Mass Univariate Regression Analysis for Three-Dimensional Liver Image-Derived Phenotypes

Abstract

Image-derived phenotypes of abdominal organs from magnetic resonance imaging reveal variations in volume and shape and may be used to model changes in a normal versus pathological organ and improve diagnosis. Computational atlases of anatomical organs provide many advantages in quantifying and modeling differences in shape and size of organs for population imaging studies. Here we made use of liver segmentations derived from Dixon MRI for 2,730 UK Biobank participants to create 3D liver meshes. We computed the signed distances between a reference and subject-specific meshes to define the surface-tosurface (S2S) phenotype. We employed mass univariate regression analysis to compare the S2S values from the liver meshes to image-derived phenotypes specific to the liver, such as proton density fat fraction and iron concentration while adjusting for age, sex, ethnicity, body mass index and waist-to-hip ratio. Vertex-based associations in the 3D liver mesh were extracted and threshold-free cluster enhancement was applied to improve the sensitivity and stability of the statistical parametric maps. Our findings show that the 3D liver meshes are a robust method for modeling the association between anatomical, anthropometric, and phenotypic variations across the liver. This approach may be readily applied to different clinical conditions as well as extended to other abdominal organs in a larger population.

Liver Shape Analysis using Statistical Parametric Maps at Population Scale

“…The process for template construction of the liver has been previously described [28]. Here, we constructed three distinct templates using liver segmentations from 20, 100 and 200 subject-specific volumes in order to evaluate the impact of cohort size on template construction.…”

“…We applied threshold-free cluster enhancement (TFCE) [31] and permutation testing to assess the associations between S2S distances and anthropometric covariates, as well as liver fat and iron content. These were adjusted for relevant covariates with correction to control the false discovery rate (FDR), as previously described [28]. Specifically, we performed mass univariate regression (MUR) analysis using a refined version of the R package mutools 3D [32] and adjusted for multiple comparisons by applying the FDR procedure [33] to all the TFCE p-values derived from each vertex and each model using 1,000 permutations.…”

“…TFCE not only provides improved sensitivity and stability compared to other cluster-based techniques but also identifies local maxima in the resulting significance map that is not possible in other enhancement and thresholding techniques [14,33]. A permutation testing was then performed on the TFCE maps and the derived TFCE p-values were corrected to control the false discovery rate (FDR), as previously described [28]. Specifically, we performed mass univariate regression (MUR) analysis using the R package mutools3D [34] and adjusted for multiple comparisons by applying the FDR procedure [35] to all the TFCE p-values derived from each vertex using 1,000 permutations.…”

Kidney Shape Statistical Analysis: Associations with Disease and Anthropometric Factors

“…Finally 𝜖 is a 𝑛 𝑠 × 𝑛 𝑣 matrix which is independent and identically distributed across the subjects and is assumed to be a zeromean Gaussian process [25]. We applied threshold-free cluster enhancement (TFCE) [26] and permutation testing to assess the associations between S2S distances and anthropometric covariates, adjusted for relevant covariates with the correction to control the false discovery rate (FDR), as previously described [22]. Specifically, we performed an SPM framework, mass univariate regression (MUR) analysis using a refined version of the R package mutools3D [27] adjusted for multiple comparisons by applying the FDR procedure using the Benjamini-Hochberg method [28] to all the TFCE-derived p-values for each vertex and each model using 1,000 permutations.…”