{"title":"Spatial correlation between in vivo imaging and immunohistochemical biomarkers: A methodological study","authors":"","doi":"10.1016/j.tranon.2024.102051","DOIUrl":null,"url":null,"abstract":"<div><p>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: <span><math><msup><mrow><mi>K</mi></mrow><mrow><mi>t</mi><mi>r</mi><mi>a</mi><mi>n</mi><mi>s</mi></mrow></msup></math></span> (transfer constant), <span><math><msub><mi>V</mi><mi>e</mi></msub></math></span> (extravascular and extracellular space), and <span><math><msub><mi>V</mi><mi>i</mi></msub></math></span> (intravascular space). Both IHC and DCE maps were downsampled to 4 × 4 × 3 mm[<span><span>3</span></span>] 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 (<span><math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math></span>). No between-subject correlations were found between IHC biomarkers and DCE parameters, whereas we found multiple significant within-subject correlations: <span><math><msub><mi>V</mi><mi>e</mi></msub></math></span> and Ki-67 (<span><math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math></span> = -0.17, <em>P</em> < .001), <span><math><msub><mi>V</mi><mi>e</mi></msub></math></span> and HIF-1α (<span><math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math></span> = -0.12, <em>P</em> < .001), <span><math><msup><mrow><mi>K</mi></mrow><mrow><mi>t</mi><mi>r</mi><mi>a</mi><mi>n</mi><mi>s</mi></mrow></msup></math></span> and CD45 (<span><math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math></span> = 0.13, <em>P</em> < .001), <span><math><msub><mi>V</mi><mi>i</mi></msub></math></span> and CD45 (<span><math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math></span> = 0.16, <em>P</em> < .001), and <span><math><msub><mi>V</mi><mi>i</mi></msub></math></span> and Ki-67 (<span><math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math></span> = 0.08, <em>P</em> = .003). The strongest correlation was found between IHC biomarkers Ki-67 and HIF-1α (<span><math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math></span> = 0.35, <em>P</em> < .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.</p></div>","PeriodicalId":48975,"journal":{"name":"Translational Oncology","volume":null,"pages":null},"PeriodicalIF":5.0000,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1936523324001785/pdfft?md5=a7b818637215ccd4114924d88a571465&pid=1-s2.0-S1936523324001785-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Translational Oncology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1936523324001785","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
Abstract
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: (transfer constant), (extravascular and extracellular space), and (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 (). No between-subject correlations were found between IHC biomarkers and DCE parameters, whereas we found multiple significant within-subject correlations: and Ki-67 ( = -0.17, P < .001), and HIF-1α ( = -0.12, P < .001), and CD45 ( = 0.13, P < .001), and CD45 ( = 0.16, P < .001), and and Ki-67 ( = 0.08, P = .003). The strongest correlation was found between IHC biomarkers Ki-67 and HIF-1α ( = 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.
期刊介绍:
Translational Oncology publishes the results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of oncology patients. Translational Oncology will publish laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer. Peer reviewed manuscript types include Original Reports, Reviews and Editorials.