Introduction
Accuracy
Reproducibility
Standardisation
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Standardisation of image acquisition: similar acquisition parameters should be used across imaging platforms, when these parameters affect the results of the biomarker. For example, the calculation of ADC depends on the number and choice of the gradient “b” values. A collaborative paper by Padhani et al. [18] lays the foundation for acquisition standardisation, notably by recommending that monoexponential assessments of ADC should use two b values above 100 mm2/s.Moreover, DW-MRI is very sensitive to motion. Motion correction schemes are thus advised for DW-MRI acquisition. However, it is still unclear which scheme is optimal. As an example for upper abdominal studies, some consider that free breathing acquisition produces reliable enough data, even with a better reproducibility than breath-hold, and that a respiratory-triggered scheme produces less reproducible data, while others recommend using tracking-only navigator techniques [19‐21].
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Standardisation of image analysis: volume and region of interest (ROI) determinations and parameter calculation (mathematical models) should be standardised. In tumour perfusion imaging, it has been shown that the ROI placements in the vascular input and in the tumour influence the results and reproducibility of the parameter measurements [22]. To take motion into account, rigid and non-rigid registration of images at different time points can be used. In heterogeneous lesions such as tumours, imaging biomarkers are frequently calculated as parametric maps with spatial resolution. We need to define how to handle the histogram that displays the obtained values. Descriptive statistics such as mean value, standard deviation, and range can be directly obtained from the histogram. The main drawback with this approach is the clear tendency to underestimate the changes in body tissues and organs, since the values indicative of disease, or its most relevant manifestations, are minimised. For this reason, percentiles are used in some settings to obtain a better relationship with the most relevant predictive clinical variables. The optimal type of approach must be defined for each problem (complete histogram, partial histogram in quartiles, partial histogram in deciles). A further approach involves the analysis of the heterogeneity in the spatial distribution of a biomarker provided by its parametric image. To this end, some distribution asymmetry statistics such as kurtosis can be used [23‐26]. Finally, the choice of the mathematical model that is used to calculate the quantitative parameters has also a major influence on the results that are obtained [27, 28]. Standardisation procedures are currently being developed [18, 29, 30]. It is important that standardisation be a collaborative effort of academia and industry. Standardisation of data reporting should also be performed. For example, to describe the liver elasticity in cirrhosis, different units (Young modulus in kPa, shear modulus in kPa, wave speed in m/s) and different cut-off values are currently used [31‐33]. Standardisation of these data would improve the communication between research groups.