Medical image-based tumor microenvironment state analysis method, system and device

By combining tumor pathology images and gene expression data, a weakly supervised learning framework model for predicting immune resistance mechanisms was constructed. This solved the problems of accuracy and cost in predicting immune resistance mechanisms in tumor treatment in existing technologies, and achieved efficient and accurate analysis of immune resistance mechanisms.

CN122244494APending Publication Date: 2026-06-19NAT HEALTH COMMISSION INST OF SCI & TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
NAT HEALTH COMMISSION INST OF SCI & TECH
Filing Date
2026-01-30
Publication Date
2026-06-19

AI Technical Summary

⚠Technical Problem

Existing technologies struggle to accurately predict immune resistance mechanisms in cancer treatment, especially in immune checkpoint inhibitor therapy. Traditional methods rely on expensive and hard-to-obtain transcriptome data, which cannot distinguish between different resistance mechanisms, and the results are disconnected from pathological morphology.

⚗Method used

By processing tumor pathological tissue images and combining them with gene expression data, a weakly supervised learning framework model for predicting immune drug resistance mechanisms is constructed. Pathologically related gene sequences are used as labels to extract semantic features from image patches. The number of model layers is dynamically set to generate a feature representation of immune drug resistance mechanisms.

🎯Benefits of technology

It achieves efficient and accurate prediction of the immune drug resistance mechanism in biological individuals, reduces detection costs, is applicable to multi-center and multi-device datasets, and improves the accuracy and generalization ability of prediction.

✦ Generated by Eureka AI based on patent content.

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Abstract

This application discloses a method, system, and device for analyzing the state of the tumor microenvironment based on medical images, relating to the field of medical image processing technology. First, by acquiring medical images and gene expression data of related tissues within those images, the medical images are preprocessed to obtain image patches, and semantic features of these patches are extracted. An algorithm is then used to calculate a predefined immune resistance mechanism that matches the gene expression data as the dominant immune resistance mechanism. This dominant immune resistance mechanism is then set as a supervisory label for the model, and a prediction model is constructed. The model is trained using a large amount of data, and the medical image to be analyzed is input into the trained prediction model to obtain the predicted category of the resistance mechanism. This application can accurately predict the resistance mechanism of pathological tissues based on medical images and their related gene expression data.
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