Application of PET/CT image under convolutional neural network model in postoperative pneumonia virus infection monitoring of patients with non-small cell lung cancer

dc.contributor.author Wei, Jing
dc.contributor.author Zhu, Ronghua
dc.contributor.author Zhang, Huai
dc.contributor.author Li, Pingwei
dc.contributor.author Okasha, Ahmad
dc.contributor.author Muttar, Ahmed K.H.
dc.date.accessioned 2021-07-13T15:20:07Z
dc.date.available 2021-07-13T15:20:07Z
dc.date.copyright © 2021
dc.date.issued 2021-07
dc.description This article is not available at CUD collection. The version of scholarly record of this article is published in Results in Physics (2021), available online at: https://doi.org/10.1016/j.rinp.2021.104385 en_US
dc.description.abstract It was to study the adoption of positron emission computed tomography (PET-CT) based on the convolutional neural networks (CNN) model in the monitoring of postoperative pneumonia virus infection in patients with non-small cell lung cancer (NSCLC). 120 patients with NSCLC were set as the research object. CNN model was constructed and applied to PET-CT images to identify lesions and screen tumor markers for detection. Then, the patients were randomly divided into group A (CT), group B (PET-CT), group C (PET-CT based on artificial neural network model), and group D (PET-CT diagnosis based on CNN model), 30 cases in each group, and infection surveillance was conducted. The result showed that the accuracy (Acc), sensitivity (Sen), and specificity (Spe) of PET-CT image recognition based on the CNN model were 99.31%, 100%, and 98.31%, respectively. The proportion of serum neutrophils, white blood cell count, and PCT content in group D three days after operation were significantly lower than those in groups B, C, and A (P < 0.05). The proportions of patients with surgical wound infection and lung infection in group D were 6.54% and 15.38% respectively, which were significantly lower than those in groups B, C, and A (P < 0.05). The complication rates of patients in groups A, B, C, and D were 32.4%, 30.2%, 28.75, and 8.7%, respectively. The complication rate of patients in group D was significantly lower than that of the other three groups (P < 0.05). In short, PET-CT images based on the CNN model had high accuracy, sensitivity, and specificity in monitoring postoperative pneumonia virus infection in NSCLC patients. Applying it to the patient's virus infection monitoring can effectively prevent the patient's lung and surgical wound infection and improve the patient's postoperative recovery effect. © 2021 The Author(s) en_US
dc.identifier.citation Wei, J., Zhu, R., Zhang, H., Li, P., Okasha, A., & Muttar, A. K. H. (2021). Application of PET/CT image under convolutional neural network model in postoperative pneumonia virus infection monitoring of patients with non-small cell lung cancer. Results in Physics, 26. https://doi.org/10.1016/j.rinp.2021.104385 en_US
dc.identifier.issn 22113797
dc.identifier.uri https://doi.org/10.1016/j.rinp.2021.104385
dc.identifier.uri http://hdl.handle.net/20.500.12519/406
dc.language.iso en en_US
dc.publisher Elsevier B.V. en_US
dc.relation Authors Affiliations : Wei, J., Department of Nuclear Medicine, Huai'an First People's Hospital, Huai'an223300 Jiangsu, China; Zhu, R., Department of Nuclear Medicine, Huai'an First People's Hospital, Huai'an223300 Jiangsu, China; Zhang, H., Department of Nuclear Medicine, Huai'an First People's Hospital, Huai'an223300 Jiangsu, China; Li, P., Department of Radiotherapy, Huai'an First People's Hospital, Huai'an223300 Jiangsu, China; Okasha, A., Department of Environmental Health Sciences, Faculty of Communication, Arts and Sciences, Canadian University Dubai, Dubai, United Arab Emirates; Muttar, A.K.H., Applied Science University, Al Eker, Bahrain
dc.relation.ispartofseries Results in Physics;Volume 26, July 2021
dc.rights Creative Commons CC-BY-NC-ND License
dc.rights.holder Copyright : © 2021 The Author(s)
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Convolutional neural network en_US
dc.subject Neutrophils en_US
dc.subject Non-small cell lung cancer en_US
dc.subject Positron emission computed tomography en_US
dc.subject Tumor markers en_US
dc.title Application of PET/CT image under convolutional neural network model in postoperative pneumonia virus infection monitoring of patients with non-small cell lung cancer en_US
dc.type Article en_US
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