Detection method based on reduction of lung self-fluorescence as mark for detecting lung cancer tissue and application thereof

Abstract

The present invention provides a detection method based on detecting the autofluorescence level of lung tissue as a detection method for lung cancer tissue, comprising the following steps: (1) detection of lung tissue under single-photon 400-700nm excitation light or two-photon 700-1000nm excitation light Under excitation, the emitted wavelength is autofluorescence of 420-800nm; (2) compare the autofluorescence intensity of various parts of the lung tissue in step (1), and compare the autofluorescence in step (1) with the known lung cancer patient Compared with the autofluorescence intensity results of the non-lung cancer population; (3) The location and size of the lung cancer tissue can be determined according to the above comparison results. The intensity of autofluorescence light emitted by lung tissue with a wavelength of 420-800nm ​​is negatively correlated with the possibility of the tissue being lung cancer tissue: the part with higher autofluorescence intensity is normal lung tissue, while the lower the intensity of autofluorescence light The more likely the part is lung cancer tissue.

Description

technical field [0001] The invention relates to the detection of biological autofluorescence of lung cancer tissue, specifically based on the detection of the reduction of the intensity of autofluorescence of lung tissue, as a detection method and application thereof as a marker for detection of lung cancer tissue. Background technique [0002] Lung cancer is a malignant tumor with the highest morbidity and mortality in the world and also in my country. It has the characteristics of high malignancy, rapid progression, and poor curative effect. For a person with suspected symptoms of lung cancer for the first time, a chest x-ray can reveal significant masses, dilated mediastinum, and atelectasis. CT images can provide more disease information. Bronchoscopy and CT are often used to extract tumor samples for histopathological analysis. On a chest radiograph, lung tumors are generally seen in the form of solitary pulmonary nodules. However, as many other diseases also have su...

AI Technical Summary

Problems solved by technology

But the main problem is that most people do not have the habit of regularly detecting lung cancer

At the same time, CT images are not convenient for everyone to detect

However, the current detection methods for lung cancer are limited to these imaging methods, as well as rough judgments such as cough, dyspnea, hemoptysis, general weight loss and anorexia.

Very inconvenient for patients, relatively time-consuming and expensive

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