Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 May 24:2022:1852718.
doi: 10.1155/2022/1852718. eCollection 2022.

Computed Tomography Texture Features and Risk Factor Analysis of Postoperative Recurrence of Patients with Advanced Gastric Cancer after Radical Treatment under Artificial Intelligence Algorithm

Zhiwu Zhou  1 , Mei Zhang  2 , Chuanwen Liao  1 , Hong Zhang  1 , Qing Yang  3 , Yu Yang  3
Affiliations

Computed Tomography Texture Features and Risk Factor Analysis of Postoperative Recurrence of Patients with Advanced Gastric Cancer after Radical Treatment under Artificial Intelligence Algorithm

Zhiwu Zhou et al. Comput Intell Neurosci. .

Abstract

Computer tomography texture analysis (CTTA) based on the V-Net convolutional neural network (CNN) algorithm was used to analyze the recurrence of advanced gastric cancer after radical treatment. Meanwhile, the clinical characteristics of patients were analyzed to explore the recurrence factors. 86 patients who underwent the advanced radical gastrectomy for gastric cancer were retrospectively selected as the research objects. Patients were divided into the no-recurrence group (30 cases) and the recurrence group (56 cases) according to whether there was recurrence after radical treatment. CTTA was performed before and after surgery in both groups to analyze the risk factors for recurrence. The results showed that the dice coefficient (0.9209) and the intersection over union (IOU) value (0.8392) of the V-CNN segmentation effect were signally higher than those of CNN, V-Net, and context encoder network (CE-Net) (P < 0.05). The mean value of arterial phase and portal phase (65.29 ± 9.23)/(79.89 ± 10.83), kurtosis (3.22)/(3.13), entropy (9.99 ± 0.53)/(9.97 ± 0.83), and correlation (4.12 × 10-5/4.21 × 10-5) of the recurrence group was higher than the no-recurrence group, while the skewness (0.01)/(-0.06) of the recurrence group was lower than that of the no-recurrence group (P < 0.05). Patients aged 60 years old and above, with a tumor diameter of 6 cm and above, and in the stage III/IV in the recurrence group were higher than those in the no-recurrence group, and patients with chemotherapy were lower (P < 0.05). To sum up, age, tumor diameter, whether chemotherapy should be performed, and tumor staging were all the risk factors of postoperative recurrence among patients with gastric cancer. Besides, CT texture parameter could be used to predict and analyze the postoperative recurrence of gastric cancer with good clinical application values.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Comparison of the evaluation results of segmentation effects. ∗Comparison with V–CNN algorithm, P < 0.05.
Figure 2
Figure 2
Diagrams of segmentation effects. If the first line was set to be the first layer of the slice image, the first, second, and fourth gastric CT slice images were from top to bottom.
Figure 3
Figure 3
CT texture analysis results and recurrence CT images. ∗Comparison with recurrence group, P < 0.05.
Figure 4
Figure 4
Comparison of general data. ∗Comparison with recurrence group, P < 0.05.
Figure 5
Figure 5
Comparison of examination results.
Figure 6
Figure 6
Comparison of treatment methods. ∗Comparison with recurrence group,P < 0.05.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Sexton R. E., Al Hallak M. N., Diab M., Azmi A. S. Gastric cancer: a comprehensive review of current and future treatment strategies. Cancer and Metastasis Reviews . 2020;39(4):1179–1203. doi: 10.1007/s10555-020-09925-3. - DOI - PMC - PubMed
    1. Machlowska J., Baj J., Sitarz M., Maciejewski R., Sitarz R. Gastric cancer: epidemiology, risk factors, classification, genomic characteristics and treatment strategies. International Journal of Molecular Sciences . 2020;21(11):p. 4012. doi: 10.3390/ijms21114012.32512697 - DOI - PMC - PubMed
    1. Yuan L., Xu Z.-Y., Ruan S.-M., Mo S., Qin J.-J., Cheng X.-D. Long non-coding RNAs towards precision medicine in gastric cancer: early diagnosis, treatment, and drug resistance. Molecular Cancer . 2020;19(1):p. 96. doi: 10.1186/s12943-020-01219-0. - DOI - PMC - PubMed
    1. Coccolini F., Nardi M., Montori G., et al. Neoadjuvant chemotherapy in advanced gastric and esophago-gastric cancer. Meta-analysis of randomized trials. International Journal of Surgery . 2018;51:120–127. doi: 10.1016/j.ijsu.2018.01.008. - DOI - PubMed
    1. Li Z., Shan F., Ying X., et al. Assessment of laparoscopic distal gastrectomy after neoadjuvant chemotherapy for locally advanced gastric cancer. JAMA Surgery . 2019;154(12):1093–1101. doi: 10.1001/jamasurg.2019.3473. - DOI - PMC - PubMed