# Spatial correlation between in vivo imaging and immunohistochemical biomarkers: A methodological study

## TL;DR

This study introduces a method to compare in vivo imaging with immunohistochemistry to better understand the tumor microenvironment.

## Contribution

A novel method for 3D spatial correlation between in vivo imaging and immunohistochemical biomarkers is introduced.

## Key findings

- Significant within-subject spatial correlations were found between DCE parameters and IHC biomarkers.
- No between-subject correlations were observed, highlighting the importance of spatial analysis.
- The strongest correlation was between Ki-67 and HIF-1α biomarkers.

## Abstract

•We present a unique method of correlating in vivo imaging to immunohistochemistry.•3D heatmaps of biomarker presence are created from whole-mount tumor resections.•By registering the 3D heatmaps to imaging, we can spatially compare them.•The method provides insight into how well imaging portrays the tumor microenvironment.

We present a unique method of correlating in vivo imaging to immunohistochemistry.

3D heatmaps of biomarker presence are created from whole-mount tumor resections.

By registering the 3D heatmaps to imaging, we can spatially compare them.

The method provides insight into how well imaging portrays the tumor microenvironment.

In this study, we present a method that enables voxel-by-voxel comparison of in vivo imaging to immunohistochemistry (IHC) biomarkers. As a proof of concept, we investigated the spatial correlation between dynamic contrast enhanced (DCE-)CT parameters and IHC biomarkers Ki-67 (proliferation), HIF-1α (hypoxia), and CD45 (immune cells). 54 whole-mount tumor slices of 15 laryngeal and hypopharyngeal carcinomas were immunohistochemically stained and digitized. Heatmaps of biomarker positivity were created and registered to DCE-CT parameter maps. The adiabatic approximation to the tissue homogeneity model was used to fit the following DCE parameters: Ktrans (transfer constant), Ve (extravascular and extracellular space), and Vi (intravascular space). Both IHC and DCE maps were downsampled to 4 × 4 × 3 mm[3] voxels. The mean values per tumor were used to calculate the between-subject correlations between parameters. For the within-subject (spatial) correlation, values of all voxels within a tumor were compared using the repeated measures correlation (rrm). No between-subject correlations were found between IHC biomarkers and DCE parameters, whereas we found multiple significant within-subject correlations: Ve and Ki-67 (rrm = -0.17, P < .001), Ve and HIF-1α (rrm = -0.12, P < .001), Ktrans and CD45 (rrm = 0.13, P < .001), Vi and CD45 (rrm = 0.16, P < .001), and Vi and Ki-67 (rrm = 0.08, P = .003). The strongest correlation was found between IHC biomarkers Ki-67 and HIF-1α (rrm = 0.35, P < .001). This study shows the technical feasibility of determining the 3 dimensional spatial correlation between histopathological biomarker heatmaps and in vivo imaging. It also shows that between-subject correlations do not reflect within-subject correlations of parameters.