The Efficacy of 18F-FDG PET/CT and Superparamagnetic Nanoferric Oxide MRI in the Diagnosis of Lung Cancer and the Value of 18F-FDG PET/CT in the Prediction of Lymph Node Metastasis

Author:

Yao Lan1,Zuo Mingfei2,Zhang Na2,Bai Tian2,Huang Qicheng2ORCID

Affiliation:

1. Department of Nuclear Medicine, The Third Affiliated Hospital of Qiqihar Medical University, Qiqihar, Heilongjiang, China

2. The Center of Image, The Third Affiliated Hospital of Qiqihar Medical University, Qiqihar, Heilongjiang, China

Abstract

In China, lung cancer is one of the leading causes of death among residents. Early diagnosis is of great significance for early interventional treatment and prolonging survival. PET/CT uses positron radiopharmaceuticals to observe the physiological and biochemical changes of the drug and its metabolites in the body and finally diagnoses the disease. 18F-FDG is a commonly used imaging agent, but its short isotopic half-life limits clinical high-throughput testing. This study retrospectively analyzed the imaging material of 100 lung cancer patients pathologically confirmed. Patients with lymph node metastasis were classified into the LM group ( n = 30 cases), and those with no lymph node metastasis were classified into the NLM group ( n = 70 cases). The results showed that MRI of superparamagnetic nanoferric oxide was better than diagnosis of lung cancer by the 18F-FDG PET/CT and had a high predictive power for lymph node metastasis. These turned out to be high-value lung cancer diagnosis of superparamagnetic nanoferric oxide MRI and high-capacity lymph node metastasis prediction of 18F-FDG PET/CT, which were worthy of implementation.

Funder

Qiqihar Academy of Medical Sciences

Publisher

Hindawi Limited

Subject

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

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Auxiliary Diagnosis of Lung Cancer with Magnetic Resonance Imaging Data under Deep Learning;Computational and Mathematical Methods in Medicine;2022-05-04

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