Skip to main content
SpringerLink
Log in

Predicting oxygen needs in COVID-19 patients using chest radiography multi-region radiomics

  • Research Article
  • Published:
Radiological Physics and Technology Aims and scope Submit manuscript

Abstract

The objective is to evaluate the performance of blood test results, radiomics, and a combination of the two data types on the prediction of the 24-h oxygenation support need for the Coronavirus disease 2019 (COVID-19) patients. In this retrospective cohort study, COVID-19 patients with confirmed real-time reverse transcription-polymerase chain reaction assay (RT-PCR) test results between February 2020 and August 2021 were investigated. Initial blood cell counts, chest radiograph, and the status of oxygenation support used within 24 h were collected (n = 290; mean age, 45 ± 19 years; 125 men). Radiomics features from six lung zones were extracted. Logistic regression and random forest models were developed using the clinical-only, radiomics-only, and combined data. Ten repeats of fivefold cross-validation with bootstrapping were used to identify the input features and models with the highest area under the receiver operating characteristic curve (AUC). Higher AUCs were achieved when using only radiomics features compared to using only clinical features (0.94 ± 0.03 vs. 0.88 ± 0.04). The best combined model using both radiomics and clinical features achieved highest in the cross-validation (0.95 ± 0.02) and test sets (0.96 ± 0.02). In comparison, the best clinical-only model yielded AUCs of 0.88 ± 0.04 in cross-validation and 0.89 ± 0.03 in test set. Both radiomics and clinical data can be used to predict 24-h oxygenation support need for COVID-19 patients with AUC > 0.88. Moreover, the combination of both data types further improved the performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

The datasets generated or analyzed during the study are not publicly available due to ethical restrictions and confidentiality agreements but are available from the corresponding author on reasonable request.

References

  1. World Health Organization. Origin of SARS-COV-2. World Health Organization; 2020. https://www.who.int/publications/i/item/origin-of-sars-cov-2. Accessed 1 Mar 2023.

  2. Xu Z, Shi L, Wang Y, et al. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med. 2020;8(4):420–2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. World Health Organization. Severe acute respiratory infections treatment centre: practical manual to set up and manage a SARI treatment centre and a SARI screening facility in health care facilities. World Health Organization; 2020. https://www.who.int/publications/i/item/10665-331603. Accessed 1 Mar 2023.

  4. World Health Organization. Diagnostic testing for SARS-CoV-2: interim guidance. World Health Organization; 2020. https://iris.who.int/handle/10665/334254. Accessed 1 Mar 2023.

  5. Stephanie S, Shum T, Cleveland H, et al. Determinants of chest x-ray sensitivity for COVID-19: a multi-institutional study in the United States. Radiol: Cardiothorac Imaging. 2020;2(5): e200337.

  6. Rubin GD, Ryerson CJ, Haramati LB, et al. The role of chest imaging in patient management during the COVID-19 pandemic: a multinational consensus statement from the Fleischner Society. Radiology. 2020;296(1):172–80.

    Article  PubMed  Google Scholar 

  7. World Health Organization. Use of chest imaging in COVID-19: a rapid advice guide. World Health Organization; 2020. https://www.who.int/publications/i/item/use-of-chest-imaging-in-covid-19. Accessed 1 Mar 2023.

  8. World Health Organization. Infection prevention and control of epidemic-and pandemic-prone acute respiratory infections in health care: World Health Organization; 2014. https://www.who.int/publications/i/item/infection-prevention-and-control-of-epidemic-and-pandemic-prone-acute-respiratory-infections-in-health-care. Accessed 1 Mar 2023.

  9. Smith DL, Grenier J-P, Batte C, et al. A characteristic chest radiographic pattern in the setting of the COVID-19 pandemic. Radiol: Cardiothorac Imaging. 2020;2(5):e200280.

  10. Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magn Reson Imaging. 2012;30(9):1234–48.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749–62.

    Article  PubMed  Google Scholar 

  12. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–77.

    Article  PubMed  Google Scholar 

  13. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441–6.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Mayerhoefer ME, Materka A, Langs G, et al. Introduction to radiomics. J Nucl Med. 2020;61(4):488–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Aerts HJ. The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol. 2016;2(12):1636–42.

    Article  PubMed  Google Scholar 

  16. Attallah O. RADIC: a tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics. Chemom Intell Lab Syst. 2023;233: 104750.

    Article  CAS  Google Scholar 

  17. Hu Z, Yang Z, Lafata KJ, et al. A radiomics-boosted deep-learning model for COVID-19 and non-COVID-19 pneumonia classification using chest x-ray images. Med Phys. 2022;49(5):3213–22.

    Article  CAS  PubMed  Google Scholar 

  18. Homayounieh F, Ebrahimian S, Babaei R, et al. CT radiomics, radiologists, and clinical information in predicting outcome of patients with COVID-19 pneumonia. Radiol: Cardiothorac Imaging. 2020;2(4):e200322.

  19. Shiri I, Salimi Y, Pakbin M, et al. COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: analysis of a multi-institutional dataset of 14,339 patients. Comput Biol Med. 2022;145: 105467.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Huang G, Hui Z, Ren J, et al. Potential predictive value of CT radiomics features for treatment response in patients with COVID-19. Clin Respir J. 2023;17(5):394–404.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Sun Y, Salerno S, He X, et al. Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality. Sci Rep. 2023;13(1):7318.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Xiao F, Sun R, Sun W, et al. Radiomics analysis of chest CT to predict the overall survival for the severe patients of COVID-19 pneumonia. Phys Med Biol. 2021;66(10): 105008.

    Article  CAS  Google Scholar 

  23. Toussie D, Voutsinas N, Finkelstein M, et al. Clinical and chest radiography features determine patient outcomes in young and middle-aged adults with COVID-19. Radiology. 2020;297(1):E197–206.

    Article  PubMed  Google Scholar 

  24. Holshue ML, DeBolt C, Lindquist S, et al. First case of 2019 novel coronavirus in the United States. N Engl J Med. 2020;382(10):929–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Weiss P, Murdoch DR. Clinical course and mortality risk of severe COVID-19. Lancet. 2020;395(10229):1014–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Borghesi A, Maroldi R. COVID-19 outbreak in Italy: experimental chest X-ray scoring system for quantifying and monitoring disease progression. Radiol Med. 2020;125(5):509–13.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Sandler M, Howard A, Zhu M, et al. MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2018. pp. 4510–20.

  28. Deng J, Dong W, Socher R, et al. ImageNet: a large-scale hierarchical image database. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE; 2009. pp. 248–55.

  29. Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D Slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging. 2012;30(9):1323–41.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Van Griethuysen JJ, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21):e104–7.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Aerts HJWL, Velazquez ER, Leijenaar RTH, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5(1):4006.

    Article  CAS  PubMed  Google Scholar 

  32. Peng C-YJ, Lee KL, Ingersoll GM. An introduction to logistic regression analysis and reporting. J Educ Res. 2002;96(1):3–14.

  33. Boulesteix AL, Janitza S, Kruppa J, et al. Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. Wiley Interdiscipl Rev: Data Mining Knowl Discov. 2012;2(6):493–507.

    Google Scholar 

  34. Shur JD, Doran SJ, Kumar S, et al. Radiomics in oncology: a practical guide. Radiographics. 2021;41(6):1717–32.

    Article  PubMed  Google Scholar 

  35. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the international joint conference on artificial intelligence; 1995. pp. 1137–43.

  36. Bae J, Kapse S, Singh G, et al. Predicting mechanical ventilation and mortality in COVID-19 using radiomics and deep learning on chest radiographs: a multi-institutional study. Diagnostics. 2021;11(10):1812.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Aljouie AF, Almazroa A, Bokhari Y, et al. Early prediction of COVID-19 ventilation requirement and mortality from routinely collected baseline chest radiographs, laboratory, and clinical data with machine learning. J Multidiscip Healthc. 2021;14:2017–33.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

The authors declare that no funds or grants were received during the preparation of this manuscript.

Author information

Author notes

  1. Sa-angtip Netprasert and Sararas Khongwirotphan have contributed equally to this work.

Authors and Affiliations

  1. Medical Physics Program, Department of Radiology, Faculty of Medicine, Chulalongkorn, University, Bangkok, Thailand

    Sa-angtip Netprasert

  2. Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand

    Sa-angtip Netprasert, Sararas Khongwirotphan, Roongprai Seangsawang, Supanuch Patipipittana & Yothin Rakvongthai

  3. Department of Radiological Technology and Medical Physics, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand

    Sararas Khongwirotphan, Roongprai Seangsawang & Supanuch Patipipittana

  4. Division of Infectious Diseases, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand

    Watsamon Jantarabenjakul & Thanyawee Puthanakit

  5. Center of Excellence for Pediatric Infectious Diseases and Vaccines, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand

    Watsamon Jantarabenjakul & Thanyawee Puthanakit

  6. Thai Red Cross Emerging Infectious Diseases Clinical Center, King Chulalongkorn Memorial Hospital, Bangkok, Thailand

    Watsamon Jantarabenjakul

  7. Department of Radiology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand

    Wariya Chintanapakdee

  8. Center of Excellence in Computational Molecular Biology, Chulalongkorn University, Bangkok, Thailand

    Sira Sriswasdi

  9. Center for Artificial Intelligence in Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand

    Sira Sriswasdi

  10. Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand

    Yothin Rakvongthai

Authors
  1. Sa-angtip Netprasert
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sararas Khongwirotphan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roongprai Seangsawang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Supanuch Patipipittana
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Watsamon Jantarabenjakul
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Thanyawee Puthanakit
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Wariya Chintanapakdee
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Sira Sriswasdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yothin Rakvongthai
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization was led by SS and YR. Data curation were performed by RS, SP, WJ, TP, and WC. Formal analysis and investigation were carried out by SN, SK, SS, and YR, who also developed the methodology for this research. The first draft of the manuscript was written by SN and SK. SS and YR supervised the manuscript writing and provided guidance throughout the study. All the authors read and approved the final manuscript.

Corresponding authors

Correspondence to Sira Sriswasdi or Yothin Rakvongthai.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interest to disclose.

Ethics approval

This study involving retrospective patient data was reviewed and approved by the Ethics Committee of the Faculty of Medicine, Chulalongkorn University (IRB no. 505/64). All methods were performed in accordance with relevant guidelines and regulations. Written informed consent was waived because this was a retrospective study of preexisting data which were de-identified.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 24 KB)

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Netprasert, Sa., Khongwirotphan, S., Seangsawang, R. et al. Predicting oxygen needs in COVID-19 patients using chest radiography multi-region radiomics. Radiol Phys Technol (2024). https://doi.org/10.1007/s12194-024-00803-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12194-024-00803-z

Keywords

Search

Navigation