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Vaccination effect on patients with Delta variant of COVID-19 pneumonia: a study of longitudinal dynamic chest CTs using artificial intelligence model

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Chinese Journal of Academic Radiology Aims and scope Submit manuscript

Abstract

Background

The Delta variant of COVID-19 has emerged and spread globally since May 2021 and has been reported in more than 70 countries. The status of the vaccination, symptom onset time, and CT imaging signatures in the infected population have not been fully investigated and clarified.

Methods

This study included 131 patients who were infected with the Delta variant of COVID-19. After screening, 106 patients with 458 follow-up CT scans were retrospectively selected and divided into complete and incomplete vaccination groups (66 and 40 patients, respectively). Imaging features were automatically extracted, and infection distribution in lung fields and progression pattern tendency were investigated. Furthermore, we extracted the most related clinical and imaging features to establish a vaccination status classification model. An independent testing dataset with 55 patients from another inpatient ward was enrolled to evaluate the generalizability of the model.

Results

The severity of infection in the lung and lung fields of the complete vaccination group was overall lower than those of the incomplete vaccination group. A relatively earlier peak CT abnormality was found on days 8–11 in the complete vaccination group than in the incomplete vaccination group on days 12–15 after the first positive PCR time. The vaccination status classification model achieved the highest performance with an AUC of 0.929 and accuracy of 0.864 in the testing set and an AUC of 0.858 and accuracy of 0.727 in the independent testing set.

Conclusion

In summary, compared to the incomplete vaccination group, the fully vaccinated patients had milder CT abnormalities and earlier peak time for chest impairment. Therefore, the vaccination status is determinable through dynamic imaging and clinical features.

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Availability of data and materials

All data generated or analyzed during this study are included in this published paper.

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Acknowledgements

We would like to thank all of the team members in this study.

Funding

This study was supported in part by the 66th Batch of China Postdoctoral Science Foundation Projects (2019M661805) and a Research Grant of Key Project supported by Medical Science and Technology Development Foundation, Nanjing Department of Health (YKK18062), Jiangsu Province, China, and the Fundamental Research Funds for the Central Universities (021414380462, 021414380484). This work was funded in part by the National Science and Technology Innovation 2030-Major Project (2021ZD0111103).

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Author notes

  1. Xiaoyan Xin and Jun Hu contributed equally.

Authors and Affiliations

  1. Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China

    Xiaoyan Xin, Jun Hu, Xin Peng, Xin Zhang, Zhao Qing, Zhengge Wang, Xiaowei Han, Cong Long, Yongxiang Yi & Bing Zhang

  2. Department of Radiology, The Second Affiliated Hospital of Nanjing University of Chinese Medicine, No. 121 Jiangjia Yuan, Nanjing, 210003, Jiangsu, China

    Changhua Yi & Chao Du

  3. Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., 701 Yunjin Rd, Xuhui District, Shanghai, 200232, China

    Ying Wei, Yaozong Gao & Feng Shi

  4. Department of Pulmonary and Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, Jiangsu, China

    Jinghong Dai

  5. Department of Infectious Diseases, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, Jiangsu, China

    Jie Li

Authors
  1. Xiaoyan Xin
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  2. Jun Hu
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  7. Xin Peng
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  8. Xin Zhang
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  17. Bing Zhang
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Contributions

Conceptualization, BZ. Data curation, JD and YS. Formal analysis, XH and CL. Investigation, PL and ZW. Methodology, XX and JL. Project administration, YG. Resources, XZ. Software, JH. Supervision, CY, YY and BZ. Validation, XX. Writing—original draft, XX, JH, and YW. Writing—review and editing, XX, JH, YW, JD, JL, CY, XP, YS, XZ, PL, ZW, XH, CL, YY, YG, FS, and CD.

Corresponding authors

Correspondence to Chao Du or Bing Zhang.

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Conflict of interest

The authors declare that they have no competing interests.

Ethical approval and consent to participate

All methods were carried out in accordance with relevant guidelines and regulations. This study was approved by the ethics committees of the Second Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China. Informed consent was obtained from each participant before enrollment.

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Xin, X., Hu, J., Wei, Y. et al. Vaccination effect on patients with Delta variant of COVID-19 pneumonia: a study of longitudinal dynamic chest CTs using artificial intelligence model. Chin J Acad Radiol 7, 92–101 (2024). https://doi.org/10.1007/s42058-024-00143-2

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  • DOI: https://doi.org/10.1007/s42058-024-00143-2

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