Table 1.
Comparison of standardisation steps for biologically driven and data-driven biomarkers (QA, quality assurance; QC, quality Control; VOI, volume of interest)
| 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 |