Introduction
Standardising the radiomics process for clinical trials
Steps | Biologically driven quantitative biomarkers | Data-driven quantitative biomarkers |
---|---|---|
Image acquisition | • Standardised protocols (single and multicentre) • QA/QC process across instruments, sites • Stability of measurement monitored with phantom studies; may be strengthened by human subject test-retest | • Non-standardised protocols in discovery phase followed by standardised protocols within trials • QA/QC process across instruments, sites • Stability of measurement requires human subject test-retest |
VOI delineation | • Can be manual or semi-automated • Can be machine-learnt • Deep learning available but infrequently used | • Can be manual or semi-automated • Can be machine-learnt • Can be derived from fully convolutional neural networks |
Data analysis | • Commercial or academic software applicable to datasets regardless of their source | • Algorithms used are specific to image datasets and may require adaptation and standardisation for individual situations or new datasets* |
Biomarker extraction | • Follows standard formula that describes the biological feature (e.g. tissue density, perfusion, diffusion, standardised uptake of radiotracers related to a biological process/receptor status) | • Algorithm-based mathematical feature extraction not directly linked to a biological process, followed by selection of feature combination that best separate disease from no disease, good from poor outcome (e.g. shape features such as diameter, sphericity; histogram-derived features such as median, skewness, entropy; texture features such as contrast, homogeneity, Haralick variance) |
Biomarker interpretation | • Directly linked to biological process | • Indirect associations with biological process assumed |
Validating the radiomics output
Radiomic analysis | Radiomic feature (process) | Modality | Tissue types investigated | Decision-making role |
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Second-order statistics | Textural (Haralick, Gabor) | Lung, breast, brain, liver, prostate, head and neck, lymph node, cervix | • Prognostic • Predictive • Response • Survival • EGFR expression • p53 mutation status | |
Higher-order statistics Filter grids extract repetitive or non-repetitive patterns | Wavelets | Lung, oesophagus, brain, pancreas, breast, head and neck | • Diagnostic • Prognostic • Predictive • Response • Survival • Surgical resection margins | |
Laplacian transforms (bandpass filters) | PET/CT [92] | Brain, lung, rectum, cervix, kidney | Prognostic Response | |
Minkowski functions (patterns of voxels with intensity above threshold) | ||||
Fractal dimensions (patterns imposed on image and number of grid elements containing voxels of a specified value is computed) | ||||
Delta radiomics | Change in radiomic features | Lung | Response | |
Dynamic radiomic studies | Pharmacokinetic radiomic features | PET/CT [100] | Lung | Response, data highly correlated to data from static studies |
Biological correlates of radiomic features
Limitations of data-driven processes
Implementation of radiomics in clinical trials
Step | Recommended process for clinical trial inclusion |
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Image acquisition | Standardised protocol agreed with site with vendor-specific amendments (incl. software version control) to achieve reproducibility of other QIBs within accepted published standards |
Image acquisition—normalisation | Raw data saved. Image normalisation predefined |
Image analysis—segmentation | If manual or semi-automated, done by centralised/core laboratory by > 1 observer to establish reproducibility. If automated, can be done with CE-marked software with established limits of agreement at local sites |
Image analysis—feature extraction | Use of validated features with established error margins, adapted for individual situations. Discard redundant features. Test reproducibility, repeatability within trial setting |
Computational statistics—feature and model selection | Based on performance by association with trial endpoint (e.g. response/survival) |
Validation | Adequate sample size, test data on samples with similar characteristics, cross-validation strategies, avoid over-fitted models |
Biomarker interpretation | Association with positive diagnosis, prognosis or outcome |