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    Home > Active Ingredient News > Antitumor Therapy > Researchers propose a predictive method for predicting the efficacy of neoadjuvant chemotherapy for advanced gastric cancer

    Researchers propose a predictive method for predicting the efficacy of neoadjuvant chemotherapy for advanced gastric cancer

    • Last Update: 2022-10-01
    • Source: Internet
    • Author: User
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    Gastric cancer is a common malignant tumor originating from the epithelial epithelium of the gastric mucosa, its early symptoms are insidious and non-specific, often similar to the symptoms of chronic diseases such as gastritis and gastric ulcers, and are easily ignored, resulting in 80%-90% of gastric cancer patients being advanced when they are first diagnosed
    .

    At present, the main treatment for patients with advanced gastric cancer is surgery, but the prognosis is poor, and the 5-year survival rate is only 30%.


    In recent years, neoadjuvant chemotherapy has achieved certain effects in improving the prognosis of patients with advanced gastric cancer, which is accepted
    by surgeons and patients.

    However, about 30% of patients with advanced gastric cancer are still unable to benefit from neoadjuvant chemotherapy and are exposed to the risk of disease progression, additional physical damage and high treatment costs
    .

    Although postoperative histopathological examination is the gold standard for the evaluation of the efficacy of neoadjuvant chemotherapy, it is obviously lagging behind and does not have the guiding value
    of clinical treatment plan optimization.

    Therefore, it is crucial
    to accurately identify patients with neoadjuvant chemotherapy resistance to advanced gastric cancer before treatment.


    In the early stage, Gao Xin's team from the Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, together with Shanxi Provincial Cancer Hospital, proposed for the first time a predictive method for predicting
    the efficacy of neoadjuvant chemotherapy for advanced gastric cancer based on artificial intelligence.

    On this basis, the team proposes a new method
    for predicting efficacy based on the intelligent calculation technology of medical imaging based on the identification problem of neoadjuvant chemotherapy resistance patients in advanced gastric cancer.

    Through the ResNet-50 neural network architecture, the team automatically excavated the high-dimensional features of tumor images, and at the same time, with the help of multi-channel image input strategy, deeply fused the spatial features of tumor images, and used tumor boundary information guidance network to focus on the lesion area, and finally constructed an end-to-end predictive model of neoadjuvant chemotherapy for advanced gastric cancer (Figure 1).


    The study used CT image data from 633 patients with advanced gastric cancer in three hospitals for model training and validation
    .


    The results show that the constructed model has high accuracy and strong generalization, and the prediction accuracy is greater than 0.
    75 on both internal and external test sets, which is the best predictive model of neoadjuvant chemotherapy for end-to-end progressive gastric cancer in the current report
    .

    In addition, in order to further visualize the interpretability of the model, the team quantified the correspondence
    between tumor imaging and chemotherapy resistance through visualization.

    The uneven activation region of the tumor internal model provides a reference for revealing the implicit association between tumor heterogeneity and chemotherapy resistance (Figure 2).


    The results were published in Gastric Cancer
    .

    The research work has been funded
    by the National Natural Science Foundation of China and other institutions.


    Figure 1 Flow of new methods for predicting the efficacy of neoadjuvant chemotherapy for advanced gastric cancer

    Figure 2 Visualization of the efficacy prediction model of neoadjuvant chemotherapy for advanced gastric cancer

    Source: Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences


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