摘要
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-morpho-logical 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 characteris-tics 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 microenviron-ments,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.
基金
supported by the Natural Science Foundation of Shandong Province,China(No.ZR2020MA088)
Natural Science Foundation of Xinjiang Uygur Autonomous Region,China(No.2019D01C188)
National Key Research and Development Program of China(No.2018YFC1200204)
National Natural Science Foundation of China(No.12175127).
