Lung cancer patient prognosis evaluation method and system in combination with pathological image and blood index

By combining pathological images with blood indicators to develop a lung cancer stage assessment model, the inaccuracy of lung cancer prognostic assessment in existing technologies has been solved, enabling accurate prediction and risk assessment of lung cancer patients' survival time and improving the reliability and stability of the assessment.

CN120998488APending Publication Date: 2025-11-21BEIJING CHEST HOSPITAL CAPITAL MEDICAL UNIV
8 Cites 0 Cited by

Patent Information

Application Number
CN202511023733.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-24
Publication Date
2025-11-21

AI Technical Summary

Technical Problem

现有技术中肺癌预后评估依赖人工经验，难以捕捉肿瘤的动态演进和治疗响应，未整合治疗过程中的患者状态时序变化信息，导致预后生存期预测的准确性和可靠性不足。

Method used

By combining pathological images and blood indicators, a lung cancer stage assessment model is constructed. Through machine learning and deep learning algorithms, the lung cancer development stage of patients is determined based on real-time pathological images and blood test reports. The proportion of blood lymphocytes and correlation indices are analyzed, and prognostic risk scores and survival prediction results are output.

Benefits of technology

It enables accurate prediction of the survival time of lung cancer patients, improves the objectivity and stability of the prediction results, eliminates the interference of human subjective factors, and enhances the reliability and practicality of the assessment.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a lung cancer patient prognosis evaluation method and system combining pathological images and blood indexes. The method comprises the steps that reference pathological features and reference blood clinical indexes of a lung cancer patient in different clinical stages are obtained, and a lung cancer stage evaluation model is constructed; acquiring a real-time pathological image and a blood detection report of the target patient, and determining a current lung cancer development stage of the target patient through the lung cancer stage evaluation model; determining the risk attribute of the target patient based on the current lung cancer development stage and determining the blood lymphocyte proportion of the target patient; determining a key blood index of the target patient according to the blood lymphocyte proportion, and outputting a prognosis risk score and a lifetime prediction result of the target patient through a preset survival analysis model according to the key blood index and the multi-scale morphological characteristics. The objectivity, the high precision and the stability of the prediction result are ensured, the interference of human subjective factors is eliminated, and the reliability is improved.
Need to check novelty before this filing date? Find Prior Art