Multi-modality measurement and comprehensive analysis of hepatocellular carcinoma using synchrotron-based microscopy and spectroscopy
Autor: | Yanling Xue, Huiqiang Liu, Sui-Xia Zhang, Te Ji, Gongxiang Wei, Zhao Li, Fu-Li Wang |
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Rok vydání: | 2021 |
Předmět: | |
Zdroj: | Nuclear Science and Techniques. 32 |
ISSN: | 2210-3147 1001-8042 |
Popis: | The visualization and data mining of tumor multidimensional information may play a major role in the analysis of the growth, metastasis, and microenvironmental changes of tumors while challenging traditional imaging and data processing techniques. In this study, a general trans-scale and multi-modality measurement method was developed for the quantitative diagnosis of hepatocellular carcinoma (HCC) using a combination of propagation-based phase-contrast computed tomography (PPCT), scanning transmission soft X-ray microscopy (STXM), and Fourier transform infrared micro-spectroscopy (FTIR). Our experimental results reveal the trans-scale micro-morphological HCC pathology and facilitate quantitative data analysis and comprehensive assessment. These results include some visualization features of PPCT-based tissue microenvironments, STXM-based cellular fine structures, and FTIR-based bio-macromolecular spectral characteristics during HCC tumor differentiation and proliferation. The proposed method provides multidimensional feature data support for constructing a high-accuracy machine learning algorithm based on a gray-level histogram, gray-gradient co-occurrence matrix, gray-level co-occurrence matrix, and back-propagation neural network model. Multi-dimensional information analysis and diagnosis revealed the morphological pathways of HCC pathological evolution and we explored the relationships between HCC-related feature changes in inflammatory microenvironments, cellular metabolism, and the stretching vibration peaks of biomolecules of lipids, proteins, and nucleic acids. Therefore, the proposed methodology has strong potential for the visualization of complex tumors and assessing the risks of tumor differentiation and metastasis. |
Databáze: | OpenAIRE |
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