活体成像与免疫组化生物标记物之间的空间相关性：方法学研究

IF 5 2区医学 Q2 Medicine Translational Oncology Pub Date : 2024-07-16 DOI:10.1016/j.tranon.2024.102051

{"title":"活体成像与免疫组化生物标记物之间的空间相关性：方法学研究","authors":"","doi":"10.1016/j.tranon.2024.102051","DOIUrl":null,"url":null,"abstract":"<div>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: <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> (transfer constant), <math><msub><mi>V</mi><mi>e</mi></msub></math> (extravascular and extracellular space), and <math><msub><mi>V</mi><mi>i</mi></msub></math> (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 (<math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math>). No between-subject correlations were found between IHC biomarkers and DCE parameters, whereas we found multiple significant within-subject correlations: <math><msub><mi>V</mi><mi>e</mi></msub></math> and Ki-67 (<math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math> = -0.17, P < .001), <math><msub><mi>V</mi><mi>e</mi></msub></math> and HIF-1α (<math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math> = -0.12, P < .001), <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> and CD45 (<math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math> = 0.13, P < .001), <math><msub><mi>V</mi><mi>i</mi></msub></math> and CD45 (<math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math> = 0.16, P < .001), and <math><msub><mi>V</mi><mi>i</mi></msub></math> and Ki-67 (<math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math> = 0.08, P = .003). The strongest correlation was found between IHC biomarkers Ki-67 and HIF-1α (<math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math> = 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.</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":"{\"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>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: <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> (transfer constant), <math><msub><mi>V</mi><mi>e</mi></msub></math> (extravascular and extracellular space), and <math><msub><mi>V</mi><mi>i</mi></msub></math> (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 (<math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math>). No between-subject correlations were found between IHC biomarkers and DCE parameters, whereas we found multiple significant within-subject correlations: <math><msub><mi>V</mi><mi>e</mi></msub></math> and Ki-67 (<math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math> = -0.17, P < .001), <math><msub><mi>V</mi><mi>e</mi></msub></math> and HIF-1α (<math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math> = -0.12, P < .001), <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> and CD45 (<math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math> = 0.13, P < .001), <math><msub><mi>V</mi><mi>i</mi></msub></math> and CD45 (<math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math> = 0.16, P < .001), and <math><msub><mi>V</mi><mi>i</mi></msub></math> and Ki-67 (<math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math> = 0.08, P = .003). The strongest correlation was found between IHC biomarkers Ki-67 and HIF-1α (<math><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub></math> = 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.</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}","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

摘要

在本研究中，我们提出了一种方法，可逐个体素比较体内成像和免疫组化（IHC）生物标记物。作为概念验证，我们研究了动态对比增强（DCE-）CT 参数与 IHC 生物标记物 Ki-67（增殖）、HIF-1α（缺氧）和 CD45（免疫细胞）之间的空间相关性。对 15 例喉癌和下咽癌的 54 张全切肿瘤切片进行免疫组化染色和数字化处理。绘制了生物标记物阳性热图，并与 DCE-CT 参数图进行了登记。组织均匀性模型的绝热近似用于拟合以下 DCE 参数：Ktrans（转移常数）、Ve（血管外和细胞外空间）和 Vi（血管内空间）。IHC 和 DCE 图谱均下采样为 4 × 4 × 3 mm[3] 体素。每个肿瘤的平均值用于计算参数之间的受试者间相关性。对于受试者内（空间）相关性，使用重复测量相关性（rrm）比较肿瘤内所有体素的值。没有发现 IHC 生物标记物与 DCE 参数之间存在受试者间相关性，但我们发现了多个显著的受试者内相关性：Ve 与 Ki-67（rrm = -0.17，P < .001）、Ve 与 HIF-1α（rrm = -0.12，P < .001）、Ktrans 与 CD45（rrm = 0.13，P < .001）、Vi 与 CD45（rrm = 0.16，P < .001）、Vi 与 Ki-67（rrm = 0.08，P = .003）。IHC 生物标志物 Ki-67 与 HIF-1α 之间的相关性最强（rrm = 0.35，P < .001）。这项研究表明，确定组织病理学生物标记热图与活体成像之间的三维空间相关性在技术上是可行的。它还表明，受试者之间的相关性并不能反映受试者内部参数的相关性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

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

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: $K^{t r a n s}$ (transfer constant), $V_{e}$ (extravascular and extracellular space), and $V_{i}$ (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 ( $r_{r m}$ ). No between-subject correlations were found between IHC biomarkers and DCE parameters, whereas we found multiple significant within-subject correlations: $V_{e}$ and Ki-67 ( $r_{r m}$ = -0.17, P < .001), $V_{e}$ and HIF-1α ( $r_{r m}$ = -0.12, P < .001), $K^{t r a n s}$ and CD45 ( $r_{r m}$ = 0.13, P < .001), $V_{i}$ and CD45 ( $r_{r m}$ = 0.16, P < .001), and $V_{i}$ and Ki-67 ( $r_{r m}$ = 0.08, P = .003). The strongest correlation was found between IHC biomarkers Ki-67 and HIF-1α ( $r_{r m}$ = 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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Translational Oncology ONCOLOGY-

CiteScore

8.40

自引率

2.00%

发文量

314

审稿时长

54 days

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.