TY - JOUR
T1 - Spatial correlation between in vivo imaging and immunohistochemical biomarkers
T2 - A methodological study
AU - Smits, Hilde J.G.
AU - Bennink, Edwin
AU - Ruiter, Lilian N.
AU - Breimer, Gerben E.
AU - Willems, Stefan M.
AU - Dankbaar, Jan W.
AU - Philippens, Marielle E.P.
N1 - Publisher Copyright:
© 2024
PY - 2024/10
Y1 - 2024/10
N2 - 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.
AB - 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.
KW - Dynamic contrast enhanced CT
KW - Head and Neck Cancer
KW - Immunohistochemistry
UR - http://www.scopus.com/inward/record.url?scp=85198537412&partnerID=8YFLogxK
U2 - 10.1016/j.tranon.2024.102051
DO - 10.1016/j.tranon.2024.102051
M3 - Article
AN - SCOPUS:85198537412
VL - 48
JO - Translational Oncology
JF - Translational Oncology
M1 - 102051
ER -