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The Application of Imaging in Differentiating Benign and Malignant Pulmonary Nodules

Chongkun Kang, Shenyang Zhou

Abstract


Pulmonary nodules are important targets for the early screening and diagnosis of lung cancer. With the continuous development of
imaging techniques, including low-dose CT (LDCT), high-resolution CT (HRCT), PET-CT, and MRI, they play a key role in the detection,
classification, and differentiation of benign and malignant pulmonary nodules. In addition, the introduction of new technologies such as artificial intelligence (AI), computer-aided diagnosis (CAD), and radiomics provides new possibilities for the precise diagnosis of pulmonary nodules. However, imaging diagnosis still faces challenges such as misjudgment and high false positive rates. The future development direction
lies in optimizing imaging techniques, promoting multi-modal image fusion, and improving the interpretability of AI algorithms to enhance
the diagnostic accuracy of pulmonary nodules.

Keywords


Pulmonary nodules; Imaging; Benign and malignant differentiation; Artificial intelligence; Radiomics

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References


[1] Zhou Q, Fan Y, Wang Y, Qiao Y, Wang G, Huang Y, Wang X, Wu N, Zhang G, Zheng X, Bu H. [China National Guideline of Classification, Diagnosis and Treatment for Lung Nodules (2016 Version)]. Zhongguo Fei Ai Za Zhi. 2016 Dec 20;19(12):793-798. Chinese.

doi: 10.3779/j.issn.1009-3419.2016.12.12. Erratum in: Zhongguo Fei Ai Za Zhi. 2023 Jul 20;26(7):558. doi: 10.3779/j.issn.1009-3419

.2023.103.02. PMID: 27978863; PMCID: PMC5973458

[2] Chinese Expert Group on Early Diagnosis and Treatment of Lung Cancer; China Lung Oncology Group. [China National Lung Cancer

Screening Guideline with Low-dose Computed Tomography (2023 Version)]. Zhongguo Fei Ai Za Zhi. 2023 Jan 20;26(1):1-9. Chinese.

doi: 10.3779/j.issn.1009-3419.2023.102.10. PMID: 36792074; PMCID: PMC9987116.

[3] Guo Jindong, Sun Xiwen The correlation between high-resolution CT imaging features of pure ground glass nodules in the lungs

and a new pathological classification of lung adenocarcinoma [J]. Chinese Clinical Medicine, 2016, 23(4): 449-453. DOI: 10.12025/

j.issn.1008-6358.2016.20160411

[4] Chinese Medical Association Radiology Branch, Chinese Medical Association Nuclear Medicine Branch Expert Consensus on Quality

Control of 18F-FDG PET-CT Data Collection and Labeling for Pulmonary Nodules (2024 Edition) [J]. Chinese Journal of Radiology,

2024, 58 (03): 258-265. DOI: 10.3760/cma.jcn112149-20230831-00147

[5] Zhang Jingyu, Xiong Ziqi, Li Zhiyong Research progress and prospects of ultra short echo time magnetic resonance imaging for pulmonary nodules [J]. Magnetic resonance imaging, 2023, 14(1): 183-188. DOI: 10.12015/issn.1674-8034.2023.01.034.

[6] Zhao Q, Kong P, Min J, Zhou Y, Liang Z, Chen S, Li M. [A review of deep learning methods for the detection and classification

of pulmonary nodules]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2019 Dec 25;36(6):1060-1068. Chinese. doi: 10.7507/1001-

5515.201903027. PMID: 31875384; PMCID: PMC9935178.

[7] Shi Z, Zhang X, Jiang T. [Study Progress of Radiomics in Precision Medicine for Lung Cancer]. Zhongguo Fei Ai Za Zhi. 2019 Jun

20;22(6):385-388. Chinese. doi: 10.3779/j.issn.1009-3419.2019.06.09. PMID: 31196373; PMCID: PMC6580079.

[8] Thoracic Surgery Committee, Department of Simulated Medicine, Wu Jieping Medical Foundation. [Chinese Experts Consensus on Artificial Intelligence Assisted Management for Pulmonary Nodule (2022 Version)]. Zhongguo Fei Ai Za Zhi. 2022 Apr 20;25(4):219-225.

Chinese. doi: 10.3779/j.issn.1009-3419.2022.102.08. Epub 2022 Mar 28. PMID: 35340198; PMCID: PMC9051301.

[9] Liu C, Cui Y. [Lung Nodules Assessment--Analysis of Four Guidelines]. Zhongguo Fei Ai Za Zhi. 2017 Jul 20;20(7):490-498. Chinese.

doi: 10.3779/j.issn.1009-3419.2017.07.08. PMID: 28738966; PMCID: PMC5972948.

[10] Cheng Xiaoguang, Li Yongli, Guo Zhiping. Application of low-dose chest CT combined with quantitative CT in health management [J].

Chinese Journal of Health Management, 2022, 16 (09): 593-595. DOI: 10.3760/cmaj.cn115624-2012119-00776




DOI: http://dx.doi.org/10.70711/mhr.v2i6.6845

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