Abstract 363: Development of computer-aided detection (CAD) tool for liver metastasis micro CT imaging using targeted contrast agent
| Autor: | Noel R. Monks, Stephanie B. Scott, Ting-Tung Chang, David J. Monsma, Dawna Dylewski, Jeff VanOss, Samhita S. Rhodes, Anderson Peck, Craig P. Webb |
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| Rok vydání: | 2012 |
| Předmět: | |
| Zdroj: | Cancer Research. 72:363-363 |
| ISSN: | 1538-7445 0008-5472 |
| DOI: | 10.1158/1538-7445.am2012-363 |
| Popis: | Introduction: Preclinical in-vivo micro CT studies of liver metastasis are difficult due to poor inherent soft tissue contrast and the need for highly technical, manual analysis of the data. Research has implicated that Kupffer cells in the liver encapsulate liver metastases providing an opportunity to deliver macrophage-specific contrast agents for the detection of small metastatic lesions. A new, long-acting preclinical CT contrast agent that targets Kupffer cells has been developed that may allow automated detection of liver lesions via CAD software. Method: A pancreatic cancer liver metastasis model was created by surgically implanting human pancreatic cancer cell line (L3.6pl) within the spleen. Mice were injected with the contrast agent and scanned to obtain baseline anatomy of the liver before implantation. The mice were scanned 1 day after implantation and weekly after that for 5 weeks to monitor the liver metastasis progression. The control group followed an identical protocol but with a sham surgery. Liver tissues were harvested and fixed in paraffin blocks after the last scan. Paraffin blocks were scanned using high resolution micro CT before IHC staining. Human Mitochondrial and F4/80 IHC were used to identify L3.6pl and Kupffer cells, respectively. The CT images were compared to the IHC images from the same block to verify that the locations of the contrast agent and the Kupffer cells were related. Once the pattern of contrast agent and metastatic tumors had been identified, CAD software was developed for automatic tumor detection. Results: The contrast agent was evenly distributed throughout the healthy liver tissue within 1 hour post injection. In healthy mice, the homogenous distribution of contrast remained unchanged for at least 6 weeks. In liver metastasis models, the contrast began to concentrate in various areas of the liver within 2 weeks post implantation. As tumors developed and grew, the contrast became highly concentrated on the borders of tumors creating a 3 dimensional outline of the lesion. IHC staining and micro CT imaging of the fixed tissue verified that the tumors are surrounded by Kupffer cells and that the distribution of concentrated contrast agent matched them. Software was able to detect the tumors based on these contrast outlines and compare them over successive weekly scans. Conclusion: Our new imaging method enables automated detection and evaluation of liver metastasis 1 mm or smaller from as early as 2 weeks. In addition to allowing better visualization, it provides new insight into macrophage motility within the liver. CAD software can take advantage of this unique capability to automate data analysis and allow for large scale longitudinal studies. This new imaging method could be a useful tool to facilitate longitudinal imaging of liver metastases in mice and has the potential for translation into clinical practice. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 363. doi:1538-7445.AM2012-363 |
| Databáze: | OpenAIRE |
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