AI Article Synopsis
- The study assesses the effectiveness of four diffusion models (mono exponential, IVIM, stretched exponential, and kurtosis) for analyzing Diffusion-Weighted Imaging (DWI) data in Wilms' tumours and normal kidneys.
- Data from 110 Wilms' tumours and 75 normal kidneys were analyzed using voxel-wise goodness of fit criteria, particularly focusing on Akaike Information Criteria (AIC) to determine the most suitable model.
- Results indicated that IVIM and stretched exponential models are superior for Wilms' tumours, while IVIM was the best for normal kidneys, challenging the adequacy of the mono exponential model for both conditions.
Article Abstract
Objective: ADC (Apparent Diffusion Coefficient) derived from Diffusion-Weighted Imaging (DWI) has shown promise as a non-invasive quantitative imaging biomarker in Wilms' tumours. However, many non-Gaussian models could be applied to DWI. This study aimed to compare the suitability of four diffusion models (mono exponential, IVIM [Intravoxel Incoherent Motion], stretched exponential, and kurtosis) in Wilms' tumours and the unaffected contralateral kidneys.
Materials And Methods: DWI data were retrospectively reviewed (110 Wilms' tumours and 75 normal kidney datasets). The goodness of fit for each model was measured voxel-wise using Akaike Information Criteria (AIC). Mean AIC was calculated for each tumour volume (or contralateral normal kidney tissue). One-way ANOVAs with Greenhouse-Geisser correction and post hoc tests using the Bonferroni correction evaluated significant differences between AIC values; the lowest AIC indicating the optimum model.
Results: IVIM and stretched exponential provided the best fits to the Wilms' tumour DWI data. IVIM provided the best fit for the normal kidney data. Mono exponential was the least appropriate fitting method for both Wilms' tumour and normal kidney data.
Discussion: The diffusion weighted signal in Wilms' tumours and normal kidney tissue does not exhibit a mono-exponential decay and is better described by non-Gaussian models of diffusion.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8018931 | PMC |
| http://dx.doi.org/10.1007/s10334-020-00862-4 | DOI Listing |
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