Repeatability of derived parameters from histograms following non-Gaussian diffusion modelling of diffusion-weighted imaging in a paediatric oncological cohort

Abstract

ObjectivesTo examine repeatability of parameters derived from non-Gaussian diffusion models in data acquired in children with solid tumours.MethodsPaediatric patients (<16 years, n = 17) were scanned twice, 24 h apart, using DWI (6 b-values, 0–1000 mm−2 s) at 1.5 T in a prospective study. Tumour ROIs were drawn (3 slices) and all data fitted using IVIM, stretched exponential, and kurtosis models; percentage coefficients of variation (CV) calculated for each parameter at all ROI histogram centiles, including the medians.ResultsThe values for ADC, D, DDCα, α, and DDCK gave CV < 10 % down to the 5th centile, with sharp CV increases below 5th and above 95th centile. K, f, and D* showed increased CV (>30 %) over the histogram. ADC, D, DDCα, and DDCK were strongly correlated (ρ > 0.9), DDCα and α were not correlated (ρ = 0.083).ConclusionPerfusion- and kurtosis-related parameters displayed larger, more variable CV across the histogram, indicating observed clinical changes outside of D/DDC in these models should be interpreted with caution. Centiles below 5th for all parameters show high CV and are unreliable as diffusion metrics. The stretched exponential model behaved well for both DDCα and α, making it a strong candidate for modelling multiple-b-value diffusion imaging data.Key Points • ADC has good repeatability as low 5th centile of the histogram distribution. • High CV was observed for all parameters at extremes of histogram. • Parameters from the stretched exponential model showed low coefficients of variation. • The median ADC, D, DDC α , and DDC K are highly correlated and repeatable. • Perfusion/kurtosis parameters showed high CV variations across their histogram distributions.

Lung Cancer: Short‐Term Reproducibility of Intravoxel Incoherent Motion Parameters and Apparent Diffusion Coefficient at 3T

“…Referring to T 2 WI and DWI images, the region of interest (ROI) was manually outlined on the ADC map of the largest cross‐section of the tumor, and visually identified calcifications, large vessels, necrosis areas, and artifacts were excluded, then the software‐generated mean ADC value was recorded. For the repeated scanned data, the matching slice was set …”

“…Therefore, to use these parameters in the clinical assessment of lung cancer, the reproducibility of these quantitative parameters should be determined to know the magnitude of change in D, D*, f, and ADC that can be detected confidently. There is only modest literature on the reproducibility of IVIM in other organs . With regard to the lungs, Weller et al discussed the short‐term scanning reproducibility of ADCs of malignant lung lesions.…”

Histogram analysis of diffusion kurtosis imaging based on whole‐volume images of breast lesions

“…Histogram analysis can provide information about whole tumors including minimum, maximum, mean, skewness and kurtosis values of the parameter distributions . More valuable information can be obtained from a histogram analysis of DKI parameters …”

Non-invasive quantification of tumour heterogeneity in water diffusivity to differentiate malignant from benign tissues of urinary bladder: a phase I study

“…A number of previous studies have shown that the assessment of tumour heterogeneity is highly valuable for cancer diagnosis, prognosis, and treatment monitoring [10–12]. Histogram analysis is a widely used tool for heterogeneity quantification, particularly for MRI features, which are not always well visualised with the naked eye [11, 13]. Two quantities calculated by using this method are uniformity (U) and entropy (E).…”