Correlation between Imaging Features and Pathological Stages of Primary Lung Tumors Based on Nanocontrast Agents

Author:

Li Jin1,Xu Qian2,Mao Cunhua3,Liu Yuliang2ORCID

Affiliation:

1. Department of Radiology, Jinan Third People’s Hospital, Jinan, Shandong, China

2. Department of Image, Jinan Municipal Hospital of Traditional Chinese Medicine, Jinan, Shandong, China

3. Department of Radiology, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China

Abstract

As one of the conventional methods of lung cancer detection, computed tomography (CT) usually requires the use of contrast agents to enhance the imaging effect. Conventional iodine contrast agents have poor signal-to-noise ratio and are prone to adverse reactions. It is necessary to find more effective and safe contrast agents for CT scans. The gold nanoparticles with secondary electron effect and photoelectric absorption effect can prolong the display time of the patient’s blood circulation after being injected into the patient’s body, which makes the nanocontrast agent a research hotspot in the field of CT imaging. In this study, ultrasmall gold nanoclusters with a diameter of about 5 nm were used as the contrast agent in CT scans. It was found that CT scans based on nanocontrast agents can obtain high-quality lung cancer imaging images, and the patient has no obvious adverse reactions. When observing the CT image, it was found that the stage of lung cancer patients can be clearly distinguished through the CT scan image. When analyzing the consistency of CT imaging and pathological classification, the Kappa value was 0.810, indicating that the two have a high degree of consistency. Therefore, this study believes that the imaging characteristics of primary lung tumors based on nanocontrast agents are highly correlated with their pathological types.

Publisher

Hindawi Limited

Subject

Applied Mathematics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modeling and Simulation,General Medicine

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