INFIERI2019@ HUST Cell research Animal research Three-dimensional visualization model of PEG-NGO-MAG3 molecularprobes based on CZT pixel detection in diagnosis and treatment of lung cancer stem Shan-shan Qin, Wen-ting Xie, Han Zhang, Xin Fan, Jia-jia Zhang, Yu-zhen Yin, Fei Yu Department of Nuclear Medicine, Shanghai Tenth People’s Hospital Abstraction 2) cell transcriptome sequence label barcode technology was used to label and immunomagnetic beads tumor stem cells with different surface markers Lung cancer stem cells (LCSCs) have become a research focus due to their close correlation with lung cancer recurrence and radiochemotherapy resistance. However, effective in-body imaging and dynamic quantitative monitoring of the survival, distribution and different phenotypic expression of LCSCs are still lacking. In our previous study, radionuclides (99mTc, 188Re and 211At) labeled somatostatin analogues were synthesized for the treatment of non-small cell lung cancer (NSCLC) and successfully proved that it can induce apoptosis in lung cancer cells.On this foundation, we proposed further selection of LCSCs special surface markers, combined with polyethylene glycol nano-graphene oxide (PEG-NGO) retention effect developed nuclide and chelating PEG-NGO LCSCs surface marker CD133/CD44/ABCG2 build new molecular probes, then the key molecule in the process of the evolvement of lung cancer will be integrated bioinformatics, to build a model of radiomics diagnosis, therapeutic effect monitoring and prognosis prediction based on the high resolution cadmium zinc telluride (CZT) pixels to capture molecular probe images. Keywords: Visualization; Cadmium zinc telluride; Radiomics; Lung cancer stem cells; Nuclide Introduction At present, lung cancer is still one of the tumors with high morbidity, high mortality and the worst prognosis worldwide. The overall survival rate of patients is only 18%, which seriously threatens human health. The US national comprehensive cancer network (NCCN) recommends the use of low-dose CT as its screening tool and surgery plus radiotherapy as its primary treatment. Although traditional treatment can eliminate some cancer cells, recurrence is inevitable due to the presence of lung cancer stem cells (LCSCs). There is growing evidence that tumor stem cells drive the development and progression of many types of cancer, including lung cancer. There is substantial overlap between embryonic stem cells (ESCs) and cancer stem cells (CSCs) in terms of self-renewal, pluripotency, proliferation and differentiation regulation, which is controlled by similar signaling pathways, such as Wnt, Notch, and Hedgehog (Hh). LCSCs are a group of lung malignant tumor cells with the characteristics of self-renewal, differentiation potential, high tumorigenicity, high drug resistance, etc., which have been proved to be the predictors of poor prognosis of lung cancer 3,4. Although the existing treatment can reduce the majority of lung cancer tumor cells, in a small number of tumor cell subgroups resistant to treatment, the presence of LCSCs is equivalent to the reservoir of tumor cells, which can replenish the number of tumor cells at any time, leading to the recurrence of tumor continuously 4,6. Therefore, only by completely eliminating LCSCs at the root can the tumor be cured. Therefore, how to identify the presence of LCSCs becomes the key to further treatment of lung cancer. 1) construction and characterization of polyethylene glycol (PEG) -go (NGO) nanocaptor targeting tumor LCSCs. 4) in vivo experiments :animal experiments 3) in vitro experiment: cell function experiment (cytotoxicity, cell binding rate, cell migration, invasion) prolification assays cell migration and invasion assays toxicity apoptosis assays Cell research 4) in vivo experiments :animal experiments In vivo biological distribution and organ tolerance construct lung cancer bearing mice CZT crystal correlation study molecular probe imaging of tumor bearing mice In vivo study of 211At in the treatment of LCSCs image reconstruction and feature extraction Animal research Methods Acknowledgments This work was supported by the Shanghai Talents Development Foundation (Grant no.2017103); the National Key R&D Program of China (Grant no.2016YFC0104303); the National Natural Science Fund (Grant no.81771859). 5TH SUMMER SCHOOL ON INTELLIGENT SIGNAL PROCESSING FOR FRONTIER RESEARCH AND INDUSTRY Huazhong University of Science & Technology Wuhan National Laboratory for Optoelectronics Digital PET Imaging Lab