Deep learning based pre-metastatic tumor lung immunosuppressive microenvironment assessment system

By integrating multidimensional omics data and simulating the positive feedback mechanism of interferon signals, the deep learning-based tumor lung metastasis pre-metastasis immunosuppressive microenvironment assessment system solves the problem that imaging technology cannot identify lung metastases in the early stage, realizes early and accurate warning and high-precision assessment, fills the window of opportunity for diagnosis and treatment, and reduces the risk of missed diagnosis.

CN122245799APending Publication Date: 2026-06-19STOMATOLOGY HOSPITAL OF HEBEI MEDICAL UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
STOMATOLOGY HOSPITAL OF HEBEI MEDICAL UNIV
Filing Date
2026-03-14
Publication Date
2026-06-19

AI Technical Summary

⚠Technical Problem

Existing imaging techniques cannot identify risks before the formation of lung metastases from tumors. Traditional liquid biopsies are difficult to assess the local immune microenvironment status of distant organs, resulting in a window of opportunity for diagnosis and treatment. Furthermore, traditional models are unable to accurately characterize the dynamic process of the immune microenvironment transitioning from a homeostatic to an inhibitory state.

⚗Method used

A deep learning-based assessment system for the pre-metastatic immunosuppressive microenvironment of tumor lung metastases was constructed. By transforming the positive feedback mechanism of TLR7/9-IFN signal induced by tumor exosomes into a deep learning model, and combining Ifi204 path switching weights and focus loss function, multidimensional omics data were integrated to simulate the positive feedback amplification process of interferon signal mediated by Stat1/Stat2, thereby achieving early identification and quantification of the pre-metastatic niche.

🎯Benefits of technology

Accurately predicting pre-metastatic niches of lung disease at least 3 weeks before radiographically visible metastatic lesions, improving the accuracy of identifying immunosuppressive microenvironment subtypes, reducing the risk of missed diagnoses, and providing interpretable clinical decision-making basis.

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Abstract

This application relates to a deep learning-based assessment system for the pre-metastatic immunosuppressive microenvironment of tumor lung metastases, comprising: a data acquisition and preprocessing unit, which acquires multi-omics data of lung tissue and extracts differentially expressed genes and metabolite features; a signal pathway dynamics modeling unit, which constructs a recurrent recurrent neural network based on the TLR7 / 9-IFN signal positive feedback mechanism, simulates the Stat1 / Stat2-mediated interferon signal amplification process, and calculates the interferon signal amplification index; an immune microenvironment decoding unit, which analyzes the immunosuppressive cell load through a deconvolution algorithm and introduces Ifi204 as the path switching weight between the TLR and STING pathways; and a deep learning fusion assessment unit, which performs multimodal fusion of the above features and optimizes training using a focus loss function, outputting a quantitative pre-metastatic lung microenvironment score and an interpretability report, thereby improving the accuracy of identifying immunosuppressive microenvironment subtypes and the ability to detect critical samples.
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