this work are resilient in the existence of respiratory motion |
| Lian et al. (2016) | Propose a novel methodology for the prognostic tool in PET imaging | Textural features, SUV-based features | SUV, Dempster-Shafer theory for feature selection, ADASYN, K-NN classifier | Proposed methodology showed promising results |
|
Positron emission tomography/computed tomography | ||||
| Cheebsumon et al. (2012) | Comparing PET-and CT-based methods to pathology | SUV, statistical analysis | Diameter from PET-based delineation has reported a more similar result with pathology as compared to CT-based delineation | |
| Yoon et al. (2015) | Discover the predictors of tumours for lung adenocarcinoma | Morphological features, histogram-based, regional, and local features | Chi-squared test, Student’s t-test, SUV, ten-fold cross-validation method | This methodology has potential to be utilised in clinical practice |
| Oliver et al. (2015) | Investigate the difference of image features between RG and 3D images | Sphericity, spherical disproportion, entropy, sum entropy, textural features | SUV, long axis calculation and rotation analysis | It is reported that features obtained from 3D and RG images are different |
| van Velden et al. (2016) | Evaluate the impact of reconstruction techniques and the delineation of radiomics features in non-small cell lung cancer | Fractal features, textural features | SUV, statistical analysis, Wilcoxon signed-rank test | It is noted that the performance of radiomics features is better in delineation than that of applied reconstruction technique |
| Bailly et al. (2016) | Assess the robustness of textural features | Textural features | SUV, coefficient of variation, one-way ANOVA, Tukey HSD test | SUV-based metrics (energy, entropy, RP, and ZP) are found to be robust features |
| Desseroit et al. (2016) | Initiate a nomogram by blending clinical and imaging features | Textural features | SUV, Kaplan-Meier method, log-rank test | Textural features can be used to create a nomogram but need to be further validated |
|
Positron emission tomography/magnetic resonance imaging | ||||
| Vallières et al. (2015) | Develop a joint FDG-PET/CT and MRI texture-based model for early diagnosis of lung metastasis risk | Textural features | SUV, discrete wavelet transform, wavelet band-pass filtering, logistic regression, multivariable analysis, ROC curve | The area under ROC curve: accuracy=98.4%; sensitivity=95.5%; specificity=92.6% |
| Antunes et al. (2016) | Investigate the capability of radiomics features in FLT-PET and MRI | Filtered-based, entropy and textural features from PET images | Sobel and Kirsch edge filters, Gaussian low-pass filter, SUV, Bhattacharyya distance | Radiomics has potential to be an effective tool for categorising treatment response in PET/MRI |
|
Ultrasonography | ||||
| Acharya et al. (2016b) | Propose a novel algorithm to accurately classify non-alcoholic fatty liver disease | DCT | Radon transform, fuzzy sugeno, fatty liver disease index | Accuracy=100.00%; sensitivity=100.00%; specificity=100.00% |
| Acharya et al. (2016c) | Develop an automated thyroid screening system | Entropies | Gabor transform, statistical analysis, C4.5 decision tree | Accuracy=94.30% |
| Acharya et al. (2016a) | Assess the reliability and robustness of an automated fatty liver disease and cirrhosis diagnosis system | Higher-order spectra and entropies | Probabilistic neural network, liver disease index | Accuracy=97.33%;sensitivity=96.00%;specificity=100.00% |
| Acharya et al. (2017) | Evaluate the performance of a new ultrasonography procedure | Second-order statistics | Quadratic discriminant analysis, shear wave breast cancer risk index | Accuracy=93.59%; sensitivity=90.41%; specificity=96.39% |
| Raghavendra et al. (2017) | Develop a computer-aided diagnosis system to automatically differentiate the different stages of thyroid cancer | Textural features | SVM, thyroid clinical risk index | Accuracy=97.52%; sensitivity=90.32%; specificity=98.57% |
| Ma et al. (2017a) | Initiate and assess an automated thyroid nodules detection system | Convolutional neural network | The area under ROC curve: accuracy=98.51% | |
| Ma et al. (2017b) | Propose a hybrid approach to automatically classify benign and malignant thyroid nodules | Convolutional neural network | Accuracy=83.02%; sensitivity=82.41%; specificity=84.96% | |
CCC: concordance correlation coefficient; ROC: receiver operating characteristic; ANOVA: analysis of variance; SVM: support vector machine; GLCM: grey level co-occurrence matrix; CT: computed tomography; BI-RADS: breast imaging reporting and data system; CART: classification and regression tree; PCA: principal component analysis; CBCT: cone beam computed tomography; K-NN: K-nearest neighbour; ROI: region of interest; MRI: magnetic resonance imaging; FLAIR: fluid-attenuated inversion recovery; EGFR: epidermal growth factor receptor; LASSO: least absolute shrinkage and selection operator; VASARI: Visually Accessible Rembrandt Images; DCT: discrete cosine transform; SUV: standardised uptake value; ICC: intraclass correlation; PET: positron-emission tomography; ADASYN: adaptive synthetic; HSD: honest significant difference; FDG: fludeoxyglucose; FLT: fluorothymidine