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

Simple item page
dc.contributor.authorWei, Jing
dc.contributor.authorZhu, Ronghua
dc.contributor.authorZhang, Huai
dc.contributor.authorLi, Pingwei
dc.contributor.authorOkasha, Ahmad
dc.contributor.authorMuttar, Ahmed K.H.
dc.date.accessioned2021-07-13T15:20:07Z
dc.date.available2021-07-13T15:20:07Z
dc.date.copyright© 2021
dc.date.issued2021-07
dc.descriptionThis 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.2021.104385en_US
dc.description.abstractIt 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.citationWei, 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.2021.104385en_US
dc.identifier.issn22113797
dc.identifier.urihttps://doi.org/10.1016/j.rinp.2021.104385
dc.identifier.urihttp://hdl.handle.net/20.500.12519/406
dc.language.isoenen_US
dc.publisherElsevier B.V.en_US
dc.relationAuthors 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.ispartofseriesResults in Physics;Volume 26, July 2021
dc.rightsCreative Commons CC-BY-NC-ND License
dc.rights.holderCopyright : © 2021 The Author(s)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectConvolutional neural networken_US
dc.subjectNeutrophilsen_US
dc.subjectNon-small cell lung canceren_US
dc.subjectPositron emission computed tomographyen_US
dc.subjectTumor markersen_US
dc.titleApplication of PET/CT image under convolutional neural network model in postoperative pneumonia virus infection monitoring of patients with non-small cell lung canceren_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Access Instruction 406.pdf
Size:
56.31 KB
Format:
Adobe Portable Document Format
Description:
Download

Collections

Department of Environmental Health Sciences